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52 Commits

Author SHA1 Message Date
teknium1 b127946bc2 docs(skills): explain X credential requirements in xitter skill
Clarify why the official X flow needs five credentials and call out the setup/cost friction explicitly.
2026-03-14 03:54:59 -07:00
teknium1 4e8f681500 feat(skills): salvage xitter skill from PR #1065
Adapt the X/Twitter skill onto current main without vendoring an external CLI.
Use upstream x-cli installation instructions, add a social-media category,
and align credential/setup guidance with Hermes conventions.
2026-03-14 03:30:26 -07:00
teknium1 163fa4a9d1 refactor(cli): implement approval locking mechanism to serialize concurrent requests
- Introduced _approval_lock to ensure that approval prompts are handled sequentially, preventing state clobbering from parallel delegation subtasks.
- Updated approval_callback and HermesCLI methods to utilize the lock for managing approval state and deadlines.
- Added tests for the config bridging logic to ensure correct environment variable mapping from config.yaml.
2026-03-13 23:59:18 -07:00
Teknium a20d373945 fix: worktree-aware minisweagent path discovery + clean up requirements check (#1248)
Salvage of PR #1246 by ChatGPT (teknium1 session), resolved against
current main which already includes #1239.

Changes:
- Add minisweagent_path.py: worktree-aware helper that finds
  mini-swe-agent/src from either the current checkout or the main
  checkout behind a git worktree
- Use the helper in tools/terminal_tool.py and mini_swe_runner.py
  instead of naive path-relative lookup that fails in worktrees
- Clean up check_terminal_requirements():
  - local: return True (no minisweagent dep, per #1239)
  - singularity/ssh: remove unnecessary minisweagent imports
  - docker/modal: use importlib.util.find_spec with clear error
- Add regression tests for worktree path discovery and tool resolution
2026-03-13 23:39:51 -07:00
Teknium 21422dba44 Merge pull request #1239 from NousResearch/hermes/hermes-07d947aa
fix: stop local terminal warning without minisweagent
2026-03-13 22:14:44 -07:00
teknium1 329f83ff2d fix: stop local terminal warning without minisweagent 2026-03-13 22:00:36 -07:00
Teknium af8791a49d test: fix stale CI assumptions in parser and quick-command coverage (#1236)
- update managed-server compatibility tests to match the current
  ServerManager.tool_parser wiring used by hermes_base_env
- make quick-command CLI assertions accept Rich Text objects, which is how
  ANSI-safe output is rendered now
- set HERMES_HOME explicitly in the Discord auto-thread config bridge test
  so it loads the intended temporary config file

Validated with the targeted test set and the full pytest suite.
2026-03-13 21:56:12 -07:00
Teknium 7c3cb9bb31 Merge pull request #1227 from NousResearch/hermes/hermes-07d947aa
fix: surface gpt-5.4 in codex setup
2026-03-13 21:55:51 -07:00
Teknium a154a13811 Merge pull request #1237 from NousResearch/hermes/hermes-58b0a1f1
fix(cli): make /new, /reset, and /clear start real fresh sessions
2026-03-13 21:55:07 -07:00
teknium1 253d54a9e1 fix(cli): make /new, /reset, and /clear start real fresh sessions
Create a new session DB row when starting fresh from the CLI, reset the
agent DB flush cursor and todo state, and update session timing/session ID
bookkeeping so follow-up logging stays correct.

Also update slash-command descriptions and add regression tests for /new,
/reset, and /clear.

Supersedes PR #899.
Closes #641.
2026-03-13 21:53:54 -07:00
Teknium 22990ed378 Merge pull request #1233 from NousResearch/hermes/hermes-7c22e5c1
fix: respect HERMES_HOME in remaining hardcoded paths
2026-03-13 21:37:02 -07:00
teknium1 206e56cc5e fix: finish HERMES_HOME path cleanup
- route CLI interrupt debug logging through HERMES_HOME
- update the remaining channel_directory test to patch HERMES_HOME
  instead of Path.home()
2026-03-13 21:35:07 -07:00
Teknium 984f00e0b0 docs: expand Docusaurus coverage across CLI, tools, skills, and skins (#1232)
- add code-derived reference pages for slash commands, tools, toolsets,
  bundled skills, and official optional skills
- document the skin system and link visual theming separately from
  conversational personality
- refresh quickstart, configuration, environment variable, and messaging
  docs to match current provider, gateway, and browser behavior
- fix stale command, session, and Home Assistant configuration guidance
2026-03-13 21:34:41 -07:00
teknium1 607689095e fix: add codex forward-compat model listing 2026-03-13 21:34:01 -07:00
0xIbra 437ec17125 fix(cli): respect HERMES_HOME in all remaining hardcoded ~/.hermes paths
Several files resolved paths via Path.home() / ".hermes" or
os.path.expanduser("~/.hermes/..."), bypassing the HERMES_HOME
environment variable. This broke isolation when running multiple
Hermes instances with distinct HERMES_HOME directories.

Replace all hardcoded paths with calls to get_hermes_home() from
hermes_cli.config, consistent with the rest of the codebase.

Files fixed:
- tools/process_registry.py (processes.json)
- gateway/pairing.py (pairing/)
- gateway/sticker_cache.py (sticker_cache.json)
- gateway/channel_directory.py (channel_directory.json, sessions.json)
- gateway/config.py (gateway.json, config.yaml, sessions_dir)
- gateway/mirror.py (sessions/)
- gateway/hooks.py (hooks/)
- gateway/platforms/base.py (image_cache/, audio_cache/, document_cache/)
- gateway/platforms/whatsapp.py (whatsapp/session)
- gateway/delivery.py (cron/output)
- agent/auxiliary_client.py (auth.json)
- agent/prompt_builder.py (SOUL.md)
- cli.py (config.yaml, images/, pastes/, history)
- run_agent.py (logs/)
- tools/environments/base.py (sandboxes/)
- tools/environments/modal.py (modal_snapshots.json)
- tools/environments/singularity.py (singularity_snapshots.json)
- tools/tts_tool.py (audio_cache)
- hermes_cli/status.py (cron/jobs.json, sessions.json)
- hermes_cli/gateway.py (logs/, whatsapp session)
- hermes_cli/main.py (whatsapp/session)

Tests updated to use HERMES_HOME env var instead of patching Path.home().

Closes #892

(cherry picked from commit 78ac1bba43)
2026-03-13 21:32:53 -07:00
Teknium 2bf6b7ad1a feat(skills): add Linear project management skill (#1230)
Comprehensive Linear GraphQL API skill with API key auth (no OAuth
needed). Includes all common queries (issues, projects, teams, search,
filters) and mutations (create, update, assign, comment, status changes).

Addresses user pain point: Linear MCP server OAuth flow is unreliable
in headless agent sessions. This skill uses personal API keys which
work reliably without browser-based auth flows.

Requires: LINEAR_API_KEY env var (personal API key from Linear settings)
2026-03-13 21:20:32 -07:00
teknium1 899cb52e7a refactor: drop codex oauth model warning 2026-03-13 21:18:29 -07:00
teknium1 529729831c fix: explain codex oauth gpt-5.4 limits 2026-03-13 21:12:55 -07:00
Teknium 938e887b4c fix: keep honcho recall out of cached system prefix (#1201)
Attach later-turn Honcho recall to the current-turn user message at API
call time instead of appending it to the system prompt. This preserves the
stable system-prefix cache while keeping Honcho continuity context
available for the turn.

Also adds regression coverage for the injection helper and for continuing
sessions so Honcho recall stays out of the system prompt.
2026-03-13 21:07:00 -07:00
teknium1 57e98fe6c9 fix: surface gpt-5.4 in codex setup 2026-03-13 21:06:06 -07:00
Teknium 07d70a0345 test: cover empty cached Anthropic tool-call turns (#1222)
Add an integration-style regression test that runs prompt caching output
through the Anthropic adapter for an assistant tool-call turn with empty
content. This locks in the empty-text-block hotfix merged in PR #1216.
2026-03-13 20:44:25 -07:00
Teknium cf78349911 Merge pull request #1216 from brandtcormorant/main
fix(cache_control) treat empty text like None to avoid anthropic api …
2026-03-13 20:31:25 -07:00
brandtcormorant 76efb0153a fix(cache_control) treat empty text like None to avoid anthropic api cache_control error 2026-03-13 18:08:46 -07:00
SHL0MS 6733a9a538 Update README 2026-03-13 19:31:29 -04:00
SHL0MS 58475261c4 Merge pull request #1213 from SHL0MS/ascii-video/design-patterns
ascii-video skill upgrades
2026-03-13 19:27:15 -04:00
SHL0MS cda5910ab0 update ascii-video skill: design patterns, local time, examples
- New references/design-patterns.md: layer hierarchy (bg/content/accent),
  directional parameter arcs, scene concepts and visual metaphors,
  counter-rotating systems, wave collision, progressive fragmentation,
  entropy/consumption, staggered crescendo buildup, scene ordering
- New references/examples.md: copy-paste-ready scenes at every complexity
- Update scenes.md: local time convention (t=0 at scene start)
- Update SKILL.md: add design-patterns.md to reference table
- Add README.md to hermes-agent copy
- Sync all reference docs with canonical source (SHL0MS/ascii-video)
2026-03-13 19:13:12 -04:00
Teknium bfb82b5cee fix: preserve Anthropic cache markers through adapter (#1205)
Keep assistant cache-control blocks intact when converting OpenAI-format
messages to Anthropic format, and propagate tool-message cache markers onto
generated tool_result blocks.

Adds regression tests covering assistant and tool cache marker preservation
through convert_messages_to_anthropic().
2026-03-13 13:27:03 -07:00
Teknium c8bfb1db8f fix(gateway): add platform-specific notes to session context prompt (#1184)
Tell the agent what it CANNOT do on Slack and Discord — no searching
channel history, no pinning messages, no managing channels/roles.
Prevents the agent from hallucinating capabilities it doesn't have
and promising actions it can't deliver.

Addresses user feedback: agent says 'I'll search your Slack history'
then goes silent because no Slack-specific tools exist.
2026-03-13 12:34:11 -07:00
Austin Pickett ebd4f2c6a8 fix: redesign landing page with Nous blue palette and cleaner layout (#974)
* fix: redesign landing page with Nous blue palette and cleaner layout

* fix: add features link

* fix: misc refactors, easings

* fix: animations, easings

* fix: mobile
2026-03-13 12:03:38 -07:00
Teknium b74facd119 fix: handle YAML null values in session reset policy + configurable API timeout (#1194)
* fix: Home Assistant event filtering now closed by default

Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.

* docs: update Home Assistant integration documentation

- homeassistant.md: Fix event filtering docs to reflect closed-by-default
  behavior. Add watch_all option. Replace Python dict config example with
  YAML. Fix defaults table (was incorrectly showing 'all'). Add required
  configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
  diagram, platform toolsets table, and Next Steps links.

* fix(terminal): strip provider env vars from background and PTY subprocesses

Extends the env var blocklist from #1157 to also cover the two remaining
leaky paths in process_registry.py:

- spawn_local() PTY path (line 156)
- spawn_local() background Popen path (line 197)

Both were still using raw os.environ, leaking provider vars to background
processes and interactive PTY sessions. Now uses the same dynamic
_HERMES_PROVIDER_ENV_BLOCKLIST from local.py.

Explicit env_vars passed to spawn_local() still override the blocklist,
matching the existing behavior for callers that intentionally need these.

Gap identified by PR #1004 (@PeterFile).

* feat(delegate): add observability metadata to subagent results

Enrich delegate_task results with metadata from the child AIAgent:

- model: which model the child used
- exit_reason: completed | interrupted | max_iterations
- tokens.input / tokens.output: token counts
- tool_trace: per-tool-call trace with byte sizes and ok/error status

Tool trace uses tool_call_id matching to correctly pair parallel tool
calls with their results, with a fallback for messages without IDs.

Cherry-picked from PR #872 by @omerkaz, with fixes:
- Fixed parallel tool call trace pairing (was always updating last entry)
- Removed redundant 'iterations' field (identical to existing 'api_calls')
- Added test for parallel tool call trace correctness

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>

* feat(stt): add free local whisper transcription via faster-whisper

Replace OpenAI-only STT with a dual-provider system mirroring the TTS
architecture (Edge TTS free / ElevenLabs paid):

  STT: faster-whisper local (free, default) / OpenAI Whisper API (paid)

Changes:
- tools/transcription_tools.py: Full rewrite with provider dispatch,
  config loading, local faster-whisper backend, and OpenAI API backend.
  Auto-downloads model (~150MB for 'base') on first voice message.
  Singleton model instance reused across calls.
- pyproject.toml: Add faster-whisper>=1.0.0 as core dependency
- hermes_cli/config.py: Expand stt config to match TTS pattern with
  provider selection and per-provider model settings
- agent/context_compressor.py: Fix .strip() crash when LLM returns
  non-string content (dict from llama.cpp, None). Fixes #1100 partially.
- tests/: 23 new tests for STT providers + 2 for compressor fix
- docs/: Updated Voice & TTS page with STT provider table, model sizes,
  config examples, and fallback behavior

Fallback behavior:
- Local not installed → OpenAI API (if key set)
- OpenAI key not set → local whisper (if installed)
- Neither → graceful error message to user

Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>

* fix: handle YAML null values in session reset policy + configurable API timeout

Two fixes from PR #888 by @Jah-yee:

1. SessionResetPolicy.from_dict() — data.get('at_hour', 4) returns None
   when the YAML key exists with a null value. Now explicitly checks for
   None and falls back to defaults. Zero remains a valid value.

2. API timeout — hardcoded 900s is now configurable via HERMES_API_TIMEOUT
   env var. Useful for slow local models (llama.cpp) that need longer.

Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>

---------

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>
Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>
2026-03-13 11:16:42 -07:00
Teknium 07927f6bf2 feat(stt): add free local whisper transcription via faster-whisper (#1185)
* fix: Home Assistant event filtering now closed by default

Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.

* docs: update Home Assistant integration documentation

- homeassistant.md: Fix event filtering docs to reflect closed-by-default
  behavior. Add watch_all option. Replace Python dict config example with
  YAML. Fix defaults table (was incorrectly showing 'all'). Add required
  configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
  diagram, platform toolsets table, and Next Steps links.

* fix(terminal): strip provider env vars from background and PTY subprocesses

Extends the env var blocklist from #1157 to also cover the two remaining
leaky paths in process_registry.py:

- spawn_local() PTY path (line 156)
- spawn_local() background Popen path (line 197)

Both were still using raw os.environ, leaking provider vars to background
processes and interactive PTY sessions. Now uses the same dynamic
_HERMES_PROVIDER_ENV_BLOCKLIST from local.py.

Explicit env_vars passed to spawn_local() still override the blocklist,
matching the existing behavior for callers that intentionally need these.

Gap identified by PR #1004 (@PeterFile).

* feat(delegate): add observability metadata to subagent results

Enrich delegate_task results with metadata from the child AIAgent:

- model: which model the child used
- exit_reason: completed | interrupted | max_iterations
- tokens.input / tokens.output: token counts
- tool_trace: per-tool-call trace with byte sizes and ok/error status

Tool trace uses tool_call_id matching to correctly pair parallel tool
calls with their results, with a fallback for messages without IDs.

Cherry-picked from PR #872 by @omerkaz, with fixes:
- Fixed parallel tool call trace pairing (was always updating last entry)
- Removed redundant 'iterations' field (identical to existing 'api_calls')
- Added test for parallel tool call trace correctness

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>

* feat(stt): add free local whisper transcription via faster-whisper

Replace OpenAI-only STT with a dual-provider system mirroring the TTS
architecture (Edge TTS free / ElevenLabs paid):

  STT: faster-whisper local (free, default) / OpenAI Whisper API (paid)

Changes:
- tools/transcription_tools.py: Full rewrite with provider dispatch,
  config loading, local faster-whisper backend, and OpenAI API backend.
  Auto-downloads model (~150MB for 'base') on first voice message.
  Singleton model instance reused across calls.
- pyproject.toml: Add faster-whisper>=1.0.0 as core dependency
- hermes_cli/config.py: Expand stt config to match TTS pattern with
  provider selection and per-provider model settings
- agent/context_compressor.py: Fix .strip() crash when LLM returns
  non-string content (dict from llama.cpp, None). Fixes #1100 partially.
- tests/: 23 new tests for STT providers + 2 for compressor fix
- docs/: Updated Voice & TTS page with STT provider table, model sizes,
  config examples, and fallback behavior

Fallback behavior:
- Local not installed → OpenAI API (if key set)
- OpenAI key not set → local whisper (if installed)
- Neither → graceful error message to user

Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>

---------

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>
Co-authored-by: Jah-yee <Jah-yee@users.noreply.github.com>
2026-03-13 11:11:05 -07:00
Teknium 11b577671b fix: auxiliary client uses main model for custom/local endpoints instead of gpt-4o-mini (#1189)
* fix: prevent model/provider mismatch when switching providers during active gateway

When _update_config_for_provider() writes the new provider and base_url
to config.yaml, the gateway (which re-reads config per-message) can pick
up the change before model selection completes. This causes the old model
name (e.g. 'anthropic/claude-opus-4.6') to be sent to the new provider's
API (e.g. MiniMax), which fails.

Changes:
- _update_config_for_provider() now accepts an optional default_model
  parameter. When provided and the current model.default is empty or
  uses OpenRouter format (contains '/'), it sets a safe default model
  for the new provider.
- All setup.py callers for direct-API providers (zai, kimi, minimax,
  minimax-cn, anthropic) now pass a provider-appropriate default model.
- _setup_provider_model_selection() now validates the 'Keep current'
  choice: if the current model uses OpenRouter format and wouldn't work
  with the new provider, it warns and switches to the provider's first
  default model instead of silently keeping the incompatible name.

Reported by a user on Home Assistant whose gateway started sending
'anthropic/claude-opus-4.6' to MiniMax's API after running hermes setup.

* fix: auxiliary client uses main model for custom/local endpoints instead of gpt-4o-mini

When a user runs a local server (e.g. Qwen3.5-9B via OPENAI_BASE_URL),
the auxiliary client (context compression, vision, session search) would
send requests for 'gpt-4o-mini' or 'google/gemini-3-flash-preview' to
the local server, which only serves one model — causing 404 errors
mid-task.

Changes:
- _try_custom_endpoint() now reads the user's configured main model via
  _read_main_model() (checks OPENAI_MODEL → HERMES_MODEL → LLM_MODEL →
  config.yaml model.default) instead of hardcoding 'gpt-4o-mini'.
- resolve_provider_client() auto mode now detects when an OpenRouter-
  formatted model override (containing '/') would be sent to a non-
  OpenRouter provider (like a local server) and drops it in favor of
  the provider's default model.
- Test isolation fixes: properly clear env vars in 'nothing available'
  tests to prevent host environment leakage.
2026-03-13 10:02:16 -07:00
teknium1 153ccbfd61 fix: strip user: prefix from Discord allowed user IDs in onboarding
Users sometimes paste Discord IDs with prefixes like 'user:123456',
'<@123456>', or '<@!123456>' from Discord's UI or third-party tools.
This caused auth failures since the allowlist contained 'user:123' but
the actual user_id from messages was just '123'.

Fixes:
- Added _clean_discord_id() helper in discord.py to strip common prefixes
- Applied sanitization at runtime when parsing DISCORD_ALLOWED_USERS env var
- Applied sanitization in hermes setup and hermes gateway setup input flows
- Handles user:, <@>, and <@!> prefix formats
2026-03-13 09:35:46 -07:00
Teknium e8c9bcea2b fix: prevent model/provider mismatch when switching providers during active gateway (#1183)
When _update_config_for_provider() writes the new provider and base_url
to config.yaml, the gateway (which re-reads config per-message) can pick
up the change before model selection completes. This causes the old model
name (e.g. 'anthropic/claude-opus-4.6') to be sent to the new provider's
API (e.g. MiniMax), which fails.

Changes:
- _update_config_for_provider() now accepts an optional default_model
  parameter. When provided and the current model.default is empty or
  uses OpenRouter format (contains '/'), it sets a safe default model
  for the new provider.
- All setup.py callers for direct-API providers (zai, kimi, minimax,
  minimax-cn, anthropic) now pass a provider-appropriate default model.
- _setup_provider_model_selection() now validates the 'Keep current'
  choice: if the current model uses OpenRouter format and wouldn't work
  with the new provider, it warns and switches to the provider's first
  default model instead of silently keeping the incompatible name.

Reported by a user on Home Assistant whose gateway started sending
'anthropic/claude-opus-4.6' to MiniMax's API after running hermes setup.
2026-03-13 09:03:48 -07:00
Teknium 7aea893b5a Merge pull request #1181 from NousResearch/hermes/hermes-294208e8
fix(skills): use generic example in 1password op run snippet
2026-03-13 08:56:16 -07:00
teknium1 938edc6466 fix(skills): use generic example in 1password op run snippet
Replace OPENAI_API_KEY with DB_PASSWORD to avoid implying the
skill is OpenAI-related.
2026-03-13 08:56:06 -07:00
Teknium b8b45bfb77 feat(discord): add /thread command, auto_thread config, and media metadata fix (#1178)
- Add /thread slash command that creates a Discord thread and starts a
  new Hermes session in it. The starter message (if provided) becomes
  the first user input in the new session.
- Add discord.auto_thread config option (DISCORD_AUTO_THREAD env var):
  when enabled, every message in a text channel automatically creates
  a thread, allowing parallel isolated sessions.
- Fix Discord media method signatures to accept metadata kwarg
  (send_voice, send_image_file, send_image) — prevents TypeError
  when the base adapter passes platform metadata.
- Fix test mock isolation: add app_commands and ForumChannel to
  discord mocks so tests pass in full-suite runs.

Based on PRs #866 and #1109 by insecurejezza, modified per review:
removed /channel command (unsafe), added auto_thread feature,
made /thread dispatch new sessions.

Co-authored-by: insecurejezza <insecurejezza@users.noreply.github.com>
2026-03-13 08:52:54 -07:00
Teknium d425901bae fix: report cronjob tool as available in hermes doctor
Set HERMES_INTERACTIVE=1 via setdefault in run_doctor() so CLI-gated
tool checks (like cronjob) see the same context as the interactive CLI.

Cherry-picked from PR #895 by @stablegenius49.

Fixes #878

Co-authored-by: stablegenius49 <stablegenius49@users.noreply.github.com>
2026-03-13 08:51:45 -07:00
Teknium bcefc2a475 fix(skills): improve 1password skill — env var prompting, auth docs, broken examples
fix(skills): improve 1password skill — env var prompting, auth docs, broken examples
2026-03-13 08:47:08 -07:00
teknium1 9667c71df8 fix(skills): improve 1password skill — env var prompting, auth docs, broken examples
Follow-up to PR #883 (arceus77-7):

- Add setup.collect_secrets for OP_SERVICE_ACCOUNT_TOKEN so the skill
  prompts users to configure their token on first load
- Fix broken code examples: garbled op run export line, truncated
  secret reference in cli-examples.md
- Add Authentication Methods section documenting all 3 auth flows
  (service account, desktop app, connect server) with service account
  recommended for Hermes
- Clarify tmux pattern is only needed for desktop app flow, not
  service account token flow
- Credit original author (arceus77-7) in frontmatter
- Add DESCRIPTION.md for security/ category

Co-authored-by: arceus77-7 <arceus77-7@users.noreply.github.com>
2026-03-13 08:46:49 -07:00
Teknium 808d81f921 Merge PR #883: feat(skills): add official optional 1password skill
feat(skills): add official optional 1password skill
2026-03-13 08:45:04 -07:00
Teknium 9f676d1394 feat(skills): add bundled opencode autonomous-agent skill
Cherry-picked from PR #880 by @arceus77-7, rebased onto current main with corrections.

Adds opencode skill under skills/autonomous-ai-agents/ with:
- One-shot opencode run workflow
- Interactive/background TUI session workflow
- PR review workflow (including opencode pr command)
- Parallel work patterns
- TUI keybindings reference
- Session/cost management
- Smoke verification

Tested with OpenCode v1.2.25. Fixed /exit bug (not a valid command),
added missing flags (--file, --thinking, --variant), expanded docs.

Co-authored-by: arceus77-7 <261276524+arceus77-7@users.noreply.github.com>
2026-03-13 08:39:21 -07:00
Teknium 02a819b16e feat(delegate): add observability metadata to subagent results (#1175)
* fix: Home Assistant event filtering now closed by default

Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.

* docs: update Home Assistant integration documentation

- homeassistant.md: Fix event filtering docs to reflect closed-by-default
  behavior. Add watch_all option. Replace Python dict config example with
  YAML. Fix defaults table (was incorrectly showing 'all'). Add required
  configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
  diagram, platform toolsets table, and Next Steps links.

* fix(terminal): strip provider env vars from background and PTY subprocesses

Extends the env var blocklist from #1157 to also cover the two remaining
leaky paths in process_registry.py:

- spawn_local() PTY path (line 156)
- spawn_local() background Popen path (line 197)

Both were still using raw os.environ, leaking provider vars to background
processes and interactive PTY sessions. Now uses the same dynamic
_HERMES_PROVIDER_ENV_BLOCKLIST from local.py.

Explicit env_vars passed to spawn_local() still override the blocklist,
matching the existing behavior for callers that intentionally need these.

Gap identified by PR #1004 (@PeterFile).

* feat(delegate): add observability metadata to subagent results

Enrich delegate_task results with metadata from the child AIAgent:

- model: which model the child used
- exit_reason: completed | interrupted | max_iterations
- tokens.input / tokens.output: token counts
- tool_trace: per-tool-call trace with byte sizes and ok/error status

Tool trace uses tool_call_id matching to correctly pair parallel tool
calls with their results, with a fallback for messages without IDs.

Cherry-picked from PR #872 by @omerkaz, with fixes:
- Fixed parallel tool call trace pairing (was always updating last entry)
- Removed redundant 'iterations' field (identical to existing 'api_calls')
- Added test for parallel tool call trace correctness

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>

---------

Co-authored-by: omerkaz <omerkaz@users.noreply.github.com>
2026-03-13 08:07:12 -07:00
Teknium 4644f71faf Merge pull request #1173 from NousResearch/hermes/hermes-4cde5efa
fix(cron): use atomic write in save_job_output to prevent data loss on crash
2026-03-13 08:05:52 -07:00
alireza78a 9a7ed81b4b fix(cron): use atomic write in save_job_output to prevent data loss on crash
save_job_output() used bare open('w') which truncates the output file
immediately. A crash or OOM kill between truncation and the completed
write would silently wipe the job output.

Write now goes to a temp file first, then os.replace() swaps it
atomically — matching the existing save_jobs() pattern in the same file.
Preserves _secure_file() permissions and uses safe cleanup on error.

Cherry-picked from PR #874 by alireza78a, rebased onto current main
with conflict resolution and fixes:
- Kept _secure_dir/_secure_file security calls from PR #757
- Used except BaseException (not bare except) to match save_jobs pattern
- Wrapped os.unlink in try/except OSError to avoid masking errors

Co-authored-by: alireza78a <alireza78a@users.noreply.github.com>
2026-03-13 08:04:36 -07:00
Teknium 646b4ec533 fix(terminal): strip provider env vars from background and PTY subprocesses (#1172)
* fix: Home Assistant event filtering now closed by default

Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.

* docs: update Home Assistant integration documentation

- homeassistant.md: Fix event filtering docs to reflect closed-by-default
  behavior. Add watch_all option. Replace Python dict config example with
  YAML. Fix defaults table (was incorrectly showing 'all'). Add required
  configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
  diagram, platform toolsets table, and Next Steps links.

* fix(terminal): strip provider env vars from background and PTY subprocesses

Extends the env var blocklist from #1157 to also cover the two remaining
leaky paths in process_registry.py:

- spawn_local() PTY path (line 156)
- spawn_local() background Popen path (line 197)

Both were still using raw os.environ, leaking provider vars to background
processes and interactive PTY sessions. Now uses the same dynamic
_HERMES_PROVIDER_ENV_BLOCKLIST from local.py.

Explicit env_vars passed to spawn_local() still override the blocklist,
matching the existing behavior for callers that intentionally need these.

Gap identified by PR #1004 (@PeterFile).
2026-03-13 07:54:46 -07:00
Muhammet Eren Karakuş c92507e53d fix(terminal): strip Hermes provider env vars from subprocess environment (#1157)
Terminal subprocesses inherit OPENAI_BASE_URL and other provider env
vars loaded from ~/.hermes/.env, silently misrouting external CLIs
like codex.  Build a blocklist dynamically from the provider registry
so new providers are automatically covered.  Callers that truly need
a blocked var can opt in via the _HERMES_FORCE_ prefix.

Closes #1002

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-13 07:52:03 -07:00
Teknium 4b53ecb1c7 docs: update Home Assistant integration documentation (#1170)
* fix: Home Assistant event filtering now closed by default

Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.

* docs: update Home Assistant integration documentation

- homeassistant.md: Fix event filtering docs to reflect closed-by-default
  behavior. Add watch_all option. Replace Python dict config example with
  YAML. Fix defaults table (was incorrectly showing 'all'). Add required
  configuration warning admonition.
- environment-variables.md: Add HASS_TOKEN and HASS_URL to Messaging section.
- messaging/index.md: Add Home Assistant to description, architecture
  diagram, platform toolsets table, and Next Steps links.
2026-03-13 07:45:06 -07:00
Teknium 61531396a0 fix: Home Assistant event filtering now closed by default (#1169)
Previously, when no watch_domains or watch_entities were configured,
ALL state_changed events passed through to the agent, causing users
to be flooded with notifications for every HA entity change.

Now events are dropped by default unless the user explicitly configures:
- watch_domains: list of domains to monitor (e.g. climate, light)
- watch_entities: list of specific entity IDs to monitor
- watch_all: true (new option — opt-in to receive all events)

A warning is logged at connect time if no filters are configured,
guiding users to set up their HA platform config.

All 49 gateway HA tests + 52 HA tool tests pass.
2026-03-13 07:40:38 -07:00
Teknium 6235fdde75 fix: raise session hygiene threshold from 50% to 85%
Session hygiene was firing at the same threshold (50%) as the agent's
own context compressor, causing premature compression on every turn
in long gateway sessions (especially Telegram).

Hygiene is a safety net for pathologically large sessions that would
cause API failures — it should NOT be doing normal compression work.
The agent's own compressor handles that during its tool loop with
accurate real token counts from the API.

Changes:
- Default hygiene threshold: 0.50 → 0.85 (fires only when truly large)
- Hygiene threshold is now independent of compression.threshold config
  (that setting controls the agent's compressor, not the pre-agent safety net)
- Removed env var override for hygiene threshold (CONTEXT_COMPRESSION_THRESHOLD
  still controls the agent's own compressor)
2026-03-13 04:17:45 -07:00
Teknium 8f8dd83443 fix: sync session_id after mid-run context compression
Critical bug: when the agent's context compressor fires during a tool
loop (_compress_context), it creates a new session_id and writes the
compressed messages there. But the gateway's session_entry still pointed
to the old session_id. On the next message, load_transcript() loaded
the stale pre-compression transcript, causing:

- Context bloat returning every turn
- Repeated compression cycles
- Loss of carefully compressed context

Fix: after run_conversation() returns, check if the agent's session_id
changed (compression split) and sync it back to the session store entry.
Also pass the effective session_id in the result dict so _handle_message
writes transcript entries to the correct session.

This affects ALL gateway adapters, not just webhook.
2026-03-13 04:14:35 -07:00
arceus77-7 d41a214c1a feat(skills): add official optional 1password skill 2026-03-10 20:45:29 -04:00
119 changed files with 10145 additions and 2016 deletions
+10 -2
View File
@@ -404,8 +404,14 @@ def convert_messages_to_anthropic(
if role == "assistant":
blocks = []
if content:
text = content if isinstance(content, str) else json.dumps(content)
blocks.append({"type": "text", "text": text})
if isinstance(content, list):
for part in content:
if isinstance(part, dict):
blocks.append(dict(part))
elif part is not None:
blocks.append({"type": "text", "text": str(part)})
else:
blocks.append({"type": "text", "text": str(content)})
for tc in m.get("tool_calls", []):
fn = tc.get("function", {})
args = fn.get("arguments", "{}")
@@ -436,6 +442,8 @@ def convert_messages_to_anthropic(
"tool_use_id": _sanitize_tool_id(m.get("tool_call_id", "")),
"content": result_content,
}
if isinstance(m.get("cache_control"), dict):
tool_result["cache_control"] = dict(m["cache_control"])
# Merge consecutive tool results into one user message
if (
result
+37 -2
View File
@@ -41,6 +41,7 @@ from typing import Any, Dict, List, Optional, Tuple
from openai import OpenAI
from hermes_cli.config import get_hermes_home
from hermes_constants import OPENROUTER_BASE_URL
logger = logging.getLogger(__name__)
@@ -73,7 +74,7 @@ auxiliary_is_nous: bool = False
_OPENROUTER_MODEL = "google/gemini-3-flash-preview"
_NOUS_MODEL = "gemini-3-flash"
_NOUS_DEFAULT_BASE_URL = "https://inference-api.nousresearch.com/v1"
_AUTH_JSON_PATH = Path.home() / ".hermes" / "auth.json"
_AUTH_JSON_PATH = get_hermes_home() / "auth.json"
# Codex fallback: uses the Responses API (the only endpoint the Codex
# OAuth token can access) with a fast model for auxiliary tasks.
@@ -439,12 +440,37 @@ def _try_nous() -> Tuple[Optional[OpenAI], Optional[str]]:
)
def _read_main_model() -> str:
"""Read the user's configured main model from config/env.
Falls back through HERMES_MODEL → LLM_MODEL → config.yaml model.default
so the auxiliary client can use the same model as the main agent when no
dedicated auxiliary model is available.
"""
from_env = os.getenv("OPENAI_MODEL") or os.getenv("HERMES_MODEL") or os.getenv("LLM_MODEL")
if from_env:
return from_env.strip()
try:
from hermes_cli.config import load_config
cfg = load_config()
model_cfg = cfg.get("model", {})
if isinstance(model_cfg, str) and model_cfg.strip():
return model_cfg.strip()
if isinstance(model_cfg, dict):
default = model_cfg.get("default", "")
if isinstance(default, str) and default.strip():
return default.strip()
except Exception:
pass
return ""
def _try_custom_endpoint() -> Tuple[Optional[OpenAI], Optional[str]]:
custom_base = os.getenv("OPENAI_BASE_URL")
custom_key = os.getenv("OPENAI_API_KEY")
if not custom_base or not custom_key:
return None, None
model = os.getenv("OPENAI_MODEL") or "gpt-4o-mini"
model = _read_main_model() or "gpt-4o-mini"
logger.debug("Auxiliary client: custom endpoint (%s)", model)
return OpenAI(api_key=custom_key, base_url=custom_base), model
@@ -575,6 +601,15 @@ def resolve_provider_client(
client, resolved = _resolve_auto()
if client is None:
return None, None
# When auto-detection lands on a non-OpenRouter provider (e.g. a
# local server), an OpenRouter-formatted model override like
# "google/gemini-3-flash-preview" won't work. Drop it and use
# the provider's own default model instead.
if model and "/" in model and resolved and "/" not in resolved:
logger.debug(
"Dropping OpenRouter-format model %r for non-OpenRouter "
"auxiliary provider (using %r instead)", model, resolved)
model = None
final_model = model or resolved
return (_to_async_client(client, final_model) if async_mode
else (client, final_model))
+5 -1
View File
@@ -132,7 +132,11 @@ Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
if self.summary_model:
call_kwargs["model"] = self.summary_model
response = call_llm(**call_kwargs)
summary = response.choices[0].message.content.strip()
content = response.choices[0].message.content
# Handle cases where content is not a string (e.g., dict from llama.cpp)
if not isinstance(content, str):
content = str(content) if content else ""
summary = content.strip()
if not summary.startswith("[CONTEXT SUMMARY]:"):
summary = "[CONTEXT SUMMARY]: " + summary
return summary
+1 -1
View File
@@ -420,7 +420,7 @@ def build_context_files_prompt(cwd: Optional[str] = None) -> str:
soul_path = candidate
break
if not soul_path:
global_soul = Path.home() / ".hermes" / "SOUL.md"
global_soul = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes")) / "SOUL.md"
if global_soul.exists():
soul_path = global_soul
+4 -2
View File
@@ -21,12 +21,14 @@ def _apply_cache_marker(msg: dict, cache_marker: dict) -> None:
msg["cache_control"] = cache_marker
return
if content is None:
if content is None or content == "":
msg["cache_control"] = cache_marker
return
if isinstance(content, str):
msg["content"] = [{"type": "text", "text": content, "cache_control": cache_marker}]
msg["content"] = [
{"type": "text", "text": content, "cache_control": cache_marker}
]
return
if isinstance(content, list) and content:
+103 -60
View File
@@ -96,7 +96,7 @@ def _load_prefill_messages(file_path: str) -> List[Dict[str, Any]]:
return []
path = Path(file_path).expanduser()
if not path.is_absolute():
path = Path.home() / ".hermes" / path
path = _hermes_home / path
if not path.exists():
logger.warning("Prefill messages file not found: %s", path)
return []
@@ -141,16 +141,16 @@ def load_cli_config() -> Dict[str, Any]:
Environment variables take precedence over config file values.
Returns default values if no config file exists.
"""
# Check user config first (~/.hermes/config.yaml)
user_config_path = Path.home() / '.hermes' / 'config.yaml'
# Check user config first ({HERMES_HOME}/config.yaml)
user_config_path = _hermes_home / 'config.yaml'
project_config_path = Path(__file__).parent / 'cli-config.yaml'
# Use user config if it exists, otherwise project config
if user_config_path.exists():
config_path = user_config_path
else:
config_path = project_config_path
# Default configuration
defaults = {
"model": {
@@ -1037,7 +1037,7 @@ def save_config_value(key_path: str, value: any) -> bool:
True if successful, False otherwise
"""
# Use the same precedence as load_cli_config: user config first, then project config
user_config_path = Path.home() / '.hermes' / 'config.yaml'
user_config_path = _hermes_home / 'config.yaml'
project_config_path = Path(__file__).parent / 'cli-config.yaml'
config_path = user_config_path if user_config_path.exists() else project_config_path
@@ -1259,7 +1259,7 @@ class HermesCLI:
self.session_id = f"{timestamp_str}_{short_uuid}"
# History file for persistent input recall across sessions
self._history_file = Path.home() / ".hermes_history"
self._history_file = _hermes_home / ".hermes_history"
self._last_invalidate: float = 0.0 # throttle UI repaints
self._app = None
self._secret_state = None
@@ -1778,7 +1778,7 @@ class HermesCLI:
"""
from hermes_cli.clipboard import save_clipboard_image
img_dir = Path.home() / ".hermes" / "images"
img_dir = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes")) / "images"
self._image_counter += 1
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
img_path = img_dir / f"clip_{ts}_{self._image_counter}.png"
@@ -2074,7 +2074,7 @@ class HermesCLI:
terminal_cwd = os.getenv("TERMINAL_CWD", os.getcwd())
terminal_timeout = os.getenv("TERMINAL_TIMEOUT", "60")
user_config_path = Path.home() / '.hermes' / 'config.yaml'
user_config_path = _hermes_home / 'config.yaml'
project_config_path = Path(__file__).parent / 'cli-config.yaml'
if user_config_path.exists():
config_path = user_config_path
@@ -2183,15 +2183,63 @@ class HermesCLI:
flush_tool_summary()
print()
def reset_conversation(self):
"""Reset the conversation history."""
def new_session(self, silent=False):
"""Start a fresh session with a new session ID and cleared agent state."""
if self.agent and self.conversation_history:
try:
self.agent.flush_memories(self.conversation_history)
except Exception:
pass
old_session_id = self.session_id
if self._session_db and old_session_id:
try:
self._session_db.end_session(old_session_id, "new_session")
except Exception:
pass
self.session_start = datetime.now()
timestamp_str = self.session_start.strftime("%Y%m%d_%H%M%S")
short_uuid = uuid.uuid4().hex[:6]
self.session_id = f"{timestamp_str}_{short_uuid}"
self.conversation_history = []
print("(^_^)b Conversation reset!")
self._pending_title = None
self._resumed = False
if self.agent:
self.agent.session_id = self.session_id
self.agent.session_start = self.session_start
if hasattr(self.agent, "_last_flushed_db_idx"):
self.agent._last_flushed_db_idx = 0
if hasattr(self.agent, "_todo_store"):
try:
from tools.todo_tool import TodoStore
self.agent._todo_store = TodoStore()
except Exception:
pass
if hasattr(self.agent, "_invalidate_system_prompt"):
self.agent._invalidate_system_prompt()
if self._session_db:
try:
self._session_db.create_session(
session_id=self.session_id,
source="cli",
model=self.model,
model_config={
"max_iterations": self.max_turns,
"reasoning_config": self.reasoning_config,
},
)
except Exception:
pass
if not silent:
print("(^_^)v New session started!")
def reset_conversation(self):
"""Reset the conversation by starting a new session."""
self.new_session()
def save_conversation(self):
"""Save the current conversation to a file."""
@@ -2675,12 +2723,7 @@ class HermesCLI:
elif cmd_lower == "/config":
self.show_config()
elif cmd_lower == "/clear":
# Flush memories before clearing
if self.agent and self.conversation_history:
try:
self.agent.flush_memories(self.conversation_history)
except Exception:
pass
self.new_session(silent=True)
# Clear terminal screen. Inside the TUI, Rich's console.clear()
# goes through patch_stdout's StdoutProxy which swallows the
# screen-clear escape sequences. Use prompt_toolkit's output
@@ -2692,8 +2735,6 @@ class HermesCLI:
out.flush()
else:
self.console.clear()
# Reset conversation
self.conversation_history = []
# Show fresh banner. Inside the TUI we must route Rich output
# through ChatConsole (which uses prompt_toolkit's native ANSI
# renderer) instead of self.console (which writes raw to stdout
@@ -2796,7 +2837,7 @@ class HermesCLI:
else:
_cprint(" Session database not available.")
elif cmd_lower in ("/reset", "/new"):
self.reset_conversation()
self.new_session()
elif cmd_lower.startswith("/model"):
# Use original case so model names like "Anthropic/Claude-Opus-4" are preserved
parts = cmd_original.split(maxsplit=1)
@@ -3530,48 +3571,51 @@ class HermesCLI:
Called from the agent thread. Shows a selection UI similar to clarify
with choices: once / session / always / deny.
Uses _approval_lock to serialize concurrent requests (e.g. from
parallel delegation subtasks) so each prompt gets its own turn
and the shared _approval_state / _approval_deadline aren't clobbered.
"""
import time as _time
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
with self._approval_lock:
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
self._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
self._approval_deadline = _time.monotonic() + timeout
self._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
self._approval_deadline = _time.monotonic() + timeout
self._invalidate()
self._invalidate()
# Same throttled countdown as _clarify_callback — repaint only
# every 5 s to avoid flicker in Kitty / ghostty / etc.
_last_countdown_refresh = _time.monotonic()
while True:
try:
result = response_queue.get(timeout=1)
self._approval_state = None
self._approval_deadline = 0
self._invalidate()
return result
except queue.Empty:
remaining = self._approval_deadline - _time.monotonic()
if remaining <= 0:
break
now = _time.monotonic()
if now - _last_countdown_refresh >= 5.0:
_last_countdown_refresh = now
_last_countdown_refresh = _time.monotonic()
while True:
try:
result = response_queue.get(timeout=1)
self._approval_state = None
self._approval_deadline = 0
self._invalidate()
return result
except queue.Empty:
remaining = self._approval_deadline - _time.monotonic()
if remaining <= 0:
break
now = _time.monotonic()
if now - _last_countdown_refresh >= 5.0:
_last_countdown_refresh = now
self._invalidate()
self._approval_state = None
self._approval_deadline = 0
self._invalidate()
_cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"
self._approval_state = None
self._approval_deadline = 0
self._invalidate()
_cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"
def _secret_capture_callback(self, var_name: str, prompt: str, metadata=None) -> dict:
return prompt_for_secret(self, var_name, prompt, metadata)
@@ -3684,8 +3728,7 @@ class HermesCLI:
self.agent.interrupt(interrupt_msg)
# Debug: log to file (stdout may be devnull from redirect_stdout)
try:
import pathlib as _pl
_dbg = _pl.Path.home() / ".hermes" / "interrupt_debug.log"
_dbg = _hermes_home / "interrupt_debug.log"
with open(_dbg, "a") as _f:
import time as _t
_f.write(f"{_t.strftime('%H:%M:%S')} interrupt fired: msg={str(interrupt_msg)[:60]!r}, "
@@ -3880,6 +3923,7 @@ class HermesCLI:
# Dangerous command approval state (similar mechanism to clarify)
self._approval_state = None # dict with command, description, choices, selected, response_queue
self._approval_deadline = 0
self._approval_lock = threading.Lock() # serialize concurrent approval prompts (delegation race fix)
# Slash command loading state
self._command_running = False
@@ -3993,8 +4037,7 @@ class HermesCLI:
self._interrupt_queue.put(payload)
# Debug: log to file when message enters interrupt queue
try:
import pathlib as _pl
_dbg = _pl.Path.home() / ".hermes" / "interrupt_debug.log"
_dbg = _hermes_home / "interrupt_debug.log"
with open(_dbg, "a") as _f:
import time as _t
_f.write(f"{_t.strftime('%H:%M:%S')} ENTER: queued interrupt msg={str(payload)[:60]!r}, "
@@ -4255,7 +4298,7 @@ class HermesCLI:
if line_count >= 5 and chars_added > 1 and not text.startswith('/'):
_paste_counter[0] += 1
# Save to temp file
paste_dir = Path(os.path.expanduser("~/.hermes/pastes"))
paste_dir = _hermes_home / "pastes"
paste_dir.mkdir(parents=True, exist_ok=True)
paste_file = paste_dir / f"paste_{_paste_counter[0]}_{datetime.now().strftime('%H%M%S')}.txt"
paste_file.write_text(text, encoding="utf-8")
+14 -3
View File
@@ -431,8 +431,19 @@ def save_job_output(job_id: str, output: str):
timestamp = _hermes_now().strftime("%Y-%m-%d_%H-%M-%S")
output_file = job_output_dir / f"{timestamp}.md"
with open(output_file, 'w', encoding='utf-8') as f:
f.write(output)
_secure_file(output_file)
fd, tmp_path = tempfile.mkstemp(dir=str(job_output_dir), suffix='.tmp', prefix='.output_')
try:
with os.fdopen(fd, 'w', encoding='utf-8') as f:
f.write(output)
f.flush()
os.fsync(f.fileno())
os.replace(tmp_path, output_file)
_secure_file(output_file)
except BaseException:
try:
os.unlink(tmp_path)
except OSError:
pass
raise
return output_file
+4 -2
View File
@@ -12,9 +12,11 @@ from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
DIRECTORY_PATH = Path.home() / ".hermes" / "channel_directory.json"
DIRECTORY_PATH = get_hermes_home() / "channel_directory.json"
def _session_entry_id(origin: Dict[str, Any]) -> Optional[str]:
@@ -129,7 +131,7 @@ def _build_slack(adapter) -> List[Dict[str, str]]:
def _build_from_sessions(platform_name: str) -> List[Dict[str, str]]:
"""Pull known channels/contacts from sessions.json origin data."""
sessions_path = Path.home() / ".hermes" / "sessions" / "sessions.json"
sessions_path = get_hermes_home() / "sessions" / "sessions.json"
if not sessions_path.exists():
return []
+16 -8
View File
@@ -16,6 +16,8 @@ from dataclasses import dataclass, field
from typing import Dict, List, Optional, Any
from enum import Enum
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
@@ -83,10 +85,13 @@ class SessionResetPolicy:
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "SessionResetPolicy":
# Handle both missing keys and explicit null values (YAML null → None)
at_hour = data.get("at_hour")
idle_minutes = data.get("idle_minutes")
return cls(
mode=data.get("mode", "both"),
at_hour=data.get("at_hour", 4),
idle_minutes=data.get("idle_minutes", 1440),
at_hour=at_hour if at_hour is not None else 4,
idle_minutes=idle_minutes if idle_minutes is not None else 1440,
)
@@ -148,7 +153,7 @@ class GatewayConfig:
reset_triggers: List[str] = field(default_factory=lambda: ["/new", "/reset"])
# Storage paths
sessions_dir: Path = field(default_factory=lambda: Path.home() / ".hermes" / "sessions")
sessions_dir: Path = field(default_factory=lambda: get_hermes_home() / "sessions")
# Delivery settings
always_log_local: bool = True # Always save cron outputs to local files
@@ -243,7 +248,7 @@ class GatewayConfig:
if "default_reset_policy" in data:
default_policy = SessionResetPolicy.from_dict(data["default_reset_policy"])
sessions_dir = Path.home() / ".hermes" / "sessions"
sessions_dir = get_hermes_home() / "sessions"
if "sessions_dir" in data:
sessions_dir = Path(data["sessions_dir"])
@@ -271,7 +276,8 @@ def load_gateway_config() -> GatewayConfig:
config = GatewayConfig()
# Try loading from ~/.hermes/gateway.json
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
_home = get_hermes_home()
gateway_config_path = _home / "gateway.json"
if gateway_config_path.exists():
try:
with open(gateway_config_path, "r", encoding="utf-8") as f:
@@ -279,13 +285,13 @@ def load_gateway_config() -> GatewayConfig:
config = GatewayConfig.from_dict(data)
except Exception as e:
print(f"[gateway] Warning: Failed to load {gateway_config_path}: {e}")
# Bridge session_reset from config.yaml (the user-facing config file)
# into the gateway config. config.yaml takes precedence over gateway.json
# for session reset policy since that's where hermes setup writes it.
try:
import yaml
config_yaml_path = Path.home() / ".hermes" / "config.yaml"
config_yaml_path = _home / "config.yaml"
if config_yaml_path.exists():
with open(config_yaml_path, encoding="utf-8") as f:
yaml_cfg = yaml.safe_load(f) or {}
@@ -304,6 +310,8 @@ def load_gateway_config() -> GatewayConfig:
if isinstance(frc, list):
frc = ",".join(str(v) for v in frc)
os.environ["DISCORD_FREE_RESPONSE_CHANNELS"] = str(frc)
if "auto_thread" in discord_cfg and not os.getenv("DISCORD_AUTO_THREAD"):
os.environ["DISCORD_AUTO_THREAD"] = str(discord_cfg["auto_thread"]).lower()
except Exception:
pass
@@ -476,7 +484,7 @@ def _apply_env_overrides(config: GatewayConfig) -> None:
def save_gateway_config(config: GatewayConfig) -> None:
"""Save gateway configuration to ~/.hermes/gateway.json."""
gateway_config_path = Path.home() / ".hermes" / "gateway.json"
gateway_config_path = get_hermes_home() / "gateway.json"
gateway_config_path.parent.mkdir(parents=True, exist_ok=True)
with open(gateway_config_path, "w", encoding="utf-8") as f:
+4 -2
View File
@@ -15,6 +15,8 @@ from dataclasses import dataclass
from typing import Dict, List, Optional, Any, Union
from enum import Enum
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
MAX_PLATFORM_OUTPUT = 4000
@@ -116,7 +118,7 @@ class DeliveryRouter:
"""
self.config = config
self.adapters = adapters or {}
self.output_dir = Path.home() / ".hermes" / "cron" / "output"
self.output_dir = get_hermes_home() / "cron" / "output"
def resolve_targets(
self,
@@ -256,7 +258,7 @@ class DeliveryRouter:
def _save_full_output(self, content: str, job_id: str) -> Path:
"""Save full cron output to disk and return the file path."""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
out_dir = Path.home() / ".hermes" / "cron" / "output"
out_dir = get_hermes_home() / "cron" / "output"
out_dir.mkdir(parents=True, exist_ok=True)
path = out_dir / f"{job_id}_{timestamp}.txt"
path.write_text(content)
+3 -1
View File
@@ -26,8 +26,10 @@ from typing import Any, Callable, Dict, List, Optional
import yaml
from hermes_cli.config import get_hermes_home
HOOKS_DIR = Path(os.path.expanduser("~/.hermes/hooks"))
HOOKS_DIR = get_hermes_home() / "hooks"
class HookRegistry:
+3 -1
View File
@@ -15,9 +15,11 @@ from datetime import datetime
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
_SESSIONS_DIR = Path.home() / ".hermes" / "sessions"
_SESSIONS_DIR = get_hermes_home() / "sessions"
_SESSIONS_INDEX = _SESSIONS_DIR / "sessions.json"
+3 -1
View File
@@ -25,6 +25,8 @@ import time
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
# Unambiguous alphabet -- excludes 0/O, 1/I to prevent confusion
ALPHABET = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789"
@@ -39,7 +41,7 @@ LOCKOUT_SECONDS = 3600 # Lockout duration after too many failures
MAX_PENDING_PER_PLATFORM = 3 # Max pending codes per platform
MAX_FAILED_ATTEMPTS = 5 # Failed approvals before lockout
PAIRING_DIR = Path(os.path.expanduser("~/.hermes/pairing"))
PAIRING_DIR = get_hermes_home() / "pairing"
def _secure_write(path: Path, data: str) -> None:
+5 -4
View File
@@ -25,6 +25,7 @@ sys.path.insert(0, str(_Path(__file__).resolve().parents[2]))
from gateway.config import Platform, PlatformConfig
from gateway.session import SessionSource, build_session_key
from hermes_cli.config import get_hermes_home
GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE = (
@@ -42,8 +43,8 @@ GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE = (
# (e.g. Telegram file URLs expire after ~1 hour).
# ---------------------------------------------------------------------------
# Default location: ~/.hermes/image_cache/
IMAGE_CACHE_DIR = Path(os.path.expanduser("~/.hermes/image_cache"))
# Default location: {HERMES_HOME}/image_cache/
IMAGE_CACHE_DIR = get_hermes_home() / "image_cache"
def get_image_cache_dir() -> Path:
@@ -125,7 +126,7 @@ def cleanup_image_cache(max_age_hours: int = 24) -> int:
# here so the STT tool (OpenAI Whisper) can transcribe them from local files.
# ---------------------------------------------------------------------------
AUDIO_CACHE_DIR = Path(os.path.expanduser("~/.hermes/audio_cache"))
AUDIO_CACHE_DIR = get_hermes_home() / "audio_cache"
def get_audio_cache_dir() -> Path:
@@ -184,7 +185,7 @@ async def cache_audio_from_url(url: str, ext: str = ".ogg") -> str:
# here so the agent can reference them by local file path.
# ---------------------------------------------------------------------------
DOCUMENT_CACHE_DIR = Path(os.path.expanduser("~/.hermes/document_cache"))
DOCUMENT_CACHE_DIR = get_hermes_home() / "document_cache"
SUPPORTED_DOCUMENT_TYPES = {
".pdf": "application/pdf",
+239 -3
View File
@@ -14,6 +14,8 @@ from typing import Dict, List, Optional, Any
logger = logging.getLogger(__name__)
VALID_THREAD_AUTO_ARCHIVE_MINUTES = {60, 1440, 4320, 10080}
try:
import discord
from discord import Message as DiscordMessage, Intents
@@ -41,6 +43,23 @@ from gateway.platforms.base import (
)
def _clean_discord_id(entry: str) -> str:
"""Strip common prefixes from a Discord user ID or username entry.
Users sometimes paste IDs with prefixes like ``user:123``, ``<@123>``,
or ``<@!123>`` from Discord's UI or other tools. This normalises the
entry to just the bare ID or username.
"""
entry = entry.strip()
# Strip Discord mention syntax: <@123> or <@!123>
if entry.startswith("<@") and entry.endswith(">"):
entry = entry.lstrip("<@!").rstrip(">")
# Strip "user:" prefix (seen in some Discord tools / onboarding pastes)
if entry.lower().startswith("user:"):
entry = entry[5:]
return entry.strip()
def check_discord_requirements() -> bool:
"""Check if Discord dependencies are available."""
return DISCORD_AVAILABLE
@@ -97,7 +116,8 @@ class DiscordAdapter(BasePlatformAdapter):
allowed_env = os.getenv("DISCORD_ALLOWED_USERS", "")
if allowed_env:
self._allowed_user_ids = {
uid.strip() for uid in allowed_env.split(",") if uid.strip()
_clean_discord_id(uid) for uid in allowed_env.split(",")
if uid.strip()
}
adapter_self = self # capture for closure
@@ -251,6 +271,7 @@ class DiscordAdapter(BasePlatformAdapter):
audio_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send audio as a Discord file attachment."""
if not self._client:
@@ -289,6 +310,7 @@ class DiscordAdapter(BasePlatformAdapter):
image_path: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send a local image file natively as a Discord file attachment."""
if not self._client:
@@ -326,6 +348,7 @@ class DiscordAdapter(BasePlatformAdapter):
image_url: str,
caption: Optional[str] = None,
reply_to: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None,
) -> SendResult:
"""Send an image natively as a Discord file attachment."""
if not self._client:
@@ -711,6 +734,21 @@ class DiscordAdapter(BasePlatformAdapter):
except Exception as e:
logger.debug("Discord followup failed: %s", e)
@tree.command(name="thread", description="Create a new thread and start a Hermes session in it")
@discord.app_commands.describe(
name="Thread name",
message="Optional first message to send to Hermes in the thread",
auto_archive_duration="Auto-archive in minutes (60, 1440, 4320, 10080)",
)
async def slash_thread(
interaction: discord.Interaction,
name: str,
message: str = "",
auto_archive_duration: int = 1440,
):
await interaction.response.defer(ephemeral=True)
await self._handle_thread_create_slash(interaction, name, message, auto_archive_duration)
def _build_slash_event(self, interaction: discord.Interaction, text: str) -> MessageEvent:
"""Build a MessageEvent from a Discord slash command interaction."""
is_dm = isinstance(interaction.channel, discord.DMChannel)
@@ -741,6 +779,188 @@ class DiscordAdapter(BasePlatformAdapter):
raw_message=interaction,
)
# ------------------------------------------------------------------
# Thread creation helpers
# ------------------------------------------------------------------
async def _handle_thread_create_slash(
self,
interaction: discord.Interaction,
name: str,
message: str = "",
auto_archive_duration: int = 1440,
) -> None:
"""Create a Discord thread from a slash command and start a session in it."""
result = await self._create_thread(
interaction,
name=name,
message=message,
auto_archive_duration=auto_archive_duration,
)
if not result.get("success"):
error = result.get("error", "unknown error")
await interaction.followup.send(f"Failed to create thread: {error}", ephemeral=True)
return
thread_id = result.get("thread_id")
thread_name = result.get("thread_name") or name
# Tell the user where the thread is
link = f"<#{thread_id}>" if thread_id else f"**{thread_name}**"
await interaction.followup.send(f"Created thread {link}", ephemeral=True)
# If a message was provided, kick off a new Hermes session in the thread
starter = (message or "").strip()
if starter and thread_id:
await self._dispatch_thread_session(interaction, thread_id, thread_name, starter)
async def _dispatch_thread_session(
self,
interaction: discord.Interaction,
thread_id: str,
thread_name: str,
text: str,
) -> None:
"""Build a MessageEvent pointing at a thread and send it through handle_message."""
guild_name = ""
if hasattr(interaction, "guild") and interaction.guild:
guild_name = interaction.guild.name
chat_name = f"{guild_name} / {thread_name}" if guild_name else thread_name
source = self.build_source(
chat_id=thread_id,
chat_name=chat_name,
chat_type="thread",
user_id=str(interaction.user.id),
user_name=interaction.user.display_name,
thread_id=thread_id,
)
event = MessageEvent(
text=text,
message_type=MessageType.TEXT,
source=source,
raw_message=interaction,
)
await self.handle_message(event)
def _thread_parent_channel(self, channel: Any) -> Any:
"""Return the parent text channel when invoked from a thread."""
return getattr(channel, "parent", None) or channel
async def _resolve_interaction_channel(self, interaction: discord.Interaction) -> Optional[Any]:
"""Return the interaction channel, fetching it if the payload is partial."""
channel = getattr(interaction, "channel", None)
if channel is not None:
return channel
if not self._client:
return None
channel_id = getattr(interaction, "channel_id", None)
if channel_id is None:
return None
channel = self._client.get_channel(int(channel_id))
if channel is not None:
return channel
try:
return await self._client.fetch_channel(int(channel_id))
except Exception:
return None
async def _create_thread(
self,
interaction: discord.Interaction,
*,
name: str,
message: str = "",
auto_archive_duration: int = 1440,
) -> Dict[str, Any]:
"""Create a thread in the current Discord channel.
Tries ``parent_channel.create_thread()`` first. If Discord rejects
that (e.g. permission issues), falls back to sending a seed message
and creating the thread from it.
"""
name = (name or "").strip()
if not name:
return {"error": "Thread name is required."}
if auto_archive_duration not in VALID_THREAD_AUTO_ARCHIVE_MINUTES:
allowed = ", ".join(str(v) for v in sorted(VALID_THREAD_AUTO_ARCHIVE_MINUTES))
return {"error": f"auto_archive_duration must be one of: {allowed}."}
channel = await self._resolve_interaction_channel(interaction)
if channel is None:
return {"error": "Could not resolve the current Discord channel."}
if isinstance(channel, discord.DMChannel):
return {"error": "Discord threads can only be created inside server text channels, not DMs."}
parent_channel = self._thread_parent_channel(channel)
if parent_channel is None:
return {"error": "Could not determine a parent text channel for the new thread."}
display_name = getattr(getattr(interaction, "user", None), "display_name", None) or "unknown user"
reason = f"Requested by {display_name} via /thread"
starter_message = (message or "").strip()
try:
thread = await parent_channel.create_thread(
name=name,
auto_archive_duration=auto_archive_duration,
reason=reason,
)
if starter_message:
await thread.send(starter_message)
return {
"success": True,
"thread_id": str(thread.id),
"thread_name": getattr(thread, "name", None) or name,
}
except Exception as direct_error:
try:
seed_content = starter_message or f"\U0001f9f5 Thread created by Hermes: **{name}**"
seed_msg = await parent_channel.send(seed_content)
thread = await seed_msg.create_thread(
name=name,
auto_archive_duration=auto_archive_duration,
reason=reason,
)
return {
"success": True,
"thread_id": str(thread.id),
"thread_name": getattr(thread, "name", None) or name,
}
except Exception as fallback_error:
return {
"error": (
"Discord rejected direct thread creation and the fallback also failed. "
f"Direct error: {direct_error}. Fallback error: {fallback_error}"
)
}
# ------------------------------------------------------------------
# Auto-thread helpers
# ------------------------------------------------------------------
async def _auto_create_thread(self, message: 'DiscordMessage') -> Optional[Any]:
"""Create a thread from a user message for auto-threading.
Returns the created thread object, or ``None`` on failure.
"""
# Build a short thread name from the message
content = (message.content or "").strip()
thread_name = content[:80] if content else "Hermes"
if len(content) > 80:
thread_name = thread_name[:77] + "..."
try:
thread = await message.create_thread(name=thread_name, auto_archive_duration=1440)
return thread
except Exception as e:
logger.warning("[%s] Auto-thread creation failed: %s", self.name, e)
return None
async def send_exec_approval(
self, chat_id: str, command: str, approval_id: str
) -> SendResult:
@@ -852,6 +1072,19 @@ class DiscordAdapter(BasePlatformAdapter):
message.content = message.content.replace(f"<@{self._client.user.id}>", "").strip()
message.content = message.content.replace(f"<@!{self._client.user.id}>", "").strip()
# Auto-thread: when enabled, automatically create a thread for every
# new message in a text channel so each conversation is isolated.
# Messages already inside threads or DMs are unaffected.
auto_threaded_channel = None
if not is_thread and not isinstance(message.channel, discord.DMChannel):
auto_thread = os.getenv("DISCORD_AUTO_THREAD", "").lower() in ("true", "1", "yes")
if auto_thread:
thread = await self._auto_create_thread(message)
if thread:
is_thread = True
thread_id = str(thread.id)
auto_threaded_channel = thread
# Determine message type
msg_type = MessageType.TEXT
if message.content.startswith("/"):
@@ -870,13 +1103,16 @@ class DiscordAdapter(BasePlatformAdapter):
msg_type = MessageType.DOCUMENT
break
# When auto-threading kicked in, route responses to the new thread
effective_channel = auto_threaded_channel or message.channel
# Determine chat type
if isinstance(message.channel, discord.DMChannel):
chat_type = "dm"
chat_name = message.author.name
elif is_thread:
chat_type = "thread"
chat_name = self._format_thread_chat_name(message.channel)
chat_name = self._format_thread_chat_name(effective_channel)
else:
chat_type = "group"
chat_name = getattr(message.channel, "name", str(message.channel.id))
@@ -888,7 +1124,7 @@ class DiscordAdapter(BasePlatformAdapter):
# Build source
source = self.build_source(
chat_id=str(message.channel.id),
chat_id=str(effective_channel.id),
chat_name=chat_name,
chat_type=chat_type,
user_id=str(message.author.id),
+15 -1
View File
@@ -83,6 +83,7 @@ class HomeAssistantAdapter(BasePlatformAdapter):
self._watch_domains: Set[str] = set(extra.get("watch_domains", []))
self._watch_entities: Set[str] = set(extra.get("watch_entities", []))
self._ignore_entities: Set[str] = set(extra.get("ignore_entities", []))
self._watch_all: bool = bool(extra.get("watch_all", False))
self._cooldown_seconds: int = int(extra.get("cooldown_seconds", 30))
# Cooldown tracking: entity_id -> last_event_timestamp
@@ -115,6 +116,15 @@ class HomeAssistantAdapter(BasePlatformAdapter):
# Dedicated REST session for send() calls
self._rest_session = aiohttp.ClientSession()
# Warn if no event filters are configured
if not self._watch_domains and not self._watch_entities and not self._watch_all:
logger.warning(
"[%s] No watch_domains, watch_entities, or watch_all configured. "
"All state_changed events will be dropped. Configure filters in "
"your HA platform config to receive events.",
self.name,
)
# Start background listener
self._listen_task = asyncio.create_task(self._listen_loop())
self._running = True
@@ -257,13 +267,17 @@ class HomeAssistantAdapter(BasePlatformAdapter):
if entity_id in self._ignore_entities:
return
# Apply domain/entity watch filters
# Apply domain/entity watch filters (closed by default — require
# explicit watch_domains, watch_entities, or watch_all to forward)
domain = entity_id.split(".")[0] if "." in entity_id else ""
if self._watch_domains or self._watch_entities:
domain_match = domain in self._watch_domains if self._watch_domains else False
entity_match = entity_id in self._watch_entities if self._watch_entities else False
if not domain_match and not entity_match:
return
elif not self._watch_all:
# No filters configured and watch_all is off — drop the event
return
# Apply cooldown
now = time.time()
+3 -1
View File
@@ -26,6 +26,8 @@ _IS_WINDOWS = platform.system() == "Windows"
from pathlib import Path
from typing import Dict, List, Optional, Any
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
@@ -132,7 +134,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
)
self._session_path: Path = Path(config.extra.get(
"session_path",
Path.home() / ".hermes" / "whatsapp" / "session"
get_hermes_home() / "whatsapp" / "session"
))
self._message_queue: asyncio.Queue = asyncio.Queue()
self._bridge_log_fh = None
+36 -11
View File
@@ -1125,10 +1125,16 @@ class GatewayRunner:
get_model_context_length,
)
# Read model + compression config from config.yaml — same
# source of truth the agent itself uses.
# Read model + compression config from config.yaml.
# NOTE: hygiene threshold is intentionally HIGHER than the agent's
# own compressor (0.85 vs 0.50). Hygiene is a safety net for
# sessions that grew too large between turns — it fires pre-agent
# to prevent API failures. The agent's own compressor handles
# normal context management during its tool loop with accurate
# real token counts. Having hygiene at 0.50 caused premature
# compression on every turn in long gateway sessions.
_hyg_model = "anthropic/claude-sonnet-4.6"
_hyg_threshold_pct = 0.50
_hyg_threshold_pct = 0.85
_hyg_compression_enabled = True
try:
_hyg_cfg_path = _hermes_home / "config.yaml"
@@ -1144,22 +1150,18 @@ class GatewayRunner:
elif isinstance(_model_cfg, dict):
_hyg_model = _model_cfg.get("default", _hyg_model)
# Read compression settings
# Read compression settings — only use enabled flag.
# The threshold is intentionally separate from the agent's
# compression.threshold (hygiene runs higher).
_comp_cfg = _hyg_data.get("compression", {})
if isinstance(_comp_cfg, dict):
_hyg_threshold_pct = float(
_comp_cfg.get("threshold", _hyg_threshold_pct)
)
_hyg_compression_enabled = str(
_comp_cfg.get("enabled", True)
).lower() in ("true", "1", "yes")
except Exception:
pass
# Also check env overrides (same as run_agent.py)
_hyg_threshold_pct = float(
os.getenv("CONTEXT_COMPRESSION_THRESHOLD", str(_hyg_threshold_pct))
)
# Check env override for disabling compression entirely
if os.getenv("CONTEXT_COMPRESSION_ENABLED", "").lower() in ("false", "0", "no"):
_hyg_compression_enabled = False
@@ -1446,6 +1448,11 @@ class GatewayRunner:
response = agent_result.get("final_response", "")
agent_messages = agent_result.get("messages", [])
# If the agent's session_id changed during compression, update
# session_entry so transcript writes below go to the right session.
if agent_result.get("session_id") and agent_result["session_id"] != session_entry.session_id:
session_entry.session_id = agent_result["session_id"]
# Prepend reasoning/thinking if display is enabled
if getattr(self, "_show_reasoning", False) and response:
last_reasoning = agent_result.get("last_reasoning")
@@ -3495,6 +3502,23 @@ class GatewayRunner:
unique_tags.insert(0, "[[audio_as_voice]]")
final_response = final_response + "\n" + "\n".join(unique_tags)
# Sync session_id: the agent may have created a new session during
# mid-run context compression (_compress_context splits sessions).
# If so, update the session store entry so the NEXT message loads
# the compressed transcript, not the stale pre-compression one.
agent = agent_holder[0]
if agent and session_key and hasattr(agent, 'session_id') and agent.session_id != session_id:
logger.info(
"Session split detected: %s%s (compression)",
session_id, agent.session_id,
)
entry = self.session_store._entries.get(session_key)
if entry:
entry.session_id = agent.session_id
self.session_store._save()
effective_session_id = getattr(agent, 'session_id', session_id) if agent else session_id
return {
"final_response": final_response,
"last_reasoning": result.get("last_reasoning"),
@@ -3503,6 +3527,7 @@ class GatewayRunner:
"tools": tools_holder[0] or [],
"history_offset": len(agent_history),
"last_prompt_tokens": _last_prompt_toks,
"session_id": effective_session_id,
}
# Start progress message sender if enabled
+20
View File
@@ -177,6 +177,26 @@ def build_session_context_prompt(context: SessionContext) -> str:
elif context.source.user_id:
lines.append(f"**User ID:** {context.source.user_id}")
# Platform-specific behavioral notes
if context.source.platform == Platform.SLACK:
lines.append("")
lines.append(
"**Platform notes:** You are running inside Slack. "
"You do NOT have access to Slack-specific APIs — you cannot search "
"channel history, pin/unpin messages, manage channels, or list users. "
"Do not promise to perform these actions. If the user asks, explain "
"that you can only read messages sent directly to you and respond."
)
elif context.source.platform == Platform.DISCORD:
lines.append("")
lines.append(
"**Platform notes:** You are running inside Discord. "
"You do NOT have access to Discord-specific APIs — you cannot search "
"channel history, pin messages, manage roles, or list server members. "
"Do not promise to perform these actions. If the user asks, explain "
"that you can only read messages sent directly to you and respond."
)
# Connected platforms
platforms_list = ["local (files on this machine)"]
for p in context.connected_platforms:
+3 -1
View File
@@ -14,8 +14,10 @@ import time
from pathlib import Path
from typing import Optional
from hermes_cli.config import get_hermes_home
CACHE_PATH = Path(os.path.expanduser("~/.hermes/sticker_cache.json"))
CACHE_PATH = get_hermes_home() / "sticker_cache.json"
# Vision prompt for describing stickers -- kept concise to save tokens
STICKER_VISION_PROMPT = (
+23 -2
View File
@@ -1541,8 +1541,20 @@ def detect_external_credentials() -> List[Dict[str, Any]]:
# CLI Commands — login / logout
# =============================================================================
def _update_config_for_provider(provider_id: str, inference_base_url: str) -> Path:
"""Update config.yaml and auth.json to reflect the active provider."""
def _update_config_for_provider(
provider_id: str,
inference_base_url: str,
default_model: Optional[str] = None,
) -> Path:
"""Update config.yaml and auth.json to reflect the active provider.
When *default_model* is provided the function also writes it as the
``model.default`` value. This prevents a race condition where the
gateway (which re-reads config per-message) picks up the new provider
before the caller has finished model selection, resulting in a
mismatched model/provider (e.g. ``anthropic/claude-opus-4.6`` sent to
MiniMax's API).
"""
# Set active_provider in auth.json so auto-resolution picks this provider
with _auth_store_lock():
auth_store = _load_auth_store()
@@ -1576,6 +1588,15 @@ def _update_config_for_provider(provider_id: str, inference_base_url: str) -> Pa
else:
# Clear stale base_url to prevent contamination when switching providers
model_cfg.pop("base_url", None)
# When switching to a non-OpenRouter provider, ensure model.default is
# valid for the new provider. An OpenRouter-formatted name like
# "anthropic/claude-opus-4.6" will fail on direct-API providers.
if default_model:
cur_default = model_cfg.get("default", "")
if not cur_default or "/" in cur_default:
model_cfg["default"] = default_model
config["model"] = model_cfg
config_path.write_text(yaml.safe_dump(config, sort_keys=False))
+45 -35
View File
@@ -227,43 +227,53 @@ def approval_callback(cli, command: str, description: str) -> str:
Shows a selection UI with choices: once / session / always / deny.
When the command is longer than 70 characters, a "view" option is
included so the user can reveal the full text before deciding.
Uses cli._approval_lock to serialize concurrent requests (e.g. from
parallel delegation subtasks) so each prompt gets its own turn.
"""
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
if len(command) > 70:
choices.append("view")
lock = getattr(cli, "_approval_lock", None)
if lock is None:
import threading
cli._approval_lock = threading.Lock()
lock = cli._approval_lock
cli._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
cli._approval_deadline = _time.monotonic() + timeout
with lock:
timeout = 60
response_queue = queue.Queue()
choices = ["once", "session", "always", "deny"]
if len(command) > 70:
choices.append("view")
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cli._approval_state = {
"command": command,
"description": description,
"choices": choices,
"selected": 0,
"response_queue": response_queue,
}
cli._approval_deadline = _time.monotonic() + timeout
while True:
try:
result = response_queue.get(timeout=1)
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
return result
except queue.Empty:
remaining = cli._approval_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"
while True:
try:
result = response_queue.get(timeout=1)
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
return result
except queue.Empty:
remaining = cli._approval_deadline - _time.monotonic()
if remaining <= 0:
break
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cli._approval_state = None
cli._approval_deadline = 0
if hasattr(cli, "_app") and cli._app:
cli._app.invalidate()
cprint(f"\n{_DIM} ⏱ Timeout — denying command{_RST}")
return "deny"
+33 -3
View File
@@ -18,6 +18,36 @@ DEFAULT_CODEX_MODELS: List[str] = [
"gpt-5.1-codex-mini",
]
_FORWARD_COMPAT_TEMPLATE_MODELS: List[tuple[str, tuple[str, ...]]] = [
("gpt-5.3-codex", ("gpt-5.2-codex",)),
("gpt-5.4", ("gpt-5.3-codex", "gpt-5.2-codex")),
("gpt-5.3-codex-spark", ("gpt-5.3-codex", "gpt-5.2-codex")),
]
def _add_forward_compat_models(model_ids: List[str]) -> List[str]:
"""Add Clawdbot-style synthetic forward-compat Codex models.
If a newer Codex slug isn't returned by live discovery, surface it when an
older compatible template model is present. This mirrors Clawdbot's
synthetic catalog / forward-compat behavior for GPT-5 Codex variants.
"""
ordered: List[str] = []
seen: set[str] = set()
for model_id in model_ids:
if model_id not in seen:
ordered.append(model_id)
seen.add(model_id)
for synthetic_model, template_models in _FORWARD_COMPAT_TEMPLATE_MODELS:
if synthetic_model in seen:
continue
if any(template in seen for template in template_models):
ordered.append(synthetic_model)
seen.add(synthetic_model)
return ordered
def _fetch_models_from_api(access_token: str) -> List[str]:
"""Fetch available models from the Codex API. Returns visible models sorted by priority."""
@@ -54,7 +84,7 @@ def _fetch_models_from_api(access_token: str) -> List[str]:
sortable.append((rank, slug))
sortable.sort(key=lambda x: (x[0], x[1]))
return [slug for _, slug in sortable]
return _add_forward_compat_models([slug for _, slug in sortable])
def _read_default_model(codex_home: Path) -> Optional[str]:
@@ -125,7 +155,7 @@ def get_codex_model_ids(access_token: Optional[str] = None) -> List[str]:
if access_token:
api_models = _fetch_models_from_api(access_token)
if api_models:
return api_models
return _add_forward_compat_models(api_models)
# Fall back to local sources
default_model = _read_default_model(codex_home)
@@ -140,4 +170,4 @@ def get_codex_model_ids(access_token: Optional[str] = None) -> List[str]:
if model_id not in ordered:
ordered.append(model_id)
return ordered
return _add_forward_compat_models(ordered)
+3 -3
View File
@@ -16,9 +16,9 @@ from prompt_toolkit.completion import Completer, Completion
# Commands organized by category for better help display
COMMANDS_BY_CATEGORY = {
"Session": {
"/new": "Start a new conversation (reset history)",
"/reset": "Reset conversation only (keep screen)",
"/clear": "Clear screen and reset conversation (fresh start)",
"/new": "Start a new session (fresh session ID + history)",
"/reset": "Start a new session (alias for /new)",
"/clear": "Clear screen and start a new session",
"/history": "Show conversation history",
"/save": "Save the current conversation",
"/retry": "Retry the last message (resend to agent)",
+7 -2
View File
@@ -194,8 +194,13 @@ DEFAULT_CONFIG = {
},
"stt": {
"enabled": True,
"model": "whisper-1",
"provider": "local", # "local" (free, faster-whisper) | "openai" (Whisper API)
"local": {
"model": "base", # tiny, base, small, medium, large-v3
},
"openai": {
"model": "whisper-1", # whisper-1, gpt-4o-mini-transcribe, gpt-4o-transcribe
},
},
"human_delay": {
+4
View File
@@ -97,6 +97,10 @@ def check_info(text: str):
def run_doctor(args):
"""Run diagnostic checks."""
should_fix = getattr(args, 'fix', False)
# Doctor runs from the interactive CLI, so CLI-gated tool availability
# checks (like cronjob management) should see the same context as `hermes`.
os.environ.setdefault("HERMES_INTERACTIVE", "1")
issues = []
manual_issues = [] # issues that can't be auto-fixed
+16 -4
View File
@@ -13,7 +13,7 @@ from pathlib import Path
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
from hermes_cli.config import get_env_value, save_env_value
from hermes_cli.config import get_env_value, get_hermes_home, save_env_value
from hermes_cli.setup import (
print_header, print_info, print_success, print_warning, print_error,
prompt, prompt_choice, prompt_yes_no,
@@ -283,7 +283,7 @@ def systemd_status(deep: bool = False):
def generate_launchd_plist() -> str:
python_path = get_python_path()
working_dir = str(PROJECT_ROOT)
log_dir = Path.home() / ".hermes" / "logs"
log_dir = get_hermes_home() / "logs"
log_dir.mkdir(parents=True, exist_ok=True)
return f"""<?xml version="1.0" encoding="UTF-8"?>
@@ -380,7 +380,7 @@ def launchd_status(deep: bool = False):
print("✗ Gateway service is not loaded")
if deep:
log_file = Path.home() / ".hermes" / "logs" / "gateway.log"
log_file = get_hermes_home() / "logs" / "gateway.log"
if log_file.exists():
print()
print("Recent logs:")
@@ -557,7 +557,7 @@ def _platform_status(platform: dict) -> str:
val = get_env_value(token_var)
if token_var == "WHATSAPP_ENABLED":
if val and val.lower() == "true":
session_file = Path.home() / ".hermes" / "whatsapp" / "session" / "creds.json"
session_file = get_hermes_home() / "whatsapp" / "session" / "creds.json"
if session_file.exists():
return "configured + paired"
return "enabled, not paired"
@@ -623,6 +623,18 @@ def _setup_standard_platform(platform: dict):
value = prompt(f" {var['prompt']}", password=False)
if value:
cleaned = value.replace(" ", "")
# For Discord, strip common prefixes (user:123, <@123>, <@!123>)
if "DISCORD" in var["name"]:
parts = []
for uid in cleaned.split(","):
uid = uid.strip()
if uid.startswith("<@") and uid.endswith(">"):
uid = uid.lstrip("<@!").rstrip(">")
if uid.lower().startswith("user:"):
uid = uid[5:]
if uid:
parts.append(uid)
cleaned = ",".join(parts)
save_env_value(var["name"], cleaned)
print_success(f" Saved — only these users can interact with the bot.")
allowed_val_set = cleaned
+3 -1
View File
@@ -648,7 +648,7 @@ def cmd_whatsapp(args):
print("✓ Bridge dependencies already installed")
# ── Step 5: Check for existing session ───────────────────────────────
session_dir = Path.home() / ".hermes" / "whatsapp" / "session"
session_dir = get_hermes_home() / "whatsapp" / "session"
session_dir.mkdir(parents=True, exist_ok=True)
if (session_dir / "creds.json").exists():
@@ -1057,6 +1057,7 @@ def _model_flow_openai_codex(config, current_model=""):
_codex_token = _codex_creds.get("api_key")
except Exception:
pass
codex_models = get_codex_model_ids(access_token=_codex_token)
selected = _prompt_model_selection(codex_models, current_model=current_model)
@@ -1072,6 +1073,7 @@ def _model_flow_openai_codex(config, current_model=""):
print("No change.")
def _model_flow_custom(config):
"""Custom endpoint: collect URL, API key, and model name.
+1
View File
@@ -40,6 +40,7 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
"deepseek-v3.2",
],
"openai-codex": [
"gpt-5.3-codex",
"gpt-5.2-codex",
"gpt-5.1-codex-mini",
"gpt-5.1-codex-max",
+48 -9
View File
@@ -111,7 +111,17 @@ def _setup_provider_model_selection(config, provider_id, current_model, prompt_c
custom = prompt_fn("Enter model name")
if custom:
_set_default_model(config, custom)
# else: keep current
else:
# "Keep current" selected — validate it's compatible with the new
# provider. OpenRouter-formatted names (containing "/") won't work
# on direct-API providers and would silently break the gateway.
if "/" in (current_model or "") and provider_models:
print_warning(
f"Current model \"{current_model}\" looks like an OpenRouter model "
f"and won't work with {pconfig.name}. "
f"Switching to {provider_models[0]}."
)
_set_default_model(config, provider_models[0])
def _sync_model_from_disk(config: Dict[str, Any]) -> None:
@@ -644,6 +654,7 @@ def setup_model_provider(config: dict):
_update_config_for_provider,
_login_openai_codex,
get_codex_auth_status,
resolve_codex_runtime_credentials,
DEFAULT_CODEX_BASE_URL,
detect_external_credentials,
)
@@ -967,7 +978,7 @@ def setup_model_provider(config: dict):
if existing_custom:
save_env_value("OPENAI_BASE_URL", "")
save_env_value("OPENAI_API_KEY", "")
_update_config_for_provider("zai", zai_base_url)
_update_config_for_provider("zai", zai_base_url, default_model="glm-5")
_set_model_provider(config, "zai", zai_base_url)
elif provider_idx == 5: # Kimi / Moonshot
@@ -1000,7 +1011,7 @@ def setup_model_provider(config: dict):
if existing_custom:
save_env_value("OPENAI_BASE_URL", "")
save_env_value("OPENAI_API_KEY", "")
_update_config_for_provider("kimi-coding", pconfig.inference_base_url)
_update_config_for_provider("kimi-coding", pconfig.inference_base_url, default_model="kimi-k2.5")
_set_model_provider(config, "kimi-coding", pconfig.inference_base_url)
elif provider_idx == 6: # MiniMax
@@ -1033,7 +1044,7 @@ def setup_model_provider(config: dict):
if existing_custom:
save_env_value("OPENAI_BASE_URL", "")
save_env_value("OPENAI_API_KEY", "")
_update_config_for_provider("minimax", pconfig.inference_base_url)
_update_config_for_provider("minimax", pconfig.inference_base_url, default_model="MiniMax-M2.5")
_set_model_provider(config, "minimax", pconfig.inference_base_url)
elif provider_idx == 7: # MiniMax China
@@ -1066,7 +1077,7 @@ def setup_model_provider(config: dict):
if existing_custom:
save_env_value("OPENAI_BASE_URL", "")
save_env_value("OPENAI_API_KEY", "")
_update_config_for_provider("minimax-cn", pconfig.inference_base_url)
_update_config_for_provider("minimax-cn", pconfig.inference_base_url, default_model="MiniMax-M2.5")
_set_model_provider(config, "minimax-cn", pconfig.inference_base_url)
elif provider_idx == 8: # Anthropic
@@ -1170,7 +1181,7 @@ def setup_model_provider(config: dict):
save_env_value("OPENAI_API_KEY", "")
# Don't save base_url for Anthropic — resolve_runtime_provider()
# always hardcodes it. Stale base_urls contaminate other providers.
_update_config_for_provider("anthropic", "")
_update_config_for_provider("anthropic", "", default_model="claude-opus-4-6")
_set_model_provider(config, "anthropic")
# else: provider_idx == 9 (Keep current) — only shown when a provider already exists
@@ -1256,7 +1267,15 @@ def setup_model_provider(config: dict):
elif selected_provider == "openai-codex":
from hermes_cli.codex_models import get_codex_model_ids
codex_models = get_codex_model_ids()
codex_token = None
try:
codex_creds = resolve_codex_runtime_credentials()
codex_token = codex_creds.get("api_key")
except Exception as exc:
logger.debug("Could not resolve Codex runtime credentials for model list: %s", exc)
codex_models = get_codex_model_ids(access_token=codex_token)
model_choices = codex_models + [f"Keep current ({current_model})"]
default_codex = 0
if current_model in codex_models:
@@ -1925,7 +1944,17 @@ def setup_gateway(config: dict):
"Allowed user IDs or usernames (comma-separated, leave empty for open access)"
)
if allowed_users:
save_env_value("DISCORD_ALLOWED_USERS", allowed_users.replace(" ", ""))
# Clean up common prefixes (user:123, <@123>, <@!123>)
cleaned_ids = []
for uid in allowed_users.replace(" ", "").split(","):
uid = uid.strip()
if uid.startswith("<@") and uid.endswith(">"):
uid = uid.lstrip("<@!").rstrip(">")
if uid.lower().startswith("user:"):
uid = uid[5:]
if uid:
cleaned_ids.append(uid)
save_env_value("DISCORD_ALLOWED_USERS", ",".join(cleaned_ids))
print_success("Discord allowlist configured")
else:
print_info(
@@ -1960,8 +1989,18 @@ def setup_gateway(config: dict):
)
allowed_users = prompt("Allowed user IDs (comma-separated)")
if allowed_users:
# Clean up common prefixes (user:123, <@123>, <@!123>)
cleaned_ids = []
for uid in allowed_users.replace(" ", "").split(","):
uid = uid.strip()
if uid.startswith("<@") and uid.endswith(">"):
uid = uid.lstrip("<@!").rstrip(">")
if uid.lower().startswith("user:"):
uid = uid[5:]
if uid:
cleaned_ids.append(uid)
save_env_value(
"DISCORD_ALLOWED_USERS", allowed_users.replace(" ", "")
"DISCORD_ALLOWED_USERS", ",".join(cleaned_ids)
)
print_success("Discord allowlist configured")
+3 -3
View File
@@ -12,7 +12,7 @@ from pathlib import Path
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
from hermes_cli.colors import Colors, color
from hermes_cli.config import get_env_path, get_env_value
from hermes_cli.config import get_env_path, get_env_value, get_hermes_home
from hermes_constants import OPENROUTER_MODELS_URL
def check_mark(ok: bool) -> str:
@@ -267,7 +267,7 @@ def show_status(args):
print()
print(color("◆ Scheduled Jobs", Colors.CYAN, Colors.BOLD))
jobs_file = Path.home() / ".hermes" / "cron" / "jobs.json"
jobs_file = get_hermes_home() / "cron" / "jobs.json"
if jobs_file.exists():
import json
try:
@@ -287,7 +287,7 @@ def show_status(args):
print()
print(color("◆ Sessions", Colors.CYAN, Colors.BOLD))
sessions_file = Path.home() / ".hermes" / "sessions" / "sessions.json"
sessions_file = get_hermes_home() / "sessions" / "sessions.json"
if sessions_file.exists():
import json
try:
+622 -462
View File
File diff suppressed because it is too large Load Diff
+459 -280
View File
@@ -4,339 +4,518 @@
// --- Platform install commands ---
const PLATFORMS = {
linux: {
command: 'curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash',
prompt: '$',
note: 'Works on Linux, macOS & WSL2 · No prerequisites · Installs everything automatically',
stepNote: 'Installs uv, Python 3.11, clones the repo, sets up everything. No sudo needed.',
},
linux: {
command:
"curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash",
prompt: "$",
note: "Works on Linux, macOS & WSL2 · No prerequisites · Installs everything automatically",
stepNote:
"Installs uv, Python 3.11, clones the repo, sets up everything. No sudo needed.",
},
};
function detectPlatform() {
return 'linux';
return "linux";
}
function switchPlatform(platform) {
const cfg = PLATFORMS[platform];
if (!cfg) return;
const cfg = PLATFORMS[platform];
if (!cfg) return;
// Update hero install widget
const commandEl = document.getElementById('install-command');
const promptEl = document.getElementById('install-prompt');
const noteEl = document.getElementById('install-note');
// Update hero install widget
const commandEl = document.getElementById("install-command");
const promptEl = document.getElementById("install-prompt");
const noteEl = document.getElementById("install-note");
if (commandEl) commandEl.textContent = cfg.command;
if (promptEl) promptEl.textContent = cfg.prompt;
if (noteEl) noteEl.textContent = cfg.note;
if (commandEl) commandEl.textContent = cfg.command;
if (promptEl) promptEl.textContent = cfg.prompt;
if (noteEl) noteEl.textContent = cfg.note;
// Update active tab in hero
document.querySelectorAll('.install-tab').forEach(tab => {
tab.classList.toggle('active', tab.dataset.platform === platform);
});
// Update active tab in hero
document.querySelectorAll(".install-tab").forEach((tab) => {
tab.classList.toggle("active", tab.dataset.platform === platform);
});
// Sync the step section tabs too
switchStepPlatform(platform);
// Sync the step section tabs too
switchStepPlatform(platform);
}
function switchStepPlatform(platform) {
const cfg = PLATFORMS[platform];
if (!cfg) return;
const cfg = PLATFORMS[platform];
if (!cfg) return;
const commandEl = document.getElementById('step1-command');
const copyBtn = document.getElementById('step1-copy');
const noteEl = document.getElementById('step1-note');
const commandEl = document.getElementById("step1-command");
const copyBtn = document.getElementById("step1-copy");
const noteEl = document.getElementById("step1-note");
if (commandEl) commandEl.textContent = cfg.command;
if (copyBtn) copyBtn.setAttribute('data-text', cfg.command);
if (noteEl) noteEl.textContent = cfg.stepNote;
if (commandEl) commandEl.textContent = cfg.command;
if (copyBtn) copyBtn.setAttribute("data-text", cfg.command);
if (noteEl) noteEl.textContent = cfg.stepNote;
// Update active tab in step section
document.querySelectorAll('.code-tab').forEach(tab => {
tab.classList.toggle('active', tab.dataset.platform === platform);
// Update active tab in step section
document.querySelectorAll(".code-tab").forEach((tab) => {
tab.classList.toggle("active", tab.dataset.platform === platform);
});
}
function toggleMobileNav() {
document.getElementById("nav-mobile").classList.toggle("open");
document.getElementById("nav-hamburger").classList.toggle("open");
}
function toggleSpecs() {
const wrapper = document.getElementById("specs-wrapper");
const btn = document.getElementById("specs-toggle");
const label = btn.querySelector(".toggle-label");
const isOpen = wrapper.classList.contains("open");
if (isOpen) {
wrapper.style.maxHeight = wrapper.scrollHeight + "px";
requestAnimationFrame(() => {
wrapper.style.maxHeight = "0";
});
wrapper.classList.remove("open");
btn.classList.remove("open");
if (label) label.textContent = "More details";
} else {
wrapper.classList.add("open");
wrapper.style.maxHeight = wrapper.scrollHeight + "px";
btn.classList.add("open");
if (label) label.textContent = "Less";
wrapper.addEventListener(
"transitionend",
() => {
if (wrapper.classList.contains("open")) {
wrapper.style.maxHeight = "none";
}
},
{ once: true }
);
}
}
// --- Copy to clipboard ---
function copyInstall() {
const text = document.getElementById('install-command').textContent;
navigator.clipboard.writeText(text).then(() => {
const btn = document.querySelector('.install-widget-body .copy-btn');
const original = btn.querySelector('.copy-text').textContent;
btn.querySelector('.copy-text').textContent = 'Copied!';
btn.style.color = 'var(--gold)';
setTimeout(() => {
btn.querySelector('.copy-text').textContent = original;
btn.style.color = '';
}, 2000);
});
const text = document.getElementById("install-command").textContent;
navigator.clipboard.writeText(text).then(() => {
const btn = document.querySelector(".install-widget-body .copy-btn");
const original = btn.querySelector(".copy-text").textContent;
btn.querySelector(".copy-text").textContent = "Copied!";
btn.style.color = "var(--primary-light)";
setTimeout(() => {
btn.querySelector(".copy-text").textContent = original;
btn.style.color = "";
}, 2000);
});
}
function copyText(btn) {
const text = btn.getAttribute('data-text');
navigator.clipboard.writeText(text).then(() => {
const original = btn.textContent;
btn.textContent = 'Copied!';
btn.style.color = 'var(--gold)';
setTimeout(() => {
btn.textContent = original;
btn.style.color = '';
}, 2000);
});
const text = btn.getAttribute("data-text");
navigator.clipboard.writeText(text).then(() => {
const original = btn.textContent;
btn.textContent = "Copied!";
btn.style.color = "var(--primary-light)";
setTimeout(() => {
btn.textContent = original;
btn.style.color = "";
}, 2000);
});
}
// --- Scroll-triggered fade-in ---
function initScrollAnimations() {
const elements = document.querySelectorAll(
'.feature-card, .tool-pill, .platform-group, .skill-category, ' +
'.install-step, .research-card, .footer-card, .section-header, ' +
'.lead-text, .section-desc, .terminal-window'
);
const elements = document.querySelectorAll(
".feature-card, .install-step, " +
".section-header, .terminal-window",
);
elements.forEach(el => el.classList.add('fade-in'));
elements.forEach((el) => el.classList.add("fade-in"));
const observer = new IntersectionObserver((entries) => {
entries.forEach(entry => {
if (entry.isIntersecting) {
// Stagger children within grids
const parent = entry.target.parentElement;
if (parent) {
const siblings = parent.querySelectorAll('.fade-in');
let idx = Array.from(siblings).indexOf(entry.target);
if (idx < 0) idx = 0;
setTimeout(() => {
entry.target.classList.add('visible');
}, idx * 60);
} else {
entry.target.classList.add('visible');
}
observer.unobserve(entry.target);
}
});
}, { threshold: 0.1, rootMargin: '0px 0px -40px 0px' });
const observer = new IntersectionObserver(
(entries) => {
entries.forEach((entry) => {
if (entry.isIntersecting) {
// Stagger children within grids
const parent = entry.target.parentElement;
if (parent) {
const siblings = parent.querySelectorAll(".fade-in");
let idx = Array.from(siblings).indexOf(entry.target);
if (idx < 0) idx = 0;
setTimeout(() => {
entry.target.classList.add("visible");
}, idx * 60);
} else {
entry.target.classList.add("visible");
}
observer.unobserve(entry.target);
}
});
},
{ threshold: 0.1, rootMargin: "0px 0px -40px 0px" },
);
elements.forEach(el => observer.observe(el));
elements.forEach((el) => observer.observe(el));
}
// --- Terminal Demo ---
const CURSOR = '<span class="terminal-cursor">█</span>';
const demoSequence = [
// Scene 1: Research task with delegation
{ type: 'prompt', text: ' ' },
{ type: 'type', text: 'Research the latest approaches to GRPO training and write a summary', delay: 30 },
{ type: 'pause', ms: 600 },
{ type: 'output', lines: [
'',
'<span class="t-dim">┊ 🔍 web_search "GRPO reinforcement learning 2026" 1.2s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 📄 web_extract arxiv.org/abs/2402.03300 3.1s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 🔍 web_search "GRPO vs PPO ablation results" 0.9s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 📄 web_extract huggingface.co/blog/grpo 2.8s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ ✍️ write_file ~/research/grpo-summary.md 0.1s</span>',
]},
{ type: 'pause', ms: 500 },
{ type: 'output', lines: [
'',
'<span class="t-text">Done! I\'ve written a summary covering:</span>',
'',
'<span class="t-text"> <span class="t-green">✓</span> GRPO\'s group-relative advantage (no critic model needed)</span>',
'<span class="t-text"> <span class="t-green">✓</span> Comparison with PPO/DPO on reasoning benchmarks</span>',
'<span class="t-text"> <span class="t-green">✓</span> Implementation notes for Axolotl and TRL</span>',
'',
'<span class="t-text">Saved to</span> <span class="t-amber">~/research/grpo-summary.md</span>',
]},
{ type: 'pause', ms: 2500 },
{ type: "prompt", text: " " },
{
type: "type",
text: "Research the latest approaches to GRPO training and write a summary",
delay: 30,
},
{ type: "pause", ms: 600 },
{
type: "output",
lines: [
"",
'<span class="t-dim"> web_search "GRPO reinforcement learning 2026" 1.2s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> web_extract arxiv.org/abs/2402.03300 3.1s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> web_search "GRPO vs PPO ablation results" 0.9s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> web_extract huggingface.co/blog/grpo 2.8s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> write_file ~/research/grpo-summary.md 0.1s</span>',
],
},
{ type: "pause", ms: 500 },
{
type: "output",
lines: [
"",
'<span class="t-text">Done! I\'ve written a summary covering:</span>',
"",
'<span class="t-text"> <span class="t-green">✓</span> GRPO\'s group-relative advantage (no critic model needed)</span>',
'<span class="t-text"> <span class="t-green">✓</span> Comparison with PPO/DPO on reasoning benchmarks</span>',
'<span class="t-text"> <span class="t-green">✓</span> Implementation notes for Axolotl and TRL</span>',
"",
'<span class="t-text">Saved to</span> <span class="t-accent">~/research/grpo-summary.md</span>',
],
},
{ type: "pause", ms: 2500 },
// Scene 2: Quick delegation
{ type: 'clear' },
{ type: 'prompt', text: ' ' },
{ type: 'type', text: 'Review the PR at NousResearch/hermes-agent#42 and fix any issues', delay: 30 },
{ type: 'pause', ms: 600 },
{ type: 'output', lines: [
'',
'<span class="t-dim">┊ 🔀 delegate_task "review PR #42 changes" 2.1s</span>',
]},
{ type: 'pause', ms: 500 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 💻 git diff main..pr-42 0.4s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ ✏️ patch tools/registry.py 0.1s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 💻 python -m pytest tests/ -x 3.2s</span>',
]},
{ type: 'pause', ms: 400 },
{ type: 'output', lines: [
'<span class="t-dim">┊ 💻 git commit -m "fix: handle empty tool schemas" 0.3s</span>',
]},
{ type: 'pause', ms: 500 },
{ type: 'output', lines: [
'',
'<span class="t-text">Found 2 issues in the PR and fixed both:</span>',
'',
'<span class="t-text"> <span class="t-green">✓</span> Empty tool schema crash in registry.py — added guard</span>',
'<span class="t-text"> <span class="t-green">✓</span> Missing error handling in delegate_tool.py — added try/except</span>',
'',
'<span class="t-text">Tests pass. Committed the fix and pushed to the PR branch.</span>',
'<span class="t-text">I also saved a</span> <span class="t-amber">skill</span> <span class="t-text">for this PR review pattern.</span>',
]},
{ type: 'pause', ms: 2500 },
{ type: "clear" },
{ type: "prompt", text: " " },
{
type: "type",
text: "Review the PR at NousResearch/hermes-agent#42 and fix any issues",
delay: 30,
},
{ type: "pause", ms: 600 },
{
type: "output",
lines: [
"",
'<span class="t-dim"> delegate_task "review PR #42 changes" 2.1s</span>',
],
},
{ type: "pause", ms: 500 },
{
type: "output",
lines: [
'<span class="t-dim"> git diff main..pr-42 0.4s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> patch tools/registry.py 0.1s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> python -m pytest tests/ -x 3.2s</span>',
],
},
{ type: "pause", ms: 400 },
{
type: "output",
lines: [
'<span class="t-dim"> git commit -m "fix: handle empty tool schemas" 0.3s</span>',
],
},
{ type: "pause", ms: 500 },
{
type: "output",
lines: [
"",
'<span class="t-text">Found 2 issues in the PR and fixed both:</span>',
"",
'<span class="t-text"> <span class="t-green">✓</span> Empty tool schema crash in registry.py — added guard</span>',
'<span class="t-text"> <span class="t-green">✓</span> Missing error handling in delegate_tool.py — added try/except</span>',
"",
'<span class="t-text">Tests pass. Committed the fix and pushed to the PR branch.</span>',
'<span class="t-text">I also saved a</span> <span class="t-accent">skill</span> <span class="t-text">for this PR review pattern.</span>',
],
},
{ type: "pause", ms: 2500 },
// Scene 3: Session search by keyword
{ type: 'clear' },
{ type: 'prompt', text: ' ' },
{ type: 'type', text: 'How did we fix that Docker networking issue?', delay: 35 },
{ type: 'pause', ms: 500 },
{ type: 'output', lines: [
'',
'<span class="t-dim">┊ 🔎 session_search "Docker networking" 1.4s</span>',
]},
{ type: 'pause', ms: 500 },
{ type: 'output', lines: [
'',
'<span class="t-text">Found it — from a session on February 12th:</span>',
'',
'<span class="t-text">The containers couldn\'t reach each other because the compose</span>',
'<span class="t-text">file was using the default bridge network. We switched to a</span>',
'<span class="t-text">custom network with</span> <span class="t-amber">driver: overlay</span><span class="t-text">, added explicit</span>',
'<span class="t-text">aliases, and set</span> <span class="t-amber">dns: 8.8.8.8</span> <span class="t-text">as a fallback.</span>',
'',
'<span class="t-text">The fix was committed in</span> <span class="t-amber">docker-compose.prod.yml</span><span class="t-text">.</span>',
]},
{ type: 'pause', ms: 3000 },
{ type: "clear" },
{ type: "prompt", text: " " },
{
type: "type",
text: "How did we fix that Docker networking issue?",
delay: 35,
},
{ type: "pause", ms: 500 },
{
type: "output",
lines: [
"",
'<span class="t-dim"> session_search "Docker networking" 1.4s</span>',
],
},
{ type: "pause", ms: 500 },
{
type: "output",
lines: [
"",
'<span class="t-text">Found it — from a session on February 12th:</span>',
"",
'<span class="t-text">The containers couldn\'t reach each other because the compose</span>',
'<span class="t-text">file was using the default bridge network. We switched to a</span>',
'<span class="t-text">custom network with</span> <span class="t-accent">driver: overlay</span><span class="t-text">, added explicit</span>',
'<span class="t-text">aliases, and set</span> <span class="t-accent">dns: 8.8.8.8</span> <span class="t-text">as a fallback.</span>',
"",
'<span class="t-text">The fix was committed in</span> <span class="t-accent">docker-compose.prod.yml</span><span class="t-text">.</span>',
],
},
{ type: "pause", ms: 3000 },
];
class TerminalDemo {
constructor(element, cursorElement) {
this.el = element;
this.cursor = cursorElement;
this.running = false;
this.content = '';
this.observer = null;
}
constructor(container) {
this.container = container;
this.running = false;
this.content = "";
}
async start() {
if (this.running) return;
this.running = true;
while (this.running) {
for (const step of demoSequence) {
if (!this.running) return;
await this.execute(step);
}
// Loop
this.clear();
await this.sleep(1000);
async start() {
if (this.running) return;
this.running = true;
while (this.running) {
for (const step of demoSequence) {
if (!this.running) return;
await this.execute(step);
}
this.clear();
await this.sleep(1000);
}
}
stop() {
this.running = false;
}
async execute(step) {
switch (step.type) {
case "prompt":
this.append(`<span class="t-prompt">${step.text}</span>`);
break;
case "type":
for (const char of step.text) {
if (!this.running) return;
this.append(`<span class="t-cmd">${char}</span>`);
await this.sleep(step.delay || 30);
}
}
stop() {
this.running = false;
}
async execute(step) {
switch (step.type) {
case 'prompt':
this.append(`<span class="t-prompt">${step.text}</span>`);
break;
case 'type':
for (const char of step.text) {
if (!this.running) return;
this.append(`<span class="t-cmd">${char}</span>`);
await this.sleep(step.delay || 30);
}
break;
case 'output':
for (const line of step.lines) {
if (!this.running) return;
this.append('\n' + line);
await this.sleep(50);
}
break;
case 'pause':
await this.sleep(step.ms);
break;
case 'clear':
this.clear();
break;
break;
case "output":
for (const line of step.lines) {
if (!this.running) return;
this.append("\n" + line);
await this.sleep(50);
}
break;
case "pause":
await this.sleep(step.ms);
break;
case "clear":
this.clear();
break;
}
}
append(html) {
this.content += html;
this.el.innerHTML = this.content;
// Keep cursor at end
this.el.parentElement.scrollTop = this.el.parentElement.scrollHeight;
}
append(html) {
this.content += html;
this.render();
}
clear() {
this.content = '';
this.el.innerHTML = '';
}
render() {
this.container.innerHTML = this.content + CURSOR;
this.container.scrollTop = this.container.scrollHeight;
}
sleep(ms) {
return new Promise(resolve => setTimeout(resolve, ms));
}
clear() {
this.content = "";
this.container.innerHTML = "";
}
sleep(ms) {
return new Promise((resolve) => setTimeout(resolve, ms));
}
}
// --- Noise Overlay (ported from hermes-chat NoiseOverlay) ---
function initNoiseOverlay() {
if (window.matchMedia("(prefers-reduced-motion: reduce)").matches) return;
if (typeof THREE === "undefined") return;
const canvas = document.getElementById("noise-overlay");
if (!canvas) return;
const vertexShader = `
varying vec2 vUv;
void main() {
vUv = uv;
gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0);
}
`;
const fragmentShader = `
uniform vec2 uRes;
uniform float uDpr, uSize, uDensity, uOpacity;
uniform vec3 uColor;
varying vec2 vUv;
float hash(vec2 p) {
vec3 p3 = fract(vec3(p.xyx) * 0.1031);
p3 += dot(p3, p3.yzx + 33.33);
return fract((p3.x + p3.y) * p3.z);
}
void main() {
float n = hash(floor(vUv * uRes / (uSize * uDpr)));
gl_FragColor = vec4(uColor, step(1.0 - uDensity, n)) * uOpacity;
}
`;
function hexToVec3(hex) {
const c = hex.replace("#", "");
return new THREE.Vector3(
parseInt(c.substring(0, 2), 16) / 255,
parseInt(c.substring(2, 4), 16) / 255,
parseInt(c.substring(4, 6), 16) / 255,
);
}
const renderer = new THREE.WebGLRenderer({
alpha: true,
canvas,
premultipliedAlpha: false,
});
renderer.setClearColor(0x000000, 0);
const scene = new THREE.Scene();
const camera = new THREE.OrthographicCamera(-1, 1, 1, -1, 0, 1);
const geo = new THREE.PlaneGeometry(2, 2);
const mat = new THREE.ShaderMaterial({
vertexShader,
fragmentShader,
transparent: true,
uniforms: {
uColor: { value: hexToVec3("#8090BB") },
uDensity: { value: 0.1 },
uDpr: { value: 1 },
uOpacity: { value: 0.4 },
uRes: { value: new THREE.Vector2() },
uSize: { value: 1.0 },
},
});
scene.add(new THREE.Mesh(geo, mat));
function resize() {
const dpr = window.devicePixelRatio;
const w = window.innerWidth;
const h = window.innerHeight;
renderer.setSize(w, h);
renderer.setPixelRatio(dpr);
mat.uniforms.uRes.value.set(w * dpr, h * dpr);
mat.uniforms.uDpr.value = dpr;
}
resize();
window.addEventListener("resize", resize);
function loop() {
requestAnimationFrame(loop);
renderer.render(scene, camera);
}
loop();
}
// --- Initialize ---
document.addEventListener('DOMContentLoaded', () => {
// Auto-detect platform and set the right install command
const detectedPlatform = detectPlatform();
switchPlatform(detectedPlatform);
document.addEventListener("DOMContentLoaded", () => {
const detectedPlatform = detectPlatform();
switchPlatform(detectedPlatform);
initScrollAnimations();
initScrollAnimations();
initNoiseOverlay();
// Terminal demo - start when visible
const terminalEl = document.getElementById('terminal-content');
const cursorEl = document.getElementById('terminal-cursor');
if (terminalEl && cursorEl) {
const demo = new TerminalDemo(terminalEl, cursorEl);
const observer = new IntersectionObserver((entries) => {
entries.forEach(entry => {
if (entry.isIntersecting) {
demo.start();
} else {
demo.stop();
}
});
}, { threshold: 0.3 });
const terminalEl = document.getElementById("terminal-demo");
observer.observe(document.querySelector('.terminal-window'));
}
if (terminalEl) {
const demo = new TerminalDemo(terminalEl);
// Smooth nav background on scroll
const nav = document.querySelector('.nav');
let ticking = false;
window.addEventListener('scroll', () => {
if (!ticking) {
requestAnimationFrame(() => {
if (window.scrollY > 50) {
nav.style.borderBottomColor = 'rgba(255, 215, 0, 0.1)';
} else {
nav.style.borderBottomColor = '';
}
ticking = false;
});
ticking = true;
const observer = new IntersectionObserver(
(entries) => {
entries.forEach((entry) => {
if (entry.isIntersecting) {
demo.start();
} else {
demo.stop();
}
});
},
{ threshold: 0.3 },
);
observer.observe(document.querySelector(".terminal-window"));
}
const nav = document.querySelector(".nav");
let ticking = false;
window.addEventListener("scroll", () => {
if (!ticking) {
requestAnimationFrame(() => {
if (window.scrollY > 50) {
nav.style.borderBottomColor = "rgba(48, 80, 255, 0.15)";
} else {
nav.style.borderBottomColor = "";
}
});
ticking = false;
});
ticking = true;
}
});
});
+318 -376
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File diff suppressed because it is too large Load Diff
+5 -4
View File
@@ -42,10 +42,11 @@ from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Add mini-swe-agent to path if not installed
mini_swe_path = Path(__file__).parent / "mini-swe-agent" / "src"
if mini_swe_path.exists():
sys.path.insert(0, str(mini_swe_path))
# Add mini-swe-agent to path if not installed. In git worktrees the populated
# submodule may live in the main checkout rather than the worktree itself.
from minisweagent_path import ensure_minisweagent_on_path
ensure_minisweagent_on_path(Path(__file__).resolve().parent)
# ============================================================================
+92
View File
@@ -0,0 +1,92 @@
"""Helpers for locating the mini-swe-agent source tree.
Hermes often runs from git worktrees. In that layout the worktree root may have
an empty ``mini-swe-agent/`` placeholder while the real populated submodule
lives under the main checkout that owns the shared ``.git`` directory.
These helpers locate a usable ``mini-swe-agent/src`` directory and optionally
prepend it to ``sys.path`` so imports like ``import minisweagent`` work from
both normal checkouts and worktrees.
"""
from __future__ import annotations
import importlib.util
import sys
from pathlib import Path
from typing import Optional
def _read_gitdir(repo_root: Path) -> Optional[Path]:
"""Resolve the gitdir referenced by ``repo_root/.git`` when it is a file."""
git_marker = repo_root / ".git"
if not git_marker.is_file():
return None
try:
raw = git_marker.read_text(encoding="utf-8").strip()
except OSError:
return None
prefix = "gitdir:"
if not raw.lower().startswith(prefix):
return None
target = raw[len(prefix):].strip()
gitdir = Path(target)
if not gitdir.is_absolute():
gitdir = (repo_root / gitdir).resolve()
else:
gitdir = gitdir.resolve()
return gitdir
def discover_minisweagent_src(repo_root: Optional[Path] = None) -> Optional[Path]:
"""Return the best available ``mini-swe-agent/src`` path, if any.
Search order:
1. Current checkout/worktree root
2. Main checkout that owns the shared ``.git`` directory (for worktrees)
"""
repo_root = (repo_root or Path(__file__).resolve().parent).resolve()
candidates: list[Path] = [repo_root / "mini-swe-agent" / "src"]
gitdir = _read_gitdir(repo_root)
if gitdir is not None:
# Worktree layout: <main>/.git/worktrees/<name>
if len(gitdir.parents) >= 3 and gitdir.parent.name == "worktrees":
candidates.append(gitdir.parents[2] / "mini-swe-agent" / "src")
# Direct checkout with .git file pointing elsewhere
elif gitdir.name == ".git":
candidates.append(gitdir.parent / "mini-swe-agent" / "src")
seen = set()
for candidate in candidates:
candidate = candidate.resolve()
if candidate in seen:
continue
seen.add(candidate)
if candidate.exists() and candidate.is_dir():
return candidate
return None
def ensure_minisweagent_on_path(repo_root: Optional[Path] = None) -> Optional[Path]:
"""Ensure ``minisweagent`` is importable by prepending its src dir to sys.path.
Returns the inserted/discovered path, or ``None`` if the package is already
importable or no local source tree could be found.
"""
if importlib.util.find_spec("minisweagent") is not None:
return None
src = discover_minisweagent_src(repo_root)
if src is None:
return None
src_str = str(src)
if src_str not in sys.path:
sys.path.insert(0, src_str)
return src
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@@ -0,0 +1,162 @@
---
name: 1password
description: Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
version: 1.0.0
author: arceus77-7, enhanced by Hermes Agent
license: MIT
metadata:
hermes:
tags: [security, secrets, 1password, op, cli]
category: security
setup:
help: "Create a service account at https://my.1password.com → Settings → Service Accounts"
collect_secrets:
- env_var: OP_SERVICE_ACCOUNT_TOKEN
prompt: "1Password Service Account Token"
provider_url: "https://developer.1password.com/docs/service-accounts/"
secret: true
---
# 1Password CLI
Use this skill when the user wants secrets managed through 1Password instead of plaintext env vars or files.
## Requirements
- 1Password account
- 1Password CLI (`op`) installed
- One of: desktop app integration, service account token (`OP_SERVICE_ACCOUNT_TOKEN`), or Connect server
- `tmux` available for stable authenticated sessions during Hermes terminal calls (desktop app flow only)
## When to Use
- Install or configure 1Password CLI
- Sign in with `op signin`
- Read secret references like `op://Vault/Item/field`
- Inject secrets into config/templates using `op inject`
- Run commands with secret env vars via `op run`
## Authentication Methods
### Service Account (recommended for Hermes)
Set `OP_SERVICE_ACCOUNT_TOKEN` in `~/.hermes/.env` (the skill will prompt for this on first load).
No desktop app needed. Supports `op read`, `op inject`, `op run`.
```bash
export OP_SERVICE_ACCOUNT_TOKEN="your-token-here"
op whoami # verify — should show Type: SERVICE_ACCOUNT
```
### Desktop App Integration (interactive)
1. Enable in 1Password desktop app: Settings → Developer → Integrate with 1Password CLI
2. Ensure app is unlocked
3. Run `op signin` and approve the biometric prompt
### Connect Server (self-hosted)
```bash
export OP_CONNECT_HOST="http://localhost:8080"
export OP_CONNECT_TOKEN="your-connect-token"
```
## Setup
1. Install CLI:
```bash
# macOS
brew install 1password-cli
# Linux (official package/install docs)
# See references/get-started.md for distro-specific links.
# Windows (winget)
winget install AgileBits.1Password.CLI
```
2. Verify:
```bash
op --version
```
3. Choose an auth method above and configure it.
## Hermes Execution Pattern (desktop app flow)
Hermes terminal commands are non-interactive by default and can lose auth context between calls.
For reliable `op` use with desktop app integration, run sign-in and secret operations inside a dedicated tmux session.
Note: This is NOT needed when using `OP_SERVICE_ACCOUNT_TOKEN` — the token persists across terminal calls automatically.
```bash
SOCKET_DIR="${TMPDIR:-/tmp}/hermes-tmux-sockets"
mkdir -p "$SOCKET_DIR"
SOCKET="$SOCKET_DIR/hermes-op.sock"
SESSION="op-auth-$(date +%Y%m%d-%H%M%S)"
tmux -S "$SOCKET" new -d -s "$SESSION" -n shell
# Sign in (approve in desktop app when prompted)
tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "eval \"\$(op signin --account my.1password.com)\"" Enter
# Verify auth
tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "op whoami" Enter
# Example read
tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "op read 'op://Private/Npmjs/one-time password?attribute=otp'" Enter
# Capture output when needed
tmux -S "$SOCKET" capture-pane -p -J -t "$SESSION":0.0 -S -200
# Cleanup
tmux -S "$SOCKET" kill-session -t "$SESSION"
```
## Common Operations
### Read a secret
```bash
op read "op://app-prod/db/password"
```
### Get OTP
```bash
op read "op://app-prod/npm/one-time password?attribute=otp"
```
### Inject into template
```bash
echo "db_password: {{ op://app-prod/db/password }}" | op inject
```
### Run a command with secret env var
```bash
export DB_PASSWORD="op://app-prod/db/password"
op run -- sh -c '[ -n "$DB_PASSWORD" ] && echo "DB_PASSWORD is set" || echo "DB_PASSWORD missing"'
```
## Guardrails
- Never print raw secrets back to user unless they explicitly request the value.
- Prefer `op run` / `op inject` instead of writing secrets into files.
- If command fails with "account is not signed in", run `op signin` again in the same tmux session.
- If desktop app integration is unavailable (headless/CI), use service account token flow.
## CI / Headless note
For non-interactive use, authenticate with `OP_SERVICE_ACCOUNT_TOKEN` and avoid interactive `op signin`.
Service accounts require CLI v2.18.0+.
## References
- `references/get-started.md`
- `references/cli-examples.md`
- https://developer.1password.com/docs/cli/
- https://developer.1password.com/docs/service-accounts/
@@ -0,0 +1,31 @@
# op CLI examples
## Sign-in and identity
```bash
op signin
op signin --account my.1password.com
op whoami
op account list
```
## Read secrets
```bash
op read "op://app-prod/db/password"
op read "op://app-prod/npm/one-time password?attribute=otp"
```
## Inject secrets
```bash
echo "api_key: {{ op://app-prod/openai/api key }}" | op inject
op inject -i config.tpl.yml -o config.yml
```
## Run command with secrets
```bash
export DB_PASSWORD="op://app-prod/db/password"
op run -- sh -c '[ -n "$DB_PASSWORD" ] && echo "DB_PASSWORD is set"'
```
@@ -0,0 +1,21 @@
# 1Password CLI get-started (summary)
Official docs: https://developer.1password.com/docs/cli/get-started/
## Core flow
1. Install `op` CLI.
2. Enable desktop app integration in 1Password app.
3. Unlock app.
4. Run `op signin` and approve prompt.
5. Verify with `op whoami`.
## Multiple accounts
- Use `op signin --account <subdomain.1password.com>`
- Or set `OP_ACCOUNT`
## Non-interactive / automation
- Use service accounts and `OP_SERVICE_ACCOUNT_TOKEN`
- Prefer `op run` and `op inject` for runtime secret handling
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@@ -0,0 +1,3 @@
# Security
Skills for secrets management, credential handling, and security tooling integrations.
+1
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@@ -30,6 +30,7 @@ dependencies = [
"fal-client",
# Text-to-speech (Edge TTS is free, no API key needed)
"edge-tts",
"faster-whisper>=1.0.0",
# mini-swe-agent deps (terminal tool)
"litellm>=1.75.5",
"typer",
+41 -8
View File
@@ -202,6 +202,32 @@ _NEVER_PARALLEL_TOOLS = frozenset({"clarify"})
_MAX_TOOL_WORKERS = 8
def _inject_honcho_turn_context(content, turn_context: str):
"""Append Honcho recall to the current-turn user message without mutating history.
The returned content is sent to the API for this turn only. Keeping Honcho
recall out of the system prompt preserves the stable cache prefix while
still giving the model continuity context.
"""
if not turn_context:
return content
note = (
"[System note: The following Honcho memory was retrieved from prior "
"sessions. It is continuity context for this turn only, not new user "
"input.]\n\n"
f"{turn_context}"
)
if isinstance(content, list):
return list(content) + [{"type": "text", "text": note}]
text = "" if content is None else str(content)
if not text.strip():
return note
return f"{text}\n\n{note}"
class AIAgent:
"""
AI Agent with tool calling capabilities.
@@ -382,7 +408,7 @@ class AIAgent:
# Persistent error log -- always writes WARNING+ to ~/.hermes/logs/errors.log
# so tool failures, API errors, etc. are inspectable after the fact.
from agent.redact import RedactingFormatter
_error_log_dir = Path.home() / ".hermes" / "logs"
_error_log_dir = _hermes_home / "logs"
_error_log_dir.mkdir(parents=True, exist_ok=True)
_error_log_path = _error_log_dir / "errors.log"
from logging.handlers import RotatingFileHandler
@@ -2729,7 +2755,7 @@ class AIAgent:
"model": self.model,
"messages": api_messages,
"tools": self.tools if self.tools else None,
"timeout": 900.0,
"timeout": float(os.getenv("HERMES_API_TIMEOUT", 900.0)),
}
if self.max_tokens is not None:
@@ -3909,10 +3935,11 @@ class AIAgent:
# Honcho prefetch consumption:
# - First turn: bake into cached system prompt (stable for the session).
# - Later turns: inject as ephemeral system context for this API call only.
# - Later turns: attach recall to the current-turn user message at
# API-call time only (never persisted to history / session DB).
#
# This keeps the persisted/cached prompt stable while still allowing
# turn N to consume background prefetch results from turn N-1.
# This keeps the system-prefix cache stable while still allowing turn N
# to consume background prefetch results from turn N-1.
self._honcho_context = ""
self._honcho_turn_context = ""
_recall_mode = (self._honcho_config.recall_mode if self._honcho_config else "hybrid")
@@ -3930,6 +3957,7 @@ class AIAgent:
# Add user message
user_msg = {"role": "user", "content": user_message}
messages.append(user_msg)
current_turn_user_idx = len(messages) - 1
if not self.quiet_mode:
print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'")
@@ -4079,9 +4107,14 @@ class AIAgent:
# However, providers like Moonshot AI require a separate 'reasoning_content' field
# on assistant messages with tool_calls. We handle both cases here.
api_messages = []
for msg in messages:
for idx, msg in enumerate(messages):
api_msg = msg.copy()
if idx == current_turn_user_idx and msg.get("role") == "user" and self._honcho_turn_context:
api_msg["content"] = _inject_honcho_turn_context(
api_msg.get("content", ""), self._honcho_turn_context
)
# For ALL assistant messages, pass reasoning back to the API
# This ensures multi-turn reasoning context is preserved
if msg.get("role") == "assistant":
@@ -4109,11 +4142,11 @@ class AIAgent:
# Build the final system message: cached prompt + ephemeral system prompt.
# Ephemeral additions are API-call-time only (not persisted to session DB).
# Honcho later-turn recall is intentionally kept OUT of the system prompt
# so the stable cache prefix remains unchanged.
effective_system = active_system_prompt or ""
if self.ephemeral_system_prompt:
effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip()
if self._honcho_turn_context:
effective_system = (effective_system + "\n\n" + self._honcho_turn_context).strip()
if effective_system:
api_messages = [{"role": "system", "content": effective_system}] + api_messages
@@ -0,0 +1,218 @@
---
name: opencode
description: Delegate coding tasks to OpenCode CLI agent for feature implementation, refactoring, PR review, and long-running autonomous sessions. Requires the opencode CLI installed and authenticated.
version: 1.2.0
author: Hermes Agent
license: MIT
metadata:
hermes:
tags: [Coding-Agent, OpenCode, Autonomous, Refactoring, Code-Review]
related_skills: [claude-code, codex, hermes-agent]
---
# OpenCode CLI
Use [OpenCode](https://opencode.ai) as an autonomous coding worker orchestrated by Hermes terminal/process tools. OpenCode is a provider-agnostic, open-source AI coding agent with a TUI and CLI.
## When to Use
- User explicitly asks to use OpenCode
- You want an external coding agent to implement/refactor/review code
- You need long-running coding sessions with progress checks
- You want parallel task execution in isolated workdirs/worktrees
## Prerequisites
- OpenCode installed: `npm i -g opencode-ai@latest` or `brew install anomalyco/tap/opencode`
- Auth configured: `opencode auth login` or set provider env vars (OPENROUTER_API_KEY, etc.)
- Verify: `opencode auth list` should show at least one provider
- Git repository for code tasks (recommended)
- `pty=true` for interactive TUI sessions
## Binary Resolution (Important)
Shell environments may resolve different OpenCode binaries. If behavior differs between your terminal and Hermes, check:
```
terminal(command="which -a opencode")
terminal(command="opencode --version")
```
If needed, pin an explicit binary path:
```
terminal(command="$HOME/.opencode/bin/opencode run '...'", workdir="~/project", pty=true)
```
## One-Shot Tasks
Use `opencode run` for bounded, non-interactive tasks:
```
terminal(command="opencode run 'Add retry logic to API calls and update tests'", workdir="~/project")
```
Attach context files with `-f`:
```
terminal(command="opencode run 'Review this config for security issues' -f config.yaml -f .env.example", workdir="~/project")
```
Show model thinking with `--thinking`:
```
terminal(command="opencode run 'Debug why tests fail in CI' --thinking", workdir="~/project")
```
Force a specific model:
```
terminal(command="opencode run 'Refactor auth module' --model openrouter/anthropic/claude-sonnet-4", workdir="~/project")
```
## Interactive Sessions (Background)
For iterative work requiring multiple exchanges, start the TUI in background:
```
terminal(command="opencode", workdir="~/project", background=true, pty=true)
# Returns session_id
# Send a prompt
process(action="submit", session_id="<id>", data="Implement OAuth refresh flow and add tests")
# Monitor progress
process(action="poll", session_id="<id>")
process(action="log", session_id="<id>")
# Send follow-up input
process(action="submit", session_id="<id>", data="Now add error handling for token expiry")
# Exit cleanly — Ctrl+C
process(action="write", session_id="<id>", data="\x03")
# Or just kill the process
process(action="kill", session_id="<id>")
```
**Important:** Do NOT use `/exit` — it is not a valid OpenCode command and will open an agent selector dialog instead. Use Ctrl+C (`\x03`) or `process(action="kill")` to exit.
### TUI Keybindings
| Key | Action |
|-----|--------|
| `Enter` | Submit message (press twice if needed) |
| `Tab` | Switch between agents (build/plan) |
| `Ctrl+P` | Open command palette |
| `Ctrl+X L` | Switch session |
| `Ctrl+X M` | Switch model |
| `Ctrl+X N` | New session |
| `Ctrl+X E` | Open editor |
| `Ctrl+C` | Exit OpenCode |
### Resuming Sessions
After exiting, OpenCode prints a session ID. Resume with:
```
terminal(command="opencode -c", workdir="~/project", background=true, pty=true) # Continue last session
terminal(command="opencode -s ses_abc123", workdir="~/project", background=true, pty=true) # Specific session
```
## Common Flags
| Flag | Use |
|------|-----|
| `run 'prompt'` | One-shot execution and exit |
| `--continue` / `-c` | Continue the last OpenCode session |
| `--session <id>` / `-s` | Continue a specific session |
| `--agent <name>` | Choose OpenCode agent (build or plan) |
| `--model provider/model` | Force specific model |
| `--format json` | Machine-readable output/events |
| `--file <path>` / `-f` | Attach file(s) to the message |
| `--thinking` | Show model thinking blocks |
| `--variant <level>` | Reasoning effort (high, max, minimal) |
| `--title <name>` | Name the session |
| `--attach <url>` | Connect to a running opencode server |
## Procedure
1. Verify tool readiness:
- `terminal(command="opencode --version")`
- `terminal(command="opencode auth list")`
2. For bounded tasks, use `opencode run '...'` (no pty needed).
3. For iterative tasks, start `opencode` with `background=true, pty=true`.
4. Monitor long tasks with `process(action="poll"|"log")`.
5. If OpenCode asks for input, respond via `process(action="submit", ...)`.
6. Exit with `process(action="write", data="\x03")` or `process(action="kill")`.
7. Summarize file changes, test results, and next steps back to user.
## PR Review Workflow
OpenCode has a built-in PR command:
```
terminal(command="opencode pr 42", workdir="~/project", pty=true)
```
Or review in a temporary clone for isolation:
```
terminal(command="REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && opencode run 'Review this PR vs main. Report bugs, security risks, test gaps, and style issues.' -f $(git diff origin/main --name-only | head -20 | tr '\n' ' ')", pty=true)
```
## Parallel Work Pattern
Use separate workdirs/worktrees to avoid collisions:
```
terminal(command="opencode run 'Fix issue #101 and commit'", workdir="/tmp/issue-101", background=true, pty=true)
terminal(command="opencode run 'Add parser regression tests and commit'", workdir="/tmp/issue-102", background=true, pty=true)
process(action="list")
```
## Session & Cost Management
List past sessions:
```
terminal(command="opencode session list")
```
Check token usage and costs:
```
terminal(command="opencode stats")
terminal(command="opencode stats --days 7 --models anthropic/claude-sonnet-4")
```
## Pitfalls
- Interactive `opencode` (TUI) sessions require `pty=true`. The `opencode run` command does NOT need pty.
- `/exit` is NOT a valid command — it opens an agent selector. Use Ctrl+C to exit the TUI.
- PATH mismatch can select the wrong OpenCode binary/model config.
- If OpenCode appears stuck, inspect logs before killing:
- `process(action="log", session_id="<id>")`
- Avoid sharing one working directory across parallel OpenCode sessions.
- Enter may need to be pressed twice to submit in the TUI (once to finalize text, once to send).
## Verification
Smoke test:
```
terminal(command="opencode run 'Respond with exactly: OPENCODE_SMOKE_OK'")
```
Success criteria:
- Output includes `OPENCODE_SMOKE_OK`
- Command exits without provider/model errors
- For code tasks: expected files changed and tests pass
## Rules
1. Prefer `opencode run` for one-shot automation — it's simpler and doesn't need pty.
2. Use interactive background mode only when iteration is needed.
3. Always scope OpenCode sessions to a single repo/workdir.
4. For long tasks, provide progress updates from `process` logs.
5. Report concrete outcomes (files changed, tests, remaining risks).
6. Exit interactive sessions with Ctrl+C or kill, never `/exit`.
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@@ -0,0 +1,249 @@
# ☤ ASCII Video
Renders any content as colored ASCII character video. Audio, video, images, text, or pure math in, MP4/GIF/PNG sequence out. Full RGB color per character cell, 1080p 24fps default. No GPU.
Built for [Hermes Agent](https://github.com/NousResearch/hermes-agent). Usable in any coding agent.
## What this is
A skill that teaches an agent how to build single-file Python renderers for ASCII video from scratch. The agent gets the full pipeline: grid system, font rasterization, effect library, shader chain, audio analysis, parallel encoding. It writes the renderer, runs it, gets video.
The output is actual video. Not terminal escape codes. Frames are computed as grids of colored characters, composited onto pixel canvases with pre-rasterized font bitmaps, post-processed through shaders, piped to ffmpeg.
## Modes
| Mode | Input | Output |
|------|-------|--------|
| Video-to-ASCII | A video file | ASCII recreation of the footage |
| Audio-reactive | An audio file | Visuals driven by frequency bands, beats, energy |
| Generative | Nothing | Procedural animation from math |
| Hybrid | Video + audio | ASCII video with audio-reactive overlays |
| Lyrics/text | Audio + timed text (SRT) | Karaoke-style text with effects |
| TTS narration | Text quotes + API key | Narrated video with typewriter text and generated speech |
## Pipeline
Every mode follows the same 6-stage path:
```
INPUT --> ANALYZE --> SCENE_FN --> TONEMAP --> SHADE --> ENCODE
```
1. **Input** loads source material (or nothing for generative).
2. **Analyze** extracts per-frame features. Audio gets 6-band FFT, RMS, spectral centroid, flatness, flux, beat detection with exponential decay. Video gets luminance, edges, motion.
3. **Scene function** returns a pixel canvas directly. Composes multiple character grids at different densities, value/hue fields, pixel blend modes. This is where the visuals happen.
4. **Tonemap** does adaptive percentile-based brightness normalization with per-scene gamma. ASCII on black is inherently dark. Linear multipliers don't work. This does.
5. **Shade** runs a `ShaderChain` (38 composable shaders) plus a `FeedbackBuffer` for temporal recursion with spatial transforms.
6. **Encode** pipes raw RGB frames to ffmpeg for H.264 encoding. Segments concatenated, audio muxed.
## Grid system
Characters render on fixed-size grids. Layer multiple densities for depth.
| Size | Font | Grid at 1080p | Use |
|------|------|---------------|-----|
| xs | 8px | 400x108 | Ultra-dense data fields |
| sm | 10px | 320x83 | Rain, starfields |
| md | 16px | 192x56 | Default balanced |
| lg | 20px | 160x45 | Readable text |
| xl | 24px | 137x37 | Large titles |
| xxl | 40px | 80x22 | Giant minimal |
Rendering the same scene on `sm` and `lg` then screen-blending them creates natural texture interference. Fine detail shows through gaps in coarse characters. Most scenes use two or three grids.
## Character palettes (20+)
Each sorted dark-to-bright, each a different visual texture. Validated against the font at init so broken glyphs get dropped silently.
| Family | Examples | Feel |
|--------|----------|------|
| Density ramps | ` .:-=+#@█` | Classic ASCII art gradient |
| Block elements | ` ░▒▓█▄▀▐▌` | Chunky, digital |
| Braille | ` ⠁⠂⠃...⠿` | Fine-grained pointillism |
| Dots | ` ⋅∘∙●◉◎` | Smooth, organic |
| Stars | ` ·✧✦✩✨★✶` | Sparkle, celestial |
| Half-fills | ` ◔◑◕◐◒◓◖◗◙` | Directional fill progression |
| Crosshatch | ` ▣▤▥▦▧▨▩` | Hatched density ramp |
| Math | ` ·∘∙•°±×÷≈≠≡∞∫∑Ω` | Scientific, abstract |
| Box drawing | ` ─│┌┐└┘├┤┬┴┼` | Structural, circuit-like |
| Katakana | ` ·ヲァィゥェォャュ...` | Matrix rain |
| Greek | ` αβγδεζηθ...ω` | Classical, academic |
| Runes | ` ᚠᚢᚦᚱᚷᛁᛇᛒᛖᛚᛞᛟ` | Mystical, ancient |
| Alchemical | ` ☉☽♀♂♃♄♅♆♇` | Esoteric |
| Arrows | ` ←↑→↓↔↕↖↗↘↙` | Directional, kinetic |
| Music | ` ♪♫♬♩♭♮♯○●` | Musical |
| Project-specific | ` .·~=≈∞⚡☿✦★⊕◊◆▲▼●■` | Themed per project |
Custom palettes are built per project to match the content.
## Color strategies
| Strategy | How it maps hue | Good for |
|----------|----------------|----------|
| Angle-mapped | Position angle from center | Rainbow radial effects |
| Distance-mapped | Distance from center | Depth, tunnels |
| Frequency-mapped | Audio spectral centroid | Timbral shifting |
| Value-mapped | Brightness level | Heat maps, fire |
| Time-cycled | Slow rotation over time | Ambient, chill |
| Source-sampled | Original video pixel colors | Video-to-ASCII |
| Palette-indexed | Discrete lookup table | Retro, flat graphic |
| Temperature | Warm-to-cool blend | Emotional tone |
| Complementary | Hue + opposite | Bold, dramatic |
| Triadic | Three equidistant hues | Psychedelic, vibrant |
| Analogous | Neighboring hues | Harmonious, subtle |
| Monochrome | Fixed hue, vary S/V | Noir, focused |
Plus 10 discrete RGB palettes (neon, pastel, cyberpunk, vaporwave, earth, ice, blood, forest, mono-green, mono-amber).
## Effects
### Backgrounds
| Effect | Description | Parameters |
|--------|-------------|------------|
| Sine field | Layered sinusoidal interference | freq, speed, octave count |
| Smooth noise | Multi-octave Perlin approximation | octaves, scale |
| Cellular | Voronoi-like moving cells | n_centers, speed |
| Noise/static | Random per-cell flicker | density |
| Video source | Downsampled video frame | brightness |
### Primary effects
| Effect | Description |
|--------|-------------|
| Concentric rings | Bass-driven pulsing rings with wobble |
| Radial rays | Spoke pattern, beat-triggered |
| Spiral arms | Logarithmic spiral, configurable arm count/tightness |
| Tunnel | Infinite depth perspective |
| Vortex | Twisting radial distortion |
| Frequency waves | Per-band sine waves at different heights |
| Interference | Overlapping sine waves creating moire |
| Aurora | Horizontal flowing bands |
| Ripple | Point-source concentric waves |
| Fire columns | Rising flames with heat-color gradient |
| Spectrum bars | Mirrored frequency visualizer |
| Waveform | Oscilloscope-style trace |
### Particle systems
| Type | Behavior | Character sets |
|------|----------|---------------|
| Explosion | Beat-triggered radial burst | `*+#@⚡✦★█▓` |
| Sparks | Short-lived bright dots | `·•●★✶*+` |
| Embers | Rising from bottom with drift | `·•●★` |
| Snow | Falling with wind sway | `❄❅❆·•*○` |
| Rain | Fast vertical streaks | `│┃║/\` |
| Bubbles | Rising, expanding | `○◎◉●∘∙°` |
| Data | Falling hex/binary | `01{}[]<>/\` |
| Runes | Mystical floating symbols | `ᚠᚢᚦᚱᚷᛁ✦★` |
| Orbit | Circular/elliptical paths | `·•●` |
| Gravity well | Attracted to point sources | configurable |
| Dissolve | Spread across screen, fade | configurable |
| Starfield | 3D projected, approaching | configurable |
## Shader pipeline
38 composable shaders, applied to the pixel canvas after character rendering. Configurable per section.
| Category | Shaders |
|----------|---------|
| Geometry | CRT barrel, pixelate, wave distort, displacement map, kaleidoscope, mirror (h/v/quad/diag) |
| Channel | Chromatic aberration (beat-reactive), channel shift, channel swap, RGB split radial |
| Color | Invert, posterize, threshold, solarize, hue rotate, saturation, color grade, color wobble, color ramp |
| Glow/Blur | Bloom, edge glow, soft focus, radial blur |
| Noise | Film grain (beat-reactive), static noise |
| Lines/Patterns | Scanlines, halftone |
| Tone | Vignette, contrast, gamma, levels, brightness |
| Glitch/Data | Glitch bands (beat-reactive), block glitch, pixel sort, data bend |
12 color tint presets: warm, cool, matrix green, amber, sepia, neon pink, ice, blood, forest, void, sunset, neutral.
7 mood presets for common shader combos:
| Mood | Shaders |
|------|---------|
| Retro terminal | CRT + scanlines + grain + amber/green tint |
| Clean modern | Light bloom + subtle vignette |
| Glitch art | Heavy chromatic + glitch bands + color wobble |
| Cinematic | Bloom + vignette + grain + color grade |
| Dreamy | Heavy bloom + soft focus + color wobble |
| Harsh/industrial | High contrast + grain + scanlines, no bloom |
| Psychedelic | Color wobble + chromatic + kaleidoscope mirror |
## Blend modes and composition
20 pixel blend modes for layering canvases: normal, add, subtract, multiply, screen, overlay, softlight, hardlight, difference, exclusion, colordodge, colorburn, linearlight, vividlight, pin_light, hard_mix, lighten, darken, grain_extract, grain_merge.
Mirror modes: horizontal, vertical, quad, diagonal, kaleidoscope (6-fold radial). Beat-triggered.
Transitions: crossfade, directional wipe, radial wipe, dissolve, glitch cut.
## Hardware adaptation
Auto-detects CPU count, RAM, platform, ffmpeg. Adapts worker count, resolution, FPS.
| Profile | Resolution | FPS | When |
|---------|-----------|-----|------|
| `draft` | 960x540 | 12 | Check timing/layout |
| `preview` | 1280x720 | 15 | Review effects |
| `production` | 1920x1080 | 24 | Final output |
| `max` | 3840x2160 | 30 | Ultra-high |
| `auto` | Detected | 24 | Adapts to hardware + duration |
`auto` estimates render time and downgrades if it would take over an hour. Low-memory systems drop to 720p automatically.
### Render times (1080p 24fps, ~180ms/frame/worker)
| Duration | 4 workers | 8 workers | 16 workers |
|----------|-----------|-----------|------------|
| 30s | ~3 min | ~2 min | ~1 min |
| 2 min | ~13 min | ~7 min | ~4 min |
| 5 min | ~33 min | ~17 min | ~9 min |
| 10 min | ~65 min | ~33 min | ~17 min |
720p roughly halves these. 4K roughly quadruples them.
## Known pitfalls
**Brightness.** ASCII characters are small bright dots on black. Most frame pixels are background. Linear `* N` multipliers clip highlights and wash out. Use `tonemap()` with per-scene gamma instead. Default gamma 0.75, solarize scenes 0.55, posterize 0.50.
**Render bottleneck.** The per-cell Python loop compositing font bitmaps runs at ~100-150ms/frame. Unavoidable without Cython/C. Everything else must be vectorized numpy. Python for-loops over rows/cols in effect functions will tank performance.
**ffmpeg deadlock.** Never `stderr=subprocess.PIPE` on long-running encodes. Buffer fills at ~64KB, process hangs. Redirect stderr to a file.
**Font cell height.** Pillow's `textbbox()` returns wrong height on macOS. Use `font.getmetrics()` for `ascent + descent`.
**Font compatibility.** Not all Unicode renders in all fonts. Palettes validated at init, blank glyphs silently removed.
## Requirements
◆ Python 3.10+
◆ NumPy, Pillow, SciPy (audio modes)
◆ ffmpeg on PATH
◆ A monospace font (Menlo, Courier, Monaco, auto-detected)
◆ Optional: OpenCV, ElevenLabs API key (TTS mode)
## File structure
```
├── SKILL.md # Modes, workflow, creative direction
├── README.md # This file
└── references/
├── architecture.md # Grid system, fonts, palettes, color, _render_vf()
├── effects.md # Value fields, hue fields, backgrounds, particles
├── shaders.md # 38 shaders, ShaderChain, tint presets, transitions
├── composition.md # Blend modes, multi-grid, tonemap, FeedbackBuffer
├── scenes.md # Scene protocol, SCENES table, render_clip(), examples
├── design-patterns.md # Layer hierarchy, directional arcs, scene concepts
├── inputs.md # Audio analysis, video sampling, text, TTS
├── optimization.md # Hardware detection, vectorized patterns, parallelism
└── troubleshooting.md # Broadcasting traps, blend pitfalls, diagnostics
```
## Projects built with this
✦ 85-second highlight reel. 15 scenes (14×5s + 15s crescendo finale), randomized order, directional parameter arcs, layer hierarchy composition. Showcases the full effect vocabulary: fBM, voronoi fragmentation, reaction-diffusion, cellular automata, dual counter-rotating spirals, wave collision, domain warping, tunnel descent, kaleidoscope symmetry, boid flocking, fire simulation, glitch corruption, and a 7-layer crescendo buildup.
✦ Audio-reactive music visualizer. 3.5 min, 8 sections with distinct effects, beat-triggered particles and glitch, cycling palettes.
✦ TTS narrated testimonial video. 23 quotes, per-quote ElevenLabs voices, background music at 15% wide stereo, per-clip re-rendering for iterative editing.
+16 -10
View File
@@ -59,16 +59,20 @@ Every mode follows the same 6-stage pipeline. See `references/architecture.md` f
| Dimension | Options | Reference |
|-----------|---------|-----------|
| **Character palette** | Density ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), dots, project-specific | `architecture.md` § Character Palettes |
| **Color strategy** | HSV (angle/distance/time/value mapped), discrete RGB palettes, monochrome, complementary, triadic, temperature | `architecture.md` § Color System |
| **Color strategy** | HSV (angle/distance/time/value mapped), OKLAB/OKLCH (perceptually uniform), discrete RGB palettes, auto-generated harmony (complementary/triadic/analogous/tetradic), monochrome, temperature | `architecture.md` § Color System |
| **Color tint** | Warm, cool, amber, matrix green, neon pink, sepia, ice, blood, void, sunset | `shaders.md` § Color Grade |
| **Background texture** | Sine fields, noise, smooth noise, cellular/voronoi, video source | `effects.md` § Background Fills |
| **Primary effects** | Rings, spirals, tunnel, vortex, waves, interference, aurora, ripple, fire | `effects.md` § Radial / Wave / Fire |
| **Particles** | Energy sparks, snow, rain, bubbles, runes, binary data, orbits, gravity wells | `effects.md` § Particle Systems |
| **Background texture** | Sine fields, fBM noise, domain warp, voronoi cells, reaction-diffusion, cellular automata, video source | `effects.md` § Background Fills, Noise-Based Fields, Simulation-Based Fields |
| **Primary effects** | Rings, spirals, tunnel, vortex, waves, interference, aurora, ripple, fire, strange attractors, SDFs (geometric shapes with smooth booleans) | `effects.md` § Radial / Wave / Fire / SDF-Based Fields |
| **Particles** | Energy sparks, snow, rain, bubbles, runes, binary data, orbits, gravity wells, flocking boids, flow-field followers, trail-drawing particles | `effects.md` § Particle Systems |
| **Shader mood** | Retro CRT, clean modern, glitch art, cinematic, dreamy, harsh industrial, psychedelic | `shaders.md` § Design Philosophy |
| **Grid density** | xs(8px) through xxl(40px), mixed per layer | `architecture.md` § Grid System |
| **Font** | Menlo, Monaco, Courier, SF Mono, JetBrains Mono, Fira Code, IBM Plex | `architecture.md` § Font Selection |
| **Coordinate space** | Cartesian, polar, tiled, rotated, skewed, fisheye, twisted, Möbius, domain-warped | `effects.md` § Coordinate Transforms |
| **Mirror mode** | None, horizontal, vertical, quad, diagonal, kaleidoscope | `shaders.md` § Mirror Effects |
| **Transition style** | Crossfade, wipe (directional/radial), dissolve, glitch cut | `shaders.md` § Transitions |
| **Masking** | Circle, rect, ring, gradient, text stencil, value-field-as-mask, animated iris/wipe/dissolve | `composition.md` § Masking |
| **Temporal motion** | Static, audio-reactive, eased keyframes, morphing between fields, temporal noise (smooth in-place evolution) | `effects.md` § Temporal Coherence |
| **Transition style** | Crossfade, wipe (directional/radial), dissolve, glitch cut, iris open/close, mask-based reveal | `shaders.md` § Transitions, `composition.md` § Animated Masks |
| **Aspect ratio** | Landscape (16:9), portrait (9:16), square (1:1), ultrawide (21:9) | `architecture.md` § Resolution Presets |
### Per-Section Variation
@@ -95,10 +99,11 @@ Establish with user:
- **Input source** — file path, format, duration
- **Mode** — which of the 6 modes above
- **Sections** — time-mapped style changes (timestamps → effect names)
- **Resolution**default 1920x1080 @ 24fps; GIFs typically 640x360 @ 15fps
- **Resolution**landscape 1920x1080 (default), portrait 1080x1920, square 1080x1080 @ 24fps; GIFs typically 640x360 @ 15fps
- **Style direction** — dense/sparse, bright/dark, chaotic/minimal, color palette
- **Text/branding** — easter eggs, overlays, credits, themed character sets
- **Output format** — MP4 (default), GIF, PNG sequence
- **Aspect ratio** — landscape (16:9), portrait (9:16 for TikTok/Reels/Stories), square (1:1 for IG feed)
### Step 2: Detect Hardware and Set Quality
@@ -240,11 +245,12 @@ Image.fromarray(canvas).save("test.png")
| File | Contents |
|------|----------|
| `references/architecture.md` | Grid system, font selection, character palettes (library of 20+), color system (HSV + discrete RGB), `_render_vf()` helper, compositing, v2 effect function contract |
| `references/architecture.md` | Grid system (landscape/portrait/square resolution presets), font selection, character palettes (library of 20+), color system (HSV + OKLAB/OKLCH + discrete RGB + color harmony generation + perceptual gradient interpolation), `_render_vf()` helper, compositing, v2 effect function contract |
| `references/inputs.md` | All input sources: audio analysis, video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing) |
| `references/effects.md` | Effect building blocks: 12 value field generators (`vf_sinefield` through `vf_noise_static`), 8 hue field generators (`hf_fixed` through `hf_plasma`), radial/wave/fire effects, particles, composing guide |
| `references/effects.md` | Effect building blocks: 20+ value field generators (trig, noise/fBM, domain warp, voronoi, reaction-diffusion, cellular automata, strange attractors, SDFs), 8 hue field generators, coordinate transforms (rotate/tile/polar/Möbius), temporal coherence (easing, keyframes, morphing), radial/wave/fire effects, advanced particles (flocking, flow fields, trails), composing guide |
| `references/shaders.md` | 38 shader implementations (geometry, channel, color, glow, noise, pattern, tone, glitch, mirror), `ShaderChain` class, full `_apply_shader_step()` dispatch, audio-reactive scaling, transitions, tint presets |
| `references/composition.md` | **v2 core**: pixel blend modes (20 modes with implementations), multi-grid composition, `_render_vf()` helper, adaptive `tonemap()`, per-scene gamma, `FeedbackBuffer` with spatial transforms, `PixelBlendStack` |
| `references/scenes.md` | **v2 scene protocol**: scene function contract, `Renderer` class, `SCENES` table structure, `render_clip()` loop, beat-synced cutting, parallel rendering + pickling constraints, 4 complete scene examples, scene design checklist |
| `references/composition.md` | **v2 core**: pixel blend modes (20 modes with implementations), multi-grid composition, `_render_vf()` helper, adaptive `tonemap()`, per-scene gamma, `FeedbackBuffer` with spatial transforms, `PixelBlendStack`, masking/stencil system (shape masks, text stencils, animated masks, boolean ops) |
| `references/scenes.md` | **v2 scene protocol**: scene function contract (local time convention), `Renderer` class, `SCENES` table structure, `render_clip()` loop, beat-synced cutting, parallel rendering + pickling constraints, 4 complete scene examples, scene design checklist |
| `references/design-patterns.md` | **Scene composition patterns**: layer hierarchy (bg/content/accent), directional parameter arcs vs oscillation, scene concepts and visual metaphors, counter-rotating dual systems, wave collision, progressive fragmentation, entropy/consumption, staggered layer entry (crescendo), scene ordering |
| `references/troubleshooting.md` | NumPy broadcasting traps, blend mode pitfalls, multiprocessing/pickling issues, brightness diagnostics, ffmpeg deadlocks, font issues, performance bottlenecks, common mistakes |
| `references/optimization.md` | Hardware detection, adaptive quality profiles (draft/preview/production/max), CLI integration, vectorized effect patterns, parallel rendering, memory management |
@@ -1,12 +1,43 @@
# Architecture Reference
**Cross-references:**
- Effect building blocks (value fields, noise, SDFs, particles): `effects.md`
- `_render_vf()`, blend modes, tonemap, masking: `composition.md`
- Scene protocol, render_clip, SCENES table: `scenes.md`
- Shader pipeline, feedback buffer, output encoding: `shaders.md`
- Complete scene examples: `examples.md`
- Input sources (audio analysis, video, TTS): `inputs.md`
- Performance tuning, hardware detection: `optimization.md`
- Common bugs (broadcasting, font, encoding): `troubleshooting.md`
## Grid System
### Resolution Presets
```python
RESOLUTION_PRESETS = {
"landscape": (1920, 1080), # 16:9 — YouTube, default
"portrait": (1080, 1920), # 9:16 — TikTok, Reels, Stories
"square": (1080, 1080), # 1:1 — Instagram feed
"ultrawide": (2560, 1080), # 21:9 — cinematic
"landscape4k":(3840, 2160), # 16:9 — 4K
"portrait4k": (2160, 3840), # 9:16 — 4K portrait
}
def get_resolution(preset="landscape", custom=None):
"""Returns (VW, VH) tuple."""
if custom:
return custom
return RESOLUTION_PRESETS.get(preset, RESOLUTION_PRESETS["landscape"])
```
### Multi-Density Grids
Pre-initialize multiple grid sizes. Switch per section for visual variety.
Pre-initialize multiple grid sizes. Switch per section for visual variety. Grid dimensions auto-compute from resolution:
| Key | Font Size | Grid (1920x1080) | Use |
**Landscape (1920x1080):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| xs | 8 | 400x108 | Ultra-dense data fields |
| sm | 10 | 320x83 | Dense detail, rain, starfields |
@@ -15,7 +46,34 @@ Pre-initialize multiple grid sizes. Switch per section for visual variety.
| xl | 24 | 137x37 | Short quotes, large titles |
| xxl | 40 | 80x22 | Giant text, minimal |
**Grid sizing for text-heavy content**: When displaying readable text (quotes, lyrics, testimonials), use 20px (`lg`) as the primary grid. This gives 160 columns -- plenty for lines up to ~50 chars centered. For very short quotes (< 60 chars, <= 3 lines), 24px (`xl`) makes them more impactful. Only init the grids you actually use -- each grid pre-rasterizes all characters which costs ~0.3-0.5s.
**Portrait (1080x1920):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| xs | 8 | 225x192 | Ultra-dense, tall data columns |
| sm | 10 | 180x148 | Dense detail, vertical rain |
| md | 16 | 112x100 | Default balanced |
| lg | 20 | 90x80 | Readable text (~30 chars/line centered) |
| xl | 24 | 75x66 | Short quotes, stacked |
| xxl | 40 | 45x39 | Giant text, minimal |
**Square (1080x1080):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| sm | 10 | 180x83 | Dense detail |
| md | 16 | 112x56 | Default balanced |
| lg | 20 | 90x45 | Readable text |
**Key differences in portrait mode:**
- Fewer columns (90 at `lg` vs 160) — lines must be shorter or wrap
- Many more rows (80 at `lg` vs 45) — vertical stacking is natural
- Aspect ratio correction flips: `asp = cw / ch` still works but the visual emphasis is vertical
- Radial effects appear as tall ellipses unless corrected
- Vertical effects (rain, embers, fire columns) are naturally enhanced
- Horizontal effects (spectrum bars, waveforms) need rotation or compression
**Grid sizing for text in portrait**: Use `lg` (20px) for 2-3 word lines. Max comfortable line length is ~25-30 chars. For longer quotes, break aggressively into many short lines stacked vertically — portrait has vertical space to spare. `xl` (24px) works for single words or very short phrases.
Grid dimensions: `cols = VW // cell_width`, `rows = VH // cell_height`.
@@ -59,7 +117,23 @@ FONT_PREFS_LINUX = [
("Noto Sans Mono", "/usr/share/fonts/truetype/noto/NotoSansMono-Regular.ttf"),
("Ubuntu Mono", "/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf"),
]
FONT_PREFS = FONT_PREFS_MACOS if platform.system() == "Darwin" else FONT_PREFS_LINUX
FONT_PREFS_WINDOWS = [
("Consolas", r"C:\Windows\Fonts\consola.ttf"),
("Courier New", r"C:\Windows\Fonts\cour.ttf"),
("Lucida Console", r"C:\Windows\Fonts\lucon.ttf"),
("Cascadia Code", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaCode.ttf")),
("Cascadia Mono", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaMono.ttf")),
]
def _get_font_prefs():
s = platform.system()
if s == "Darwin":
return FONT_PREFS_MACOS
elif s == "Windows":
return FONT_PREFS_WINDOWS
return FONT_PREFS_LINUX
FONT_PREFS = _get_font_prefs()
```
**Multi-font rendering**: use different fonts for different layers (e.g., monospace for background, a bolder variant for overlay text). Each GridLayer owns its own font:
@@ -77,8 +151,8 @@ Before initializing grids, gather all characters that need bitmap pre-rasterizat
all_chars = set()
for pal in [PAL_DEFAULT, PAL_DENSE, PAL_BLOCKS, PAL_RUNE, PAL_KATA,
PAL_GREEK, PAL_MATH, PAL_DOTS, PAL_BRAILLE, PAL_STARS,
PAL_BINARY, PAL_MUSIC, PAL_BOX, PAL_CIRCUIT, PAL_ARROWS,
PAL_HERMES]: # ... all palettes used in project
PAL_HALFFILL, PAL_HATCH, PAL_BINARY, PAL_MUSIC, PAL_BOX,
PAL_CIRCUIT, PAL_ARROWS, PAL_HERMES]: # ... all palettes used in project
all_chars.update(pal)
# Add any overlay text characters
all_chars.update("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 .,-:;!?/|")
@@ -87,21 +161,31 @@ all_chars.discard(" ") # space is never rendered
### GridLayer Initialization
Each grid pre-computes coordinate arrays for vectorized effect math:
Each grid pre-computes coordinate arrays for vectorized effect math. The grid automatically adapts to any resolution (landscape, portrait, square):
```python
class GridLayer:
def __init__(self, font_path, font_size):
def __init__(self, font_path, font_size, vw=None, vh=None):
"""Initialize grid for any resolution.
vw, vh: video width/height in pixels. Defaults to global VW, VH."""
vw = vw or VW; vh = vh or VH
self.vw = vw; self.vh = vh
self.font = ImageFont.truetype(font_path, font_size)
asc, desc = self.font.getmetrics()
bbox = self.font.getbbox("M")
self.cw = bbox[2] - bbox[0] # character cell width
self.ch = asc + desc # CRITICAL: not textbbox height
self.cols = VW // self.cw
self.rows = VH // self.ch
self.ox = (VW - self.cols * self.cw) // 2 # centering
self.oy = (VH - self.rows * self.ch) // 2
self.cols = vw // self.cw
self.rows = vh // self.ch
self.ox = (vw - self.cols * self.cw) // 2 # centering
self.oy = (vh - self.rows * self.ch) // 2
# Aspect ratio metadata
self.aspect = vw / vh # >1 = landscape, <1 = portrait, 1 = square
self.is_portrait = vw < vh
self.is_landscape = vw > vh
# Index arrays
self.rr = np.arange(self.rows, dtype=np.float32)[:, None]
@@ -219,9 +303,11 @@ PAL_ARABIC = " \u0627\u0628\u062a\u062b\u062c\u062d\u062e\u062f\u0630\u0631\u0
#### Dot / Point Progressions
```python
PAL_DOTS = " \u22c5\u2218\u2219\u25cf\u25c9\u25ce\u25c6\u2726\u2605" # dot size progression
PAL_BRAILLE = " \u2801\u2802\u2803\u2804\u2805\u2806\u2807\u2808\u2809\u280a\u280b\u280c\u280d\u280e\u280f\u2810\u2811\u2812\u2813\u2814\u2815\u2816\u2817\u2818\u2819\u281a\u281b\u281c\u281d\u281e\u281f\u283f" # braille patterns
PAL_STARS = " \u00b7\u2727\u2726\u2729\u2728\u2605\u2736\u2733\u2738" # star progression
PAL_DOTS = " ⋅∘∙●◉◎◆✦★" # dot size progression
PAL_BRAILLE = " ⠁⠂⠃⠄⠅⠆⠇⠈⠉⠊⠋⠌⠍⠎⠏⠐⠑⠒⠓⠔⠕⠖⠗⠘⠙⠚⠛⠜⠝⠞⠟⠿" # braille patterns
PAL_STARS = " ·✧✦✩✨★✶✳✸" # star progression
PAL_HALFFILL = " ◔◑◕◐◒◓◖◗◙" # directional half-fill progression
PAL_HATCH = " ▣▤▥▦▧▨▩" # crosshatch density ramp
```
#### Project-Specific (examples -- invent new ones per project)
@@ -353,6 +439,202 @@ def rgb_palette_map(val, mask, palette):
return R, G, B
```
### OKLAB Color Space (Perceptually Uniform)
HSV hue is perceptually non-uniform: green occupies far more visual range than blue. OKLAB / OKLCH provide perceptually even color steps — hue increments of 0.1 look equally different regardless of starting hue. Use OKLAB for:
- Gradient interpolation (no unwanted intermediate hues)
- Color harmony generation (perceptually balanced palettes)
- Smooth color transitions over time
```python
# --- sRGB <-> Linear sRGB ---
def srgb_to_linear(c):
"""Convert sRGB [0,1] to linear light. c: float32 array."""
return np.where(c <= 0.04045, c / 12.92, ((c + 0.055) / 1.055) ** 2.4)
def linear_to_srgb(c):
"""Convert linear light to sRGB [0,1]."""
return np.where(c <= 0.0031308, c * 12.92, 1.055 * np.power(np.maximum(c, 0), 1/2.4) - 0.055)
# --- Linear sRGB <-> OKLAB ---
def linear_rgb_to_oklab(r, g, b):
"""Linear sRGB to OKLAB. r,g,b: float32 arrays [0,1].
Returns (L, a, b) where L=[0,1], a,b=[-0.4, 0.4] approx."""
l_ = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b
m_ = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b
s_ = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b
l_c = np.cbrt(l_); m_c = np.cbrt(m_); s_c = np.cbrt(s_)
L = 0.2104542553 * l_c + 0.7936177850 * m_c - 0.0040720468 * s_c
a = 1.9779984951 * l_c - 2.4285922050 * m_c + 0.4505937099 * s_c
b_ = 0.0259040371 * l_c + 0.7827717662 * m_c - 0.8086757660 * s_c
return L, a, b_
def oklab_to_linear_rgb(L, a, b):
"""OKLAB to linear sRGB. Returns (r, g, b) float32 arrays [0,1]."""
l_ = L + 0.3963377774 * a + 0.2158037573 * b
m_ = L - 0.1055613458 * a - 0.0638541728 * b
s_ = L - 0.0894841775 * a - 1.2914855480 * b
l_c = l_ ** 3; m_c = m_ ** 3; s_c = s_ ** 3
r = +4.0767416621 * l_c - 3.3077115913 * m_c + 0.2309699292 * s_c
g = -1.2684380046 * l_c + 2.6097574011 * m_c - 0.3413193965 * s_c
b_ = -0.0041960863 * l_c - 0.7034186147 * m_c + 1.7076147010 * s_c
return np.clip(r, 0, 1), np.clip(g, 0, 1), np.clip(b_, 0, 1)
# --- Convenience: sRGB uint8 <-> OKLAB ---
def rgb_to_oklab(R, G, B):
"""sRGB uint8 arrays to OKLAB."""
r = srgb_to_linear(R.astype(np.float32) / 255.0)
g = srgb_to_linear(G.astype(np.float32) / 255.0)
b = srgb_to_linear(B.astype(np.float32) / 255.0)
return linear_rgb_to_oklab(r, g, b)
def oklab_to_rgb(L, a, b):
"""OKLAB to sRGB uint8 arrays."""
r, g, b_ = oklab_to_linear_rgb(L, a, b)
R = np.clip(linear_to_srgb(r) * 255, 0, 255).astype(np.uint8)
G = np.clip(linear_to_srgb(g) * 255, 0, 255).astype(np.uint8)
B = np.clip(linear_to_srgb(b_) * 255, 0, 255).astype(np.uint8)
return R, G, B
# --- OKLCH (cylindrical form of OKLAB) ---
def oklab_to_oklch(L, a, b):
"""OKLAB to OKLCH. Returns (L, C, H) where H is in [0, 1] (normalized)."""
C = np.sqrt(a**2 + b**2)
H = (np.arctan2(b, a) / (2 * np.pi)) % 1.0
return L, C, H
def oklch_to_oklab(L, C, H):
"""OKLCH to OKLAB. H in [0, 1]."""
angle = H * 2 * np.pi
a = C * np.cos(angle)
b = C * np.sin(angle)
return L, a, b
```
### Gradient Interpolation (OKLAB vs HSV)
Interpolating colors through OKLAB avoids the hue detours that HSV produces:
```python
def lerp_oklab(color_a, color_b, t_array):
"""Interpolate between two sRGB colors through OKLAB.
color_a, color_b: (R, G, B) tuples 0-255
t_array: float32 array [0,1] — interpolation parameter per pixel.
Returns (R, G, B) uint8 arrays."""
La, aa, ba = rgb_to_oklab(
np.full_like(t_array, color_a[0], dtype=np.uint8),
np.full_like(t_array, color_a[1], dtype=np.uint8),
np.full_like(t_array, color_a[2], dtype=np.uint8))
Lb, ab, bb = rgb_to_oklab(
np.full_like(t_array, color_b[0], dtype=np.uint8),
np.full_like(t_array, color_b[1], dtype=np.uint8),
np.full_like(t_array, color_b[2], dtype=np.uint8))
L = La + (Lb - La) * t_array
a = aa + (ab - aa) * t_array
b = ba + (bb - ba) * t_array
return oklab_to_rgb(L, a, b)
def lerp_oklch(color_a, color_b, t_array, short_path=True):
"""Interpolate through OKLCH (preserves chroma, smooth hue path).
short_path: take the shorter arc around the hue wheel."""
La, aa, ba = rgb_to_oklab(
np.full_like(t_array, color_a[0], dtype=np.uint8),
np.full_like(t_array, color_a[1], dtype=np.uint8),
np.full_like(t_array, color_a[2], dtype=np.uint8))
Lb, ab, bb = rgb_to_oklab(
np.full_like(t_array, color_b[0], dtype=np.uint8),
np.full_like(t_array, color_b[1], dtype=np.uint8),
np.full_like(t_array, color_b[2], dtype=np.uint8))
L1, C1, H1 = oklab_to_oklch(La, aa, ba)
L2, C2, H2 = oklab_to_oklch(Lb, ab, bb)
# Shortest hue path
if short_path:
dh = H2 - H1
dh = np.where(dh > 0.5, dh - 1.0, np.where(dh < -0.5, dh + 1.0, dh))
H = (H1 + dh * t_array) % 1.0
else:
H = H1 + (H2 - H1) * t_array
L = L1 + (L2 - L1) * t_array
C = C1 + (C2 - C1) * t_array
Lout, aout, bout = oklch_to_oklab(L, C, H)
return oklab_to_rgb(Lout, aout, bout)
```
### Color Harmony Generation
Auto-generate harmonious palettes from a seed color:
```python
def harmony_complementary(seed_rgb):
"""Two colors: seed + opposite hue."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb, _oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0)]
def harmony_triadic(seed_rgb):
"""Three colors: seed + two at 120-degree offsets."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.333) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.667) % 1.0)]
def harmony_analogous(seed_rgb, spread=0.08, n=5):
"""N colors spread evenly around seed hue."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
offsets = np.linspace(-spread * (n-1)/2, spread * (n-1)/2, n)
return [_oklch_to_srgb_tuple(L[0], C[0], (H[0] + off) % 1.0) for off in offsets]
def harmony_split_complementary(seed_rgb, split=0.08):
"""Three colors: seed + two flanking the complement."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
comp = (H[0] + 0.5) % 1.0
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (comp - split) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (comp + split) % 1.0)]
def harmony_tetradic(seed_rgb):
"""Four colors: two complementary pairs at 90-degree offset."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.25) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.75) % 1.0)]
def _oklch_to_srgb_tuple(L, C, H):
"""Helper: single OKLCH -> sRGB (R,G,B) int tuple."""
La = np.array([L]); Ca = np.array([C]); Ha = np.array([H])
Lo, ao, bo = oklch_to_oklab(La, Ca, Ha)
R, G, B = oklab_to_rgb(Lo, ao, bo)
return (int(R[0]), int(G[0]), int(B[0]))
```
### OKLAB Hue Fields
Drop-in replacements for `hf_*` generators that produce perceptually uniform hue variation:
```python
def hf_oklch_angle(offset=0.0, chroma=0.12, lightness=0.7):
"""OKLCH hue mapped to angle from center. Perceptually uniform rainbow.
Returns (R, G, B) uint8 color array instead of a float hue.
NOTE: Use with _render_vf_rgb() variant, not standard _render_vf()."""
def fn(g, f, t, S):
H = (g.angle / (2 * np.pi) + offset + t * 0.05) % 1.0
L = np.full_like(H, lightness)
C = np.full_like(H, chroma)
Lo, ao, bo = oklch_to_oklab(L, C, H)
R, G, B = oklab_to_rgb(Lo, ao, bo)
return mkc(R, G, B, g.rows, g.cols)
return fn
```
### Compositing Helpers
```python
@@ -458,7 +740,7 @@ subprocess.run(["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_path,
### v2 Protocol (Current)
Every scene function: `(renderer, features_dict, time_float, state_dict) -> canvas_uint8`
Every scene function: `(r, f, t, S) -> canvas_uint8` — where `r` = Renderer, `f` = features dict, `t` = time float, `S` = persistent state dict
```python
def fx_example(r, f, t, S):
@@ -1,6 +1,14 @@
# Composition & Brightness Reference
The composable system is the core of visual complexity. It operates at three levels: pixel-level blend modes, multi-grid composition, and adaptive brightness management. This document covers all three.
The composable system is the core of visual complexity. It operates at three levels: pixel-level blend modes, multi-grid composition, and adaptive brightness management. This document covers all three, plus the masking/stencil system for spatial control.
**Cross-references:**
- Grid system, palettes, color (HSV + OKLAB): `architecture.md`
- Effect building blocks (value fields, hue fields, particles): `effects.md`
- Scene protocol, render_clip, SCENES table: `scenes.md`
- Shader pipeline, feedback buffer: `shaders.md`
- Complete scene examples with blend/mask usage: `examples.md`
- Blend mode pitfalls (overlay crush, division by zero): `troubleshooting.md`
## Pixel-Level Blend Modes
@@ -102,6 +110,69 @@ result = blend_canvas(result, canvas_c, "difference", 0.6)
Order matters: `screen(A, B)` is commutative, but `difference(screen(A,B), C)` differs from `difference(A, screen(B,C))`.
### Linear-Light Blend Modes
Standard `blend_canvas()` operates in sRGB space — the raw byte values. This is fine for most uses, but sRGB is perceptually non-linear: blending in sRGB darkens midtones and shifts hues slightly. For physically accurate blending (matching how light actually combines), convert to linear light first.
Uses `srgb_to_linear()` / `linear_to_srgb()` from `architecture.md` § OKLAB Color System.
```python
def blend_canvas_linear(base, top, mode="normal", opacity=1.0):
"""Blend in linear light space for physically accurate results.
Identical API to blend_canvas(), but converts sRGB → linear before
blending and linear → sRGB after. More expensive (~2x) due to the
gamma conversions, but produces correct results for additive blending,
screen, and any mode where brightness matters.
"""
af = srgb_to_linear(base.astype(np.float32) / 255.0)
bf = srgb_to_linear(top.astype(np.float32) / 255.0)
fn = BLEND_MODES.get(mode, BLEND_MODES["normal"])
result = fn(af, bf)
if opacity < 1.0:
result = af * (1 - opacity) + result * opacity
result = linear_to_srgb(np.clip(result, 0, 1))
return np.clip(result * 255, 0, 255).astype(np.uint8)
```
**When to use `blend_canvas_linear()` vs `blend_canvas()`:**
| Scenario | Use | Why |
|----------|-----|-----|
| Screen-blending two bright layers | `linear` | sRGB screen over-brightens highlights |
| Add mode for glow/bloom effects | `linear` | Additive light follows linear physics |
| Blending text overlay at low opacity | `srgb` | Perceptual blending looks more natural for text |
| Multiply for shadow/darkening | `srgb` | Differences are minimal for darken ops |
| Color-critical work (matching reference) | `linear` | Avoids sRGB hue shifts in midtones |
| Performance-critical inner loop | `srgb` | ~2x faster, good enough for most ASCII art |
**Batch version** for compositing many layers (converts once, blends multiple, converts back):
```python
def blend_many_linear(layers, modes, opacities):
"""Blend a stack of layers in linear light space.
Args:
layers: list of uint8 (H,W,3) canvases
modes: list of blend mode strings (len = len(layers) - 1)
opacities: list of floats (len = len(layers) - 1)
Returns:
uint8 (H,W,3) canvas
"""
# Convert all to linear at once
linear = [srgb_to_linear(l.astype(np.float32) / 255.0) for l in layers]
result = linear[0]
for i in range(1, len(linear)):
fn = BLEND_MODES.get(modes[i-1], BLEND_MODES["normal"])
blended = fn(result, linear[i])
op = opacities[i-1]
if op < 1.0:
blended = result * (1 - op) + blended * op
result = np.clip(blended, 0, 1)
result = linear_to_srgb(result)
return np.clip(result * 255, 0, 255).astype(np.uint8)
```
---
## Multi-Grid Composition
@@ -219,19 +290,22 @@ def tonemap(canvas, target_mean=90, gamma=0.75, black_point=2, white_point=253):
"""Adaptive tone-mapping: normalizes + gamma-corrects so no frame is
fully dark or washed out.
1. Compute 1st and 99.5th percentile (ignores outlier pixels)
1. Compute 1st and 99.5th percentile on 4x subsample (16x fewer values,
negligible accuracy loss, major speedup at 1080p+)
2. Stretch that range to [0, 1]
3. Apply gamma curve (< 1 lifts shadows, > 1 darkens)
4. Rescale to [black_point, white_point]
"""
f = canvas.astype(np.float32)
lo = np.percentile(f, 1)
hi = np.percentile(f, 99.5)
sub = f[::4, ::4] # 4x subsample: ~390K values vs ~6.2M at 1080p
lo = np.percentile(sub, 1)
hi = np.percentile(sub, 99.5)
if hi - lo < 10:
hi = max(hi, lo + 10) # near-uniform frame fallback
f = np.clip((f - lo) / (hi - lo), 0.0, 1.0)
f = np.power(f, gamma)
f = f * (white_point - black_point) + black_point
np.power(f, gamma, out=f) # in-place: avoids allocation
np.multiply(f, (white_point - black_point), out=f)
np.add(f, black_point, out=f)
return np.clip(f, 0, 255).astype(np.uint8)
```
@@ -453,6 +527,208 @@ class FeedbackBuffer:
---
## Masking / Stencil System
Masks are float32 arrays `(rows, cols)` or `(VH, VW)` in range [0, 1]. They control where effects are visible: 1.0 = fully visible, 0.0 = fully hidden. Use masks to create figure/ground relationships, focal points, and shaped reveals.
### Shape Masks
```python
def mask_circle(g, cx_frac=0.5, cy_frac=0.5, radius=0.3, feather=0.05):
"""Circular mask centered at (cx_frac, cy_frac) in normalized coords.
feather: width of soft edge (0 = hard cutoff)."""
asp = g.cw / g.ch if hasattr(g, 'cw') else 1.0
dx = (g.cc / g.cols - cx_frac)
dy = (g.rr / g.rows - cy_frac) * asp
d = np.sqrt(dx**2 + dy**2)
if feather > 0:
return np.clip(1.0 - (d - radius) / feather, 0, 1)
return (d <= radius).astype(np.float32)
def mask_rect(g, x0=0.2, y0=0.2, x1=0.8, y1=0.8, feather=0.03):
"""Rectangular mask. Coordinates in [0,1] normalized."""
dx = np.maximum(x0 - g.cc / g.cols, g.cc / g.cols - x1)
dy = np.maximum(y0 - g.rr / g.rows, g.rr / g.rows - y1)
d = np.maximum(dx, dy)
if feather > 0:
return np.clip(1.0 - d / feather, 0, 1)
return (d <= 0).astype(np.float32)
def mask_ring(g, cx_frac=0.5, cy_frac=0.5, inner_r=0.15, outer_r=0.35,
feather=0.03):
"""Ring / annulus mask."""
inner = mask_circle(g, cx_frac, cy_frac, inner_r, feather)
outer = mask_circle(g, cx_frac, cy_frac, outer_r, feather)
return outer - inner
def mask_gradient_h(g, start=0.0, end=1.0):
"""Left-to-right gradient mask."""
return np.clip((g.cc / g.cols - start) / (end - start + 1e-10), 0, 1).astype(np.float32)
def mask_gradient_v(g, start=0.0, end=1.0):
"""Top-to-bottom gradient mask."""
return np.clip((g.rr / g.rows - start) / (end - start + 1e-10), 0, 1).astype(np.float32)
def mask_gradient_radial(g, cx_frac=0.5, cy_frac=0.5, inner=0.0, outer=0.5):
"""Radial gradient mask — bright at center, dark at edges."""
d = np.sqrt((g.cc / g.cols - cx_frac)**2 + (g.rr / g.rows - cy_frac)**2)
return np.clip(1.0 - (d - inner) / (outer - inner + 1e-10), 0, 1)
```
### Value Field as Mask
Use any `vf_*` function's output as a spatial mask:
```python
def mask_from_vf(vf_result, threshold=0.5, feather=0.1):
"""Convert a value field to a mask by thresholding.
feather: smooth edge width around threshold."""
if feather > 0:
return np.clip((vf_result - threshold + feather) / (2 * feather), 0, 1)
return (vf_result > threshold).astype(np.float32)
def mask_select(mask, vf_a, vf_b):
"""Spatial conditional: show vf_a where mask is 1, vf_b where mask is 0.
mask: float32 [0,1] array. Intermediate values blend."""
return vf_a * mask + vf_b * (1 - mask)
```
### Text Stencil
Render text to a mask. Effects are visible only through the letterforms:
```python
def mask_text(grid, text, row_frac=0.5, font=None, font_size=None):
"""Render text string as a float32 mask [0,1] at grid resolution.
Characters = 1.0, background = 0.0.
row_frac: vertical position as fraction of grid height.
font: PIL ImageFont (defaults to grid's font if None).
font_size: override font size for the mask text (for larger stencil text).
"""
from PIL import Image, ImageDraw, ImageFont
f = font or grid.font
if font_size and font != grid.font:
f = ImageFont.truetype(font.path, font_size)
# Render text to image at pixel resolution, then downsample to grid
img = Image.new("L", (grid.cols * grid.cw, grid.ch), 0)
draw = ImageDraw.Draw(img)
bbox = draw.textbbox((0, 0), text, font=f)
tw = bbox[2] - bbox[0]
x = (grid.cols * grid.cw - tw) // 2
draw.text((x, 0), text, fill=255, font=f)
row_mask = np.array(img, dtype=np.float32) / 255.0
# Place in full grid mask
mask = np.zeros((grid.rows, grid.cols), dtype=np.float32)
target_row = int(grid.rows * row_frac)
# Downsample rendered text to grid cells
for c in range(grid.cols):
px = c * grid.cw
if px + grid.cw <= row_mask.shape[1]:
cell = row_mask[:, px:px + grid.cw]
if cell.mean() > 0.1:
mask[target_row, c] = cell.mean()
return mask
def mask_text_block(grid, lines, start_row_frac=0.3, font=None):
"""Multi-line text stencil. Returns full grid mask."""
mask = np.zeros((grid.rows, grid.cols), dtype=np.float32)
for i, line in enumerate(lines):
row_frac = start_row_frac + i / grid.rows
line_mask = mask_text(grid, line, row_frac, font)
mask = np.maximum(mask, line_mask)
return mask
```
### Animated Masks
Masks that change over time for reveals, wipes, and morphing:
```python
def mask_iris(g, t, t_start, t_end, cx_frac=0.5, cy_frac=0.5,
max_radius=0.7, ease_fn=None):
"""Iris open/close: circle that grows from 0 to max_radius.
ease_fn: easing function (default: ease_in_out_cubic from effects.md)."""
if ease_fn is None:
ease_fn = lambda x: x * x * (3 - 2 * x) # smoothstep fallback
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
radius = ease_fn(progress) * max_radius
return mask_circle(g, cx_frac, cy_frac, radius, feather=0.03)
def mask_wipe_h(g, t, t_start, t_end, direction="right"):
"""Horizontal wipe reveal."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
if direction == "left":
progress = 1 - progress
return mask_gradient_h(g, start=progress - 0.05, end=progress + 0.05)
def mask_wipe_v(g, t, t_start, t_end, direction="down"):
"""Vertical wipe reveal."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
if direction == "up":
progress = 1 - progress
return mask_gradient_v(g, start=progress - 0.05, end=progress + 0.05)
def mask_dissolve(g, t, t_start, t_end, seed=42):
"""Random pixel dissolve — noise threshold sweeps from 0 to 1."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
rng = np.random.RandomState(seed)
noise = rng.random((g.rows, g.cols)).astype(np.float32)
return (noise < progress).astype(np.float32)
```
### Mask Boolean Operations
```python
def mask_union(a, b):
"""OR — visible where either mask is active."""
return np.maximum(a, b)
def mask_intersect(a, b):
"""AND — visible only where both masks are active."""
return np.minimum(a, b)
def mask_subtract(a, b):
"""A minus B — visible where A is active but B is not."""
return np.clip(a - b, 0, 1)
def mask_invert(m):
"""NOT — flip mask."""
return 1.0 - m
```
### Applying Masks to Canvases
```python
def apply_mask_canvas(canvas, mask, bg_canvas=None):
"""Apply a grid-resolution mask to a pixel canvas.
Expands mask from (rows, cols) to (VH, VW) via nearest-neighbor.
canvas: uint8 (VH, VW, 3)
mask: float32 (rows, cols) [0,1]
bg_canvas: what shows through where mask=0. None = black.
"""
# Expand mask to pixel resolution
mask_px = np.repeat(np.repeat(mask, canvas.shape[0] // mask.shape[0] + 1, axis=0),
canvas.shape[1] // mask.shape[1] + 1, axis=1)
mask_px = mask_px[:canvas.shape[0], :canvas.shape[1]]
if bg_canvas is not None:
return np.clip(canvas * mask_px[:, :, None] +
bg_canvas * (1 - mask_px[:, :, None]), 0, 255).astype(np.uint8)
return np.clip(canvas * mask_px[:, :, None], 0, 255).astype(np.uint8)
def apply_mask_vf(vf_a, vf_b, mask):
"""Apply mask at value-field level — blend two value fields spatially.
All arrays are (rows, cols) float32."""
return vf_a * mask + vf_b * (1 - mask)
```
---
## PixelBlendStack
Higher-level wrapper for multi-layer compositing:
@@ -0,0 +1,193 @@
# Scene Design Patterns
**Cross-references:**
- Scene protocol, SCENES table: `scenes.md`
- Blend modes, multi-grid composition, tonemap: `composition.md`
- Effect building blocks (value fields, noise, SDFs): `effects.md`
- Shader pipeline, feedback buffer: `shaders.md`
- Complete scene examples: `examples.md`
Higher-order patterns for composing scenes that feel intentional rather than random. These patterns use the existing building blocks (value fields, blend modes, shaders, feedback) but organize them with compositional intent.
## Layer Hierarchy
Every scene should have clear visual layers with distinct roles:
| Layer | Grid | Brightness | Purpose |
|-------|------|-----------|---------|
| **Background** | xs or sm (dense) | 0.10.25 | Atmosphere, texture. Never competes with content. |
| **Content** | md (balanced) | 0.40.8 | The main visual idea. Carries the scene's concept. |
| **Accent** | lg or sm (sparse) | 0.51.0 (sparse coverage) | Highlights, punctuation, sparse bright points. |
The background sets mood. The content layer is what the scene *is about*. The accent adds visual interest without overwhelming.
```python
def fx_example(r, f, t, S):
local = t
progress = min(local / 5.0, 1.0)
g_bg = r.get_grid("sm")
g_main = r.get_grid("md")
g_accent = r.get_grid("lg")
# --- Background: dim atmosphere ---
bg_val = vf_smooth_noise(g_bg, f, t * 0.3, S, octaves=2, bri=0.15)
# ... render bg to canvas
# --- Content: the main visual idea ---
content_val = vf_spiral(g_main, f, t, S, n_arms=n_arms, tightness=tightness)
# ... render content on top of canvas
# --- Accent: sparse highlights ---
accent_val = vf_noise_static(g_accent, f, t, S, density=0.05)
# ... render accent on top
return canvas
```
## Directional Parameter Arcs
Parameters should *go somewhere* over the scene's duration — not oscillate aimlessly with `sin(t * N)`.
**Bad:** `twist = 3.0 + 2.0 * math.sin(t * 0.6)` — wobbles back and forth, feels aimless.
**Good:** `twist = 2.0 + progress * 5.0` — starts gentle, ends intense. The scene *builds*.
Use `progress = min(local / duration, 1.0)` (0→1 over the scene) to drive directional change:
| Pattern | Formula | Feel |
|---------|---------|------|
| Linear ramp | `progress * range` | Steady buildup |
| Ease-out | `1 - (1 - progress) ** 2` | Fast start, gentle finish |
| Ease-in | `progress ** 2` | Slow start, accelerating |
| Step reveal | `np.clip((progress - 0.5) / 0.25, 0, 1)` | Nothing until 50%, then fades in |
| Build + plateau | `min(1.0, progress * 1.5)` | Reaches full at 67%, holds |
Oscillation is fine for *secondary* parameters (saturation shimmer, hue drift). But the *defining* parameter of the scene should have a direction.
### Examples of Directional Arcs
| Scene concept | Parameter | Arc |
|--------------|-----------|-----|
| Emergence | Ring radius | 0 → max (ease-out) |
| Shatter | Voronoi cell count | 8 → 38 (linear) |
| Descent | Tunnel speed | 2.0 → 10.0 (linear) |
| Mandala | Shape complexity | ring → +polygon → +star → +rosette (step reveals) |
| Crescendo | Layer count | 1 → 7 (staggered entry) |
| Entropy | Geometry visibility | 1.0 → 0.0 (consumed) |
## Scene Concepts
Each scene should be built around a *visual idea*, not an effect name.
**Bad:** "fx_plasma_cascade" — named after the effect. No concept.
**Good:** "fx_emergence" — a point of light expands into a field. The name tells you *what happens*.
Good scene concepts have:
1. A **visual metaphor** (emergence, descent, collision, entropy)
2. A **directional arc** (things change from A to B, not oscillate)
3. **Motivated layer choices** (each layer serves the concept)
4. **Motivated feedback** (transform direction matches the metaphor)
| Concept | Metaphor | Feedback transform | Why |
|---------|----------|-------------------|-----|
| Emergence | Birth, expansion | zoom-out | Past frames expand outward |
| Descent | Falling, acceleration | zoom-in | Past frames rush toward center |
| Inferno | Rising fire | shift-up | Past frames rise with the flames |
| Entropy | Decay, dissolution | none | Clean, no persistence — things disappear |
| Crescendo | Accumulation | zoom + hue_shift | Everything compounds and shifts |
## Compositional Techniques
### Counter-Rotating Dual Systems
Two instances of the same effect rotating in opposite directions create visual interference:
```python
# Primary spiral (clockwise)
s1_val = vf_spiral(g_main, f, t * 1.5, S, n_arms=n_arms_1, tightness=tightness_1)
# Counter-rotating spiral (counter-clockwise via negative time)
s2_val = vf_spiral(g_accent, f, -t * 1.2, S, n_arms=n_arms_2, tightness=tightness_2)
# Screen blend creates bright interference at crossing points
canvas = blend_canvas(canvas_with_s1, c2, "screen", 0.7)
```
Works with spirals, vortexes, rings. The counter-rotation creates constantly shifting interference patterns.
### Wave Collision
Two wave fronts converging from opposite sides, meeting at a collision point:
```python
collision_phase = abs(progress - 0.5) * 2 # 1→0→1 (0 at collision)
# Wave A approaches from left
offset_a = (1 - progress) * g.cols * 0.4
wave_a = np.sin((g.cc + offset_a) * 0.08 + t * 2) * 0.5 + 0.5
# Wave B approaches from right
offset_b = -(1 - progress) * g.cols * 0.4
wave_b = np.sin((g.cc + offset_b) * 0.08 - t * 2) * 0.5 + 0.5
# Interference peaks at collision
combined = wave_a * 0.5 + wave_b * 0.5 + np.abs(wave_a - wave_b) * (1 - collision_phase) * 0.5
```
### Progressive Fragmentation
Voronoi with cell count increasing over time — visual shattering:
```python
n_pts = int(8 + progress * 30) # 8 cells → 38 cells
# Pre-generate enough points, slice to n_pts
px = base_x[:n_pts] + np.sin(t * 0.3 + np.arange(n_pts) * 0.7) * (3 + progress * 3)
```
The edge glow width can also increase with progress to emphasize the cracks.
### Entropy / Consumption
A clean geometric pattern being overtaken by an organic process:
```python
# Geometry fades out
geo_val = clean_pattern * max(0.05, 1.0 - progress * 0.9)
# Organic process grows in
rd_val = vf_reaction_diffusion(g, f, t, S) * min(1.0, progress * 1.5)
# Render geometry first, organic on top — organic consumes geometry
```
### Staggered Layer Entry (Crescendo)
Layers enter one at a time, building to overwhelming density:
```python
def layer_strength(enter_t, ramp=1.5):
"""0.0 until enter_t, ramps to 1.0 over ramp seconds."""
return max(0.0, min(1.0, (local - enter_t) / ramp))
# Layer 1: always present
s1 = layer_strength(0.0)
# Layer 2: enters at 2s
s2 = layer_strength(2.0)
# Layer 3: enters at 4s
s3 = layer_strength(4.0)
# ... etc
# Each layer uses a different effect, grid, palette, and blend mode
# Screen blend between layers so they accumulate light
```
For a 15-second crescendo, 7 layers entering every 2 seconds works well. Use different blend modes (screen for most, add for energy, colordodge for the final wash).
## Scene Ordering
For a multi-scene reel or video:
- **Vary mood between adjacent scenes** — don't put two calm scenes next to each other
- **Randomize order** rather than grouping by type — prevents "effect demo" feel
- **End on the strongest scene** — crescendo or something with a clear payoff
- **Open with energy** — grab attention in the first 2 seconds
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,416 @@
# Scene Examples
**Cross-references:**
- Grid system, palettes, color (HSV + OKLAB): `architecture.md`
- Effect building blocks (value fields, noise, SDFs, particles): `effects.md`
- `_render_vf()`, blend modes, tonemap, masking: `composition.md`
- Scene protocol, render_clip, SCENES table: `scenes.md`
- Shader pipeline, feedback buffer, ShaderChain: `shaders.md`
- Input sources (audio features, video features): `inputs.md`
- Performance tuning: `optimization.md`
- Common bugs: `troubleshooting.md`
Copy-paste-ready scene functions at increasing complexity. Each is a complete, working v2 scene function that returns a pixel canvas. See `scenes.md` for the scene protocol and `composition.md` for blend modes and tonemap.
---
## Minimal — Single Grid, Single Effect
### Breathing Plasma
One grid, one value field, one hue field. The simplest possible scene.
```python
def fx_breathing_plasma(r, f, t, S):
"""Plasma field with time-cycling hue. Audio modulates brightness."""
canvas = _render_vf(r, "md",
lambda g, f, t, S: vf_plasma(g, f, t, S) * 1.3,
hf_time_cycle(0.08), PAL_DENSE, f, t, S, sat=0.8)
return canvas
```
### Reaction-Diffusion Coral
Single grid, simulation-based field. Evolves organically over time.
```python
def fx_coral(r, f, t, S):
"""Gray-Scott reaction-diffusion — coral branching pattern.
Slow-evolving, organic. Best for ambient/chill sections."""
canvas = _render_vf(r, "sm",
lambda g, f, t, S: vf_reaction_diffusion(g, f, t, S,
feed=0.037, kill=0.060, steps_per_frame=6, init_mode="center"),
hf_distance(0.55, 0.015), PAL_DOTS, f, t, S, sat=0.7)
return canvas
```
### SDF Geometry
Geometric shapes from SDFs. Clean, precise, graphic.
```python
def fx_sdf_rings(r, f, t, S):
"""Concentric SDF rings with smooth pulsing."""
def val_fn(g, f, t, S):
d1 = sdf_ring(g, radius=0.15 + f.get("bass", 0.3) * 0.05, thickness=0.015)
d2 = sdf_ring(g, radius=0.25 + f.get("mid", 0.3) * 0.05, thickness=0.012)
d3 = sdf_ring(g, radius=0.35 + f.get("hi", 0.3) * 0.04, thickness=0.010)
combined = sdf_smooth_union(sdf_smooth_union(d1, d2, 0.05), d3, 0.05)
return sdf_glow(combined, falloff=0.08) * (0.5 + f.get("rms", 0.3) * 0.8)
canvas = _render_vf(r, "md", val_fn, hf_angle(0.0), PAL_STARS, f, t, S, sat=0.85)
return canvas
```
---
## Standard — Two Grids + Blend
### Tunnel Through Noise
Two grids at different densities, screen blended. The fine noise texture shows through the coarser tunnel characters.
```python
def fx_tunnel_noise(r, f, t, S):
"""Tunnel depth on md grid + fBM noise on sm grid, screen blended."""
canvas_a = _render_vf(r, "md",
lambda g, f, t, S: vf_tunnel(g, f, t, S, speed=4.0, complexity=8) * 1.2,
hf_distance(0.5, 0.02), PAL_BLOCKS, f, t, S, sat=0.7)
canvas_b = _render_vf(r, "sm",
lambda g, f, t, S: vf_fbm(g, f, t, S, octaves=4, freq=0.05, speed=0.15) * 1.3,
hf_time_cycle(0.06), PAL_RUNE, f, t, S, sat=0.6)
return blend_canvas(canvas_a, canvas_b, "screen", 0.7)
```
### Voronoi Cells + Spiral Overlay
Voronoi cell edges with a spiral arm pattern overlaid.
```python
def fx_voronoi_spiral(r, f, t, S):
"""Voronoi edge detection on md + logarithmic spiral on lg."""
canvas_a = _render_vf(r, "md",
lambda g, f, t, S: vf_voronoi(g, f, t, S,
n_cells=15, mode="edge", edge_width=2.0, speed=0.4),
hf_angle(0.2), PAL_CIRCUIT, f, t, S, sat=0.75)
canvas_b = _render_vf(r, "lg",
lambda g, f, t, S: vf_spiral(g, f, t, S, n_arms=4, tightness=3.0) * 1.2,
hf_distance(0.1, 0.03), PAL_BLOCKS, f, t, S, sat=0.9)
return blend_canvas(canvas_a, canvas_b, "exclusion", 0.6)
```
### Domain-Warped fBM
Two layers of the same fBM, one domain-warped, difference-blended for psychedelic organic texture.
```python
def fx_organic_warp(r, f, t, S):
"""Clean fBM vs domain-warped fBM, difference blended."""
canvas_a = _render_vf(r, "sm",
lambda g, f, t, S: vf_fbm(g, f, t, S, octaves=5, freq=0.04, speed=0.1),
hf_plasma(0.2), PAL_DENSE, f, t, S, sat=0.6)
canvas_b = _render_vf(r, "md",
lambda g, f, t, S: vf_domain_warp(g, f, t, S,
warp_strength=20.0, freq=0.05, speed=0.15),
hf_time_cycle(0.05), PAL_BRAILLE, f, t, S, sat=0.7)
return blend_canvas(canvas_a, canvas_b, "difference", 0.7)
```
---
## Complex — Three Grids + Conditional + Feedback
### Psychedelic Cathedral
Three-grid composition with beat-triggered kaleidoscope and feedback zoom tunnel. The most visually complex pattern.
```python
def fx_cathedral(r, f, t, S):
"""Three-layer cathedral: interference + rings + noise, kaleidoscope on beat,
feedback zoom tunnel."""
# Layer 1: interference pattern on sm grid
canvas_a = _render_vf(r, "sm",
lambda g, f, t, S: vf_interference(g, f, t, S, n_waves=7) * 1.3,
hf_angle(0.0), PAL_MATH, f, t, S, sat=0.8)
# Layer 2: pulsing rings on md grid
canvas_b = _render_vf(r, "md",
lambda g, f, t, S: vf_rings(g, f, t, S, n_base=10, spacing_base=3) * 1.4,
hf_distance(0.3, 0.02), PAL_STARS, f, t, S, sat=0.9)
# Layer 3: temporal noise on lg grid (slow morph)
canvas_c = _render_vf(r, "lg",
lambda g, f, t, S: vf_temporal_noise(g, f, t, S,
freq=0.04, t_freq=0.2, octaves=3),
hf_time_cycle(0.12), PAL_BLOCKS, f, t, S, sat=0.7)
# Blend: A screen B, then difference with C
result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
result = blend_canvas(result, canvas_c, "difference", 0.5)
# Beat-triggered kaleidoscope
if f.get("bdecay", 0) > 0.3:
folds = 6 if f.get("sub_r", 0.3) > 0.4 else 8
result = sh_kaleidoscope(result.copy(), folds=folds)
return result
# Scene table entry with feedback:
# {"start": 30.0, "end": 50.0, "name": "cathedral", "fx": fx_cathedral,
# "gamma": 0.65, "shaders": [("bloom", {"thr": 110}), ("chromatic", {"amt": 4}),
# ("vignette", {"s": 0.2}), ("grain", {"amt": 8})],
# "feedback": {"decay": 0.75, "blend": "screen", "opacity": 0.35,
# "transform": "zoom", "transform_amt": 0.012, "hue_shift": 0.015}}
```
### Masked Reaction-Diffusion with Attractor Overlay
Reaction-diffusion visible only through an animated iris mask, with a strange attractor density field underneath.
```python
def fx_masked_life(r, f, t, S):
"""Attractor base + reaction-diffusion visible through iris mask + particles."""
g_sm = r.get_grid("sm")
g_md = r.get_grid("md")
# Layer 1: strange attractor density field (background)
canvas_bg = _render_vf(r, "sm",
lambda g, f, t, S: vf_strange_attractor(g, f, t, S,
attractor="clifford", n_points=30000),
hf_time_cycle(0.04), PAL_DOTS, f, t, S, sat=0.5)
# Layer 2: reaction-diffusion (foreground, will be masked)
canvas_rd = _render_vf(r, "md",
lambda g, f, t, S: vf_reaction_diffusion(g, f, t, S,
feed=0.046, kill=0.063, steps_per_frame=4, init_mode="ring"),
hf_angle(0.15), PAL_HALFFILL, f, t, S, sat=0.85)
# Animated iris mask — opens over first 5 seconds of scene
scene_start = S.get("_scene_start", t)
if "_scene_start" not in S:
S["_scene_start"] = t
mask = mask_iris(g_md, t, scene_start, scene_start + 5.0,
max_radius=0.6)
canvas_rd = apply_mask_canvas(canvas_rd, mask, bg_canvas=canvas_bg)
# Layer 3: flow-field particles following the R-D gradient
rd_field = vf_reaction_diffusion(g_sm, f, t, S,
feed=0.046, kill=0.063, steps_per_frame=0) # read without stepping
ch_p, co_p = update_flow_particles(S, g_sm, f, rd_field,
n=300, speed=0.8, char_set=list("·•◦∘°"))
canvas_p = g_sm.render(ch_p, co_p)
result = blend_canvas(canvas_rd, canvas_p, "add", 0.7)
return result
```
### Morphing Field Sequence with Eased Keyframes
Demonstrates temporal coherence: smooth morphing between effects with keyframed parameters.
```python
def fx_morphing_journey(r, f, t, S):
"""Morphs through 4 value fields over 20 seconds with eased transitions.
Parameters (twist, arm count) also keyframed."""
# Keyframed twist parameter
twist = keyframe(t, [(0, 1.0), (5, 5.0), (10, 2.0), (15, 8.0), (20, 1.0)],
ease_fn=ease_in_out_cubic, loop=True)
# Sequence of value fields with 2s crossfade
fields = [
lambda g, f, t, S: vf_plasma(g, f, t, S),
lambda g, f, t, S: vf_vortex(g, f, t, S, twist=twist),
lambda g, f, t, S: vf_fbm(g, f, t, S, octaves=5, freq=0.04),
lambda g, f, t, S: vf_domain_warp(g, f, t, S, warp_strength=15),
]
durations = [5.0, 5.0, 5.0, 5.0]
val_fn = lambda g, f, t, S: vf_sequence(g, f, t, S, fields, durations,
crossfade=2.0)
# Render with slowly rotating hue
canvas = _render_vf(r, "md", val_fn, hf_time_cycle(0.06),
PAL_DENSE, f, t, S, sat=0.8)
# Second layer: tiled version of same sequence at smaller grid
tiled_fn = lambda g, f, t, S: vf_sequence(
make_tgrid(g, *uv_tile(g, 3, 3, mirror=True)),
f, t, S, fields, durations, crossfade=2.0)
canvas_b = _render_vf(r, "sm", tiled_fn, hf_angle(0.1),
PAL_RUNE, f, t, S, sat=0.6)
return blend_canvas(canvas, canvas_b, "screen", 0.5)
```
---
## Specialized — Unique State Patterns
### Game of Life with Ghost Trails
Cellular automaton with analog fade trails. Beat injects random cells.
```python
def fx_life(r, f, t, S):
"""Conway's Game of Life with fading ghost trails.
Beat events inject random live cells for disruption."""
canvas = _render_vf(r, "sm",
lambda g, f, t, S: vf_game_of_life(g, f, t, S,
rule="life", steps_per_frame=1, fade=0.92, density=0.25),
hf_fixed(0.33), PAL_BLOCKS, f, t, S, sat=0.8)
# Overlay: coral automaton on lg grid for chunky texture
canvas_b = _render_vf(r, "lg",
lambda g, f, t, S: vf_game_of_life(g, f, t, S,
rule="coral", steps_per_frame=1, fade=0.85, density=0.15, seed=99),
hf_time_cycle(0.1), PAL_HATCH, f, t, S, sat=0.6)
return blend_canvas(canvas, canvas_b, "screen", 0.5)
```
### Boids Flock Over Voronoi
Emergent swarm movement over a cellular background.
```python
def fx_boid_swarm(r, f, t, S):
"""Flocking boids over animated voronoi cells."""
# Background: voronoi cells
canvas_bg = _render_vf(r, "md",
lambda g, f, t, S: vf_voronoi(g, f, t, S,
n_cells=20, mode="distance", speed=0.2),
hf_distance(0.4, 0.02), PAL_CIRCUIT, f, t, S, sat=0.5)
# Foreground: boids
g = r.get_grid("md")
ch_b, co_b = update_boids(S, g, f, n_boids=150, perception=6.0,
max_speed=1.5, char_set=list("▸▹►▻→⟶"))
canvas_boids = g.render(ch_b, co_b)
# Trails for the boids
# (boid positions are stored in S["boid_x"], S["boid_y"])
S["px"] = list(S.get("boid_x", []))
S["py"] = list(S.get("boid_y", []))
ch_t, co_t = draw_particle_trails(S, g, max_trail=6, fade=0.6)
canvas_trails = g.render(ch_t, co_t)
result = blend_canvas(canvas_bg, canvas_trails, "add", 0.3)
result = blend_canvas(result, canvas_boids, "add", 0.9)
return result
```
### Fire Rising Through SDF Text Stencil
Fire effect visible only through text letterforms.
```python
def fx_fire_text(r, f, t, S):
"""Fire columns visible through text stencil. Text acts as window."""
g = r.get_grid("lg")
# Full-screen fire (will be masked)
canvas_fire = _render_vf(r, "sm",
lambda g, f, t, S: np.clip(
vf_fbm(g, f, t, S, octaves=4, freq=0.08, speed=0.8) *
(1.0 - g.rr / g.rows) * # fade toward top
(0.6 + f.get("bass", 0.3) * 0.8), 0, 1),
hf_fixed(0.05), PAL_BLOCKS, f, t, S, sat=0.9) # fire hue
# Background: dark domain warp
canvas_bg = _render_vf(r, "md",
lambda g, f, t, S: vf_domain_warp(g, f, t, S,
warp_strength=8, freq=0.03, speed=0.05) * 0.3,
hf_fixed(0.6), PAL_DENSE, f, t, S, sat=0.4)
# Text stencil mask
mask = mask_text(g, "FIRE", row_frac=0.45)
# Expand vertically for multi-row coverage
for offset in range(-2, 3):
shifted = mask_text(g, "FIRE", row_frac=0.45 + offset / g.rows)
mask = mask_union(mask, shifted)
canvas_masked = apply_mask_canvas(canvas_fire, mask, bg_canvas=canvas_bg)
return canvas_masked
```
### Portrait Mode: Vertical Rain + Quote
Optimized for 9:16. Uses vertical space for long rain trails and stacked text.
```python
def fx_portrait_rain_quote(r, f, t, S):
"""Portrait-optimized: matrix rain (long vertical trails) with stacked quote.
Designed for 1080x1920 (9:16)."""
g = r.get_grid("md") # ~112x100 in portrait
# Matrix rain — long trails benefit from portrait's extra rows
ch, co, S = eff_matrix_rain(g, f, t, S,
hue=0.33, bri=0.6, pal=PAL_KATA, speed_base=0.4, speed_beat=2.5)
canvas_rain = g.render(ch, co)
# Tunnel depth underneath for texture
canvas_tunnel = _render_vf(r, "sm",
lambda g, f, t, S: vf_tunnel(g, f, t, S, speed=3.0, complexity=6) * 0.8,
hf_fixed(0.33), PAL_BLOCKS, f, t, S, sat=0.5)
result = blend_canvas(canvas_tunnel, canvas_rain, "screen", 0.8)
# Quote text — portrait layout: short lines, many of them
g_text = r.get_grid("lg") # ~90x80 in portrait
quote_lines = layout_text_portrait(
"The code is the art and the art is the code",
max_chars_per_line=20)
# Center vertically
block_start = (g_text.rows - len(quote_lines)) // 2
ch_t = np.full((g_text.rows, g_text.cols), " ", dtype="U1")
co_t = np.zeros((g_text.rows, g_text.cols, 3), dtype=np.uint8)
total_chars = sum(len(l) for l in quote_lines)
progress = min(1.0, (t - S.get("_scene_start", t)) / 3.0)
if "_scene_start" not in S: S["_scene_start"] = t
render_typewriter(ch_t, co_t, quote_lines, block_start, g_text.cols,
progress, total_chars, (200, 255, 220), t)
canvas_text = g_text.render(ch_t, co_t)
result = blend_canvas(result, canvas_text, "add", 0.9)
return result
```
---
## Scene Table Template
Wire scenes into a complete video:
```python
SCENES = [
{"start": 0.0, "end": 5.0, "name": "coral",
"fx": fx_coral, "grid": "sm", "gamma": 0.70,
"shaders": [("bloom", {"thr": 110}), ("vignette", {"s": 0.2})],
"feedback": {"decay": 0.8, "blend": "screen", "opacity": 0.3,
"transform": "zoom", "transform_amt": 0.01}},
{"start": 5.0, "end": 15.0, "name": "tunnel_noise",
"fx": fx_tunnel_noise, "grid": "md", "gamma": 0.75,
"shaders": [("chromatic", {"amt": 3}), ("bloom", {"thr": 120}),
("scanlines", {"intensity": 0.06}), ("grain", {"amt": 8})],
"feedback": None},
{"start": 15.0, "end": 35.0, "name": "cathedral",
"fx": fx_cathedral, "grid": "sm", "gamma": 0.65,
"shaders": [("bloom", {"thr": 100}), ("chromatic", {"amt": 5}),
("color_wobble", {"amt": 0.2}), ("vignette", {"s": 0.18})],
"feedback": {"decay": 0.75, "blend": "screen", "opacity": 0.35,
"transform": "zoom", "transform_amt": 0.012, "hue_shift": 0.015}},
{"start": 35.0, "end": 50.0, "name": "morphing",
"fx": fx_morphing_journey, "grid": "md", "gamma": 0.70,
"shaders": [("bloom", {"thr": 110}), ("grain", {"amt": 6})],
"feedback": {"decay": 0.7, "blend": "screen", "opacity": 0.25,
"transform": "rotate_cw", "transform_amt": 0.003}},
]
```
+328 -43
View File
@@ -1,5 +1,14 @@
# Input Sources
**Cross-references:**
- Grid system, resolution presets: `architecture.md`
- Effect building blocks (audio-reactive modulation): `effects.md`
- Scene protocol, SCENES table (feature routing): `scenes.md`
- Shader pipeline, output encoding: `shaders.md`
- Performance tuning (audio chunking, WAV caching): `optimization.md`
- Common bugs (sample rate, dtype, silence handling): `troubleshooting.md`
- Complete scene examples with feature usage: `examples.md`
## Audio Analysis
### Loading
@@ -294,23 +303,73 @@ For narrated videos (testimonials, quotes, storytelling), generate speech audio
### ElevenLabs Voice Generation
```python
import requests
import requests, time, os
def generate_tts(text, voice_id, api_key, output_path, model="eleven_multilingual_v2"):
"""Generate TTS audio via ElevenLabs API."""
"""Generate TTS audio via ElevenLabs API. Streams response to disk."""
# Skip if already generated (idempotent re-runs)
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
return
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
data = {"text": text, "model_id": model,
"voice_settings": {"stability": 0.5, "similarity_boost": 0.75}}
resp = requests.post(url, json=data, headers=headers, timeout=30)
data = {
"text": text,
"model_id": model,
"voice_settings": {
"stability": 0.65,
"similarity_boost": 0.80,
"style": 0.15,
"use_speaker_boost": True,
},
}
resp = requests.post(url, json=data, headers=headers, stream=True)
resp.raise_for_status()
with open(output_path, "wb") as f:
f.write(resp.content)
for chunk in resp.iter_content(chunk_size=4096):
f.write(chunk)
time.sleep(0.3) # rate limit: avoid 429s on batch generation
```
Voice settings notes:
- `stability` 0.65 gives natural variation without drift. Lower (0.3-0.5) for more expressive reads, higher (0.7-0.9) for monotone/narration.
- `similarity_boost` 0.80 keeps it close to the voice profile. Lower for more generic sound.
- `style` 0.15 adds slight stylistic variation. Keep low (0-0.2) for straightforward reads.
- `use_speaker_boost` True improves clarity at the cost of slightly more processing time.
### Voice Pool
ElevenLabs has ~20 built-in voices. Use multiple voices for variety across quotes. Reference pool:
```python
VOICE_POOL = [
("JBFqnCBsd6RMkjVDRZzb", "George"),
("nPczCjzI2devNBz1zQrb", "Brian"),
("pqHfZKP75CvOlQylNhV4", "Bill"),
("CwhRBWXzGAHq8TQ4Fs17", "Roger"),
("cjVigY5qzO86Huf0OWal", "Eric"),
("onwK4e9ZLuTAKqWW03F9", "Daniel"),
("IKne3meq5aSn9XLyUdCD", "Charlie"),
("iP95p4xoKVk53GoZ742B", "Chris"),
("bIHbv24MWmeRgasZH58o", "Will"),
("TX3LPaxmHKxFdv7VOQHJ", "Liam"),
("SAz9YHcvj6GT2YYXdXww", "River"),
("EXAVITQu4vr4xnSDxMaL", "Sarah"),
("Xb7hH8MSUJpSbSDYk0k2", "Alice"),
("pFZP5JQG7iQjIQuC4Bku", "Lily"),
("XrExE9yKIg1WjnnlVkGX", "Matilda"),
("FGY2WhTYpPnrIDTdsKH5", "Laura"),
("SOYHLrjzK2X1ezoPC6cr", "Harry"),
("hpp4J3VqNfWAUOO0d1Us", "Bella"),
("N2lVS1w4EtoT3dr4eOWO", "Callum"),
("cgSgspJ2msm6clMCkdW9", "Jessica"),
("pNInz6obpgDQGcFmaJgB", "Adam"),
]
```
### Voice Assignment
Use multiple voices for variety. Shuffle deterministically so re-runs are consistent:
Shuffle deterministically so re-runs produce the same voice mapping:
```python
import random as _rng
@@ -318,83 +377,199 @@ import random as _rng
def assign_voices(n_quotes, voice_pool, seed=42):
"""Assign a different voice to each quote, cycling if needed."""
r = _rng.Random(seed)
shuffled = list(voice_pool)
r.shuffle(shuffled)
return [shuffled[i % len(shuffled)] for i in range(n_quotes)]
ids = [v[0] for v in voice_pool]
r.shuffle(ids)
return [ids[i % len(ids)] for i in range(n_quotes)]
```
### Pronunciation Control
TTS text should be separate from display text. Common fixes:
TTS text must be separate from display text. The display text has line breaks for visual layout; the TTS text is a flat sentence with phonetic fixes.
Common fixes:
- Brand names: spell phonetically ("Nous" -> "Noose", "nginx" -> "engine-x")
- Abbreviations: expand ("API" -> "A P I", "CLI" -> "C L I")
- Technical terms: add phonetic hints
- Punctuation for pacing: periods create pauses, commas create slight pauses
```python
QUOTES = [("Display text here", "Author")]
QUOTES_TTS = ["TTS text with phonetic spelling here"]
# Display text: line breaks control visual layout
QUOTES = [
("It can do far more than the Claws,\nand you don't need to buy a Mac Mini.\nNous Research has a winner here.", "Brian Roemmele"),
]
# TTS text: flat, phonetically corrected for speech
QUOTES_TTS = [
"It can do far more than the Claws, and you don't need to buy a Mac Mini. Noose Research has a winner here.",
]
# Keep both arrays in sync -- same indices
```
### Audio Pipeline
1. Generate individual TTS clips (MP3/WAV per quote)
2. Get duration of each clip
3. Calculate timing: speech start/end per quote with gaps
1. Generate individual TTS clips (MP3 per quote, skipping existing)
2. Convert each to WAV (mono, 22050 Hz) for duration measurement and concatenation
3. Calculate timing: intro pad + speech + gaps + outro pad = target duration
4. Concatenate into single TTS track with silence padding
5. Mix with background music
```python
def build_tts_track(tts_clips, target_duration, gap_seconds=2.0):
"""Concatenate TTS clips with gaps, pad to target duration."""
# Get durations
def build_tts_track(tts_clips, target_duration, intro_pad=5.0, outro_pad=4.0):
"""Concatenate TTS clips with calculated gaps, pad to target duration.
Returns:
timing: list of (start_time, end_time, quote_index) tuples
"""
sr = 22050
# Convert MP3s to WAV for duration and sample-level concatenation
durations = []
for clip in tts_clips:
wav = clip.replace(".mp3", ".wav")
subprocess.run(
["ffmpeg", "-y", "-i", clip, "-ac", "1", "-ar", str(sr),
"-sample_fmt", "s16", wav],
capture_output=True, check=True)
result = subprocess.run(
["ffprobe", "-v", "error", "-show_entries", "format=duration",
"-of", "csv=p=0", clip],
"-of", "csv=p=0", wav],
capture_output=True, text=True)
durations.append(float(result.stdout.strip()))
# Calculate timing
# Calculate gap to fill target duration
total_speech = sum(durations)
total_gaps = target_duration - total_speech
gap = max(0.5, total_gaps / (len(tts_clips) + 1))
timing = [] # (start, end, quote_index)
t = gap # start after initial gap
n_gaps = len(tts_clips) - 1
remaining = target_duration - total_speech - intro_pad - outro_pad
gap = max(1.0, remaining / max(1, n_gaps))
# Build timing and concatenate samples
timing = []
t = intro_pad
all_audio = [np.zeros(int(sr * intro_pad), dtype=np.int16)]
for i, dur in enumerate(durations):
wav = tts_clips[i].replace(".mp3", ".wav")
with wave.open(wav) as wf:
samples = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
timing.append((t, t + dur, i))
t += dur + gap
# Concatenate with ffmpeg
# ... silence padding + concat filter
all_audio.append(samples)
t += dur
if i < len(tts_clips) - 1:
all_audio.append(np.zeros(int(sr * gap), dtype=np.int16))
t += gap
all_audio.append(np.zeros(int(sr * outro_pad), dtype=np.int16))
# Pad or trim to exactly target_duration
full = np.concatenate(all_audio)
target_samples = int(sr * target_duration)
if len(full) < target_samples:
full = np.pad(full, (0, target_samples - len(full)))
else:
full = full[:target_samples]
# Write concatenated TTS track
with wave.open("tts_full.wav", "w") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sr)
wf.writeframes(full.tobytes())
return timing
```
### Audio Mixing
Mix TTS (center) with background music (wide stereo, low volume):
Mix TTS (center) with background music (wide stereo, low volume). The filter chain:
1. TTS mono duplicated to both channels (centered)
2. BGM loudness-normalized, volume reduced to 15%, stereo widened with `extrastereo`
3. Mixed together with dropout transition for smooth endings
```python
def mix_audio(tts_path, bgm_path, output_path, bgm_volume=0.15):
"""Mix TTS centered with BGM panned wide stereo."""
filter_complex = (
# TTS: mono -> stereo center
"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=mono,"
"pan=stereo|c0=c0|c1=c0[tts];"
# BGM: normalize loudness, reduce volume, widen stereo
f"[1:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo,"
f"loudnorm=I=-16:TP=-1.5:LRA=11,"
f"volume={bgm_volume},"
f"extrastereo=m=2.5[bgm];"
# Mix with smooth dropout at end
"[tts][bgm]amix=inputs=2:duration=longest:dropout_transition=3,"
"aformat=sample_fmts=s16:sample_rates=44100:channel_layouts=stereo[out]"
)
cmd = [
"ffmpeg", "-y",
"-i", tts_path, # mono TTS
"-i", bgm_path, # stereo BGM
"-filter_complex",
f"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=mono,"
f"pan=stereo|c0=c0|c1=c0[tts];" # TTS center
f"[1:a]loudnorm=I=-16:TP=-1.5:LRA=11,"
f"volume={bgm_volume},"
f"extrastereo=2.5[bgm];" # BGM wide stereo
f"[tts][bgm]amix=inputs=2:duration=longest[out]",
"-map", "[out]", "-c:a", "pcm_s16le", output_path
"-i", tts_path,
"-i", bgm_path,
"-filter_complex", filter_complex,
"-map", "[out]", output_path,
]
subprocess.run(cmd, capture_output=True, check=True)
```
### Per-Quote Visual Style
Cycle through visual presets per quote for variety. Each preset defines a background effect, color scheme, and text color:
```python
QUOTE_STYLES = [
{"hue": 0.08, "accent": 0.7, "bg": "spiral", "text_rgb": (255, 220, 140)}, # warm gold
{"hue": 0.55, "accent": 0.6, "bg": "rings", "text_rgb": (180, 220, 255)}, # cool blue
{"hue": 0.75, "accent": 0.7, "bg": "wave", "text_rgb": (220, 180, 255)}, # purple
{"hue": 0.35, "accent": 0.6, "bg": "matrix", "text_rgb": (140, 255, 180)}, # green
{"hue": 0.95, "accent": 0.8, "bg": "fire", "text_rgb": (255, 180, 160)}, # red/coral
{"hue": 0.12, "accent": 0.5, "bg": "interference", "text_rgb": (255, 240, 200)}, # amber
{"hue": 0.60, "accent": 0.7, "bg": "tunnel", "text_rgb": (160, 210, 255)}, # cyan
{"hue": 0.45, "accent": 0.6, "bg": "aurora", "text_rgb": (180, 255, 220)}, # teal
]
style = QUOTE_STYLES[quote_index % len(QUOTE_STYLES)]
```
This guarantees no two adjacent quotes share the same look, even without randomness.
### Typewriter Text Rendering
Display quote text character-by-character synced to speech progress. Recently revealed characters are brighter, creating a "just typed" glow:
```python
def render_typewriter(ch, co, lines, block_start, cols, progress, total_chars, text_rgb, t):
"""Overlay typewriter text onto character/color grids.
progress: 0.0 (nothing visible) to 1.0 (all text visible)."""
chars_visible = int(total_chars * min(1.0, progress * 1.2)) # slight overshoot for snappy feel
tr, tg, tb = text_rgb
char_count = 0
for li, line in enumerate(lines):
row = block_start + li
col = (cols - len(line)) // 2
for ci, c in enumerate(line):
if char_count < chars_visible:
age = chars_visible - char_count
bri_factor = min(1.0, 0.5 + 0.5 / (1 + age * 0.015)) # newer = brighter
hue_shift = math.sin(char_count * 0.3 + t * 2) * 0.05
stamp(ch, co, c, row, col + ci,
(int(min(255, tr * bri_factor * (1.0 + hue_shift))),
int(min(255, tg * bri_factor)),
int(min(255, tb * bri_factor * (1.0 - hue_shift)))))
char_count += 1
# Blinking cursor at insertion point
if progress < 1.0 and int(t * 3) % 2 == 0:
# Find cursor position (char_count == chars_visible)
cc = 0
for li, line in enumerate(lines):
for ci, c in enumerate(line):
if cc == chars_visible:
stamp(ch, co, "\u258c", block_start + li,
(cols - len(line)) // 2 + ci, (255, 220, 100))
return
cc += 1
```
### Feature Analysis on Mixed Audio
Run the standard audio analysis (FFT, beat detection) on the final mixed track so visual effects react to both TTS and music:
@@ -404,4 +579,114 @@ Run the standard audio analysis (FFT, beat detection) on the final mixed track s
features = analyze_audio("mixed_final.wav", fps=24)
```
This means visuals will pulse with both the music beats and the speech energy -- creating natural synchronization.
Visuals pulse with both the music beats and the speech energy.
---
## Audio-Video Sync Verification
After rendering, verify that visual beat markers align with actual audio beats. Drift accumulates from frame timing errors, ffmpeg concat boundaries, and rounding in `fi / fps`.
### Beat Timestamp Extraction
```python
def extract_beat_timestamps(features, fps, threshold=0.5):
"""Extract timestamps where beat feature exceeds threshold."""
beat = features["beat"]
timestamps = []
for fi in range(len(beat)):
if beat[fi] > threshold:
timestamps.append(fi / fps)
return timestamps
def extract_visual_beat_timestamps(video_path, fps, brightness_jump=30):
"""Detect visual beats by brightness jumps between consecutive frames.
Returns timestamps where mean brightness increases by more than threshold."""
import subprocess
cmd = ["ffmpeg", "-i", video_path, "-f", "rawvideo", "-pix_fmt", "gray", "-"]
proc = subprocess.run(cmd, capture_output=True)
frames = np.frombuffer(proc.stdout, dtype=np.uint8)
# Infer frame dimensions from total byte count
n_pixels = len(frames)
# For 1080p: 1920*1080 pixels per frame
# Auto-detect from video metadata is more robust:
probe = subprocess.run(
["ffprobe", "-v", "error", "-select_streams", "v:0",
"-show_entries", "stream=width,height",
"-of", "csv=p=0", video_path],
capture_output=True, text=True)
w, h = map(int, probe.stdout.strip().split(","))
ppf = w * h # pixels per frame
n_frames = n_pixels // ppf
frames = frames[:n_frames * ppf].reshape(n_frames, ppf)
means = frames.mean(axis=1)
timestamps = []
for i in range(1, len(means)):
if means[i] - means[i-1] > brightness_jump:
timestamps.append(i / fps)
return timestamps
```
### Sync Report
```python
def sync_report(audio_beats, visual_beats, tolerance_ms=50):
"""Compare audio beat timestamps to visual beat timestamps.
Args:
audio_beats: list of timestamps (seconds) from audio analysis
visual_beats: list of timestamps (seconds) from video brightness analysis
tolerance_ms: max acceptable drift in milliseconds
Returns:
dict with matched/unmatched/drift statistics
"""
tolerance = tolerance_ms / 1000.0
matched = []
unmatched_audio = []
unmatched_visual = list(visual_beats)
for at in audio_beats:
best_match = None
best_delta = float("inf")
for vt in unmatched_visual:
delta = abs(at - vt)
if delta < best_delta:
best_delta = delta
best_match = vt
if best_match is not None and best_delta < tolerance:
matched.append({"audio": at, "visual": best_match, "drift_ms": best_delta * 1000})
unmatched_visual.remove(best_match)
else:
unmatched_audio.append(at)
drifts = [m["drift_ms"] for m in matched]
return {
"matched": len(matched),
"unmatched_audio": len(unmatched_audio),
"unmatched_visual": len(unmatched_visual),
"total_audio_beats": len(audio_beats),
"total_visual_beats": len(visual_beats),
"mean_drift_ms": np.mean(drifts) if drifts else 0,
"max_drift_ms": np.max(drifts) if drifts else 0,
"p95_drift_ms": np.percentile(drifts, 95) if len(drifts) > 1 else 0,
}
# Usage:
audio_beats = extract_beat_timestamps(features, fps=24)
visual_beats = extract_visual_beat_timestamps("output.mp4", fps=24)
report = sync_report(audio_beats, visual_beats)
print(f"Matched: {report['matched']}/{report['total_audio_beats']} beats")
print(f"Mean drift: {report['mean_drift_ms']:.1f}ms, Max: {report['max_drift_ms']:.1f}ms")
# Target: mean drift < 20ms, max drift < 42ms (1 frame at 24fps)
```
### Common Sync Issues
| Symptom | Cause | Fix |
|---------|-------|-----|
| Consistent late visual beats | ffmpeg concat adds frames at boundaries | Use `-vsync cfr` flag; pad segments to exact frame count |
| Drift increases over time | Floating-point accumulation in `t = fi / fps` | Use integer frame counter, compute `t` fresh each frame |
| Random missed beats | Beat threshold too high / feature smoothing too aggressive | Lower threshold; reduce EMA alpha for beat feature |
| Beats land on wrong frame | Off-by-one in frame indexing | Verify: frame 0 = t=0, frame 1 = t=1/fps (not t=0) |
@@ -1,5 +1,15 @@
# Optimization Reference
**Cross-references:**
- Grid system, resolution presets, portrait GridLayer: `architecture.md`
- Effect building blocks (pre-computation strategies): `effects.md`
- `_render_vf()`, tonemap (subsampled percentile): `composition.md`
- Scene protocol, render_clip: `scenes.md`
- Shader pipeline, encoding (ffmpeg flags): `shaders.md`
- Input sources (audio chunking, WAV extraction): `inputs.md`
- Common bugs (memory, OOM, frame drops): `troubleshooting.md`
- Complete scene examples: `examples.md`
## Hardware Detection
Detect the user's hardware at script startup and adapt rendering parameters automatically. Never hardcode worker counts or resolution.
@@ -124,6 +134,8 @@ def apply_quality_profile(profile):
parser = argparse.ArgumentParser()
parser.add_argument("--quality", choices=["draft", "preview", "production", "max", "auto"],
default="auto", help="Render quality preset")
parser.add_argument("--aspect", choices=["landscape", "portrait", "square"],
default="landscape", help="Aspect ratio preset")
parser.add_argument("--workers", type=int, default=0, help="Override worker count (0=auto)")
parser.add_argument("--resolution", type=str, default="", help="Override resolution e.g. 1280x720")
args = parser.parse_args()
@@ -132,6 +144,16 @@ hw = detect_hardware()
if args.workers > 0:
hw["workers"] = args.workers
profile = quality_profile(hw, target_duration, args.quality)
# Apply aspect ratio preset (before manual resolution override)
ASPECT_PRESETS = {
"landscape": (1920, 1080),
"portrait": (1080, 1920),
"square": (1080, 1080),
}
if args.aspect != "landscape" and not args.resolution:
profile["vw"], profile["vh"] = ASPECT_PRESETS[args.aspect]
if args.resolution:
w, h = args.resolution.split("x")
profile["vw"], profile["vh"] = int(w), int(h)
@@ -142,6 +164,47 @@ log(f"Render: {profile['vw']}x{profile['vh']} @{profile['fps']}fps, "
f"CRF {profile['crf']}, {profile['workers']} workers")
```
### Portrait Mode Considerations
Portrait (1080x1920) has the same pixel count as landscape 1080p, so performance is equivalent. But composition patterns differ:
| Concern | Landscape | Portrait |
|---------|-----------|----------|
| Grid cols at `lg` | 160 | 90 |
| Grid rows at `lg` | 45 | 80 |
| Max text line chars | ~50 centered | ~25-30 centered |
| Vertical rain | Short travel | Long, dramatic travel |
| Horizontal spectrum | Full width | Needs rotation or compression |
| Radial effects | Natural circles | Tall ellipses (aspect correction handles this) |
| Particle explosions | Wide spread | Tall spread |
| Text stacking | 3-4 lines comfortable | 8-10 lines comfortable |
| Quote layout | 2-3 wide lines | 5-6 short lines |
**Portrait-optimized patterns:**
- Vertical rain/matrix effects are naturally enhanced — longer column travel
- Fire columns rise through more screen space
- Rising embers/particles have more vertical runway
- Text can be stacked more aggressively with more lines
- Radial effects work if aspect correction is applied (GridLayer handles this automatically)
- Spectrum bars can be rotated 90 degrees (vertical bars from bottom)
**Portrait text layout:**
```python
def layout_text_portrait(text, max_chars_per_line=25, grid=None):
"""Break text into short lines for portrait display."""
words = text.split()
lines = []; current = ""
for w in words:
if len(current) + len(w) + 1 > max_chars_per_line:
lines.append(current.strip())
current = w + " "
else:
current += w + " "
if current.strip():
lines.append(current.strip())
return lines
```
## Performance Budget
Target: 100-200ms per frame (5-10 fps single-threaded, 40-80 fps across 8 workers).
@@ -173,6 +236,74 @@ canvas[y:y+ch, x:x+cw] = np.maximum(canvas[y:y+ch, x:x+cw],
Collect all characters from all palettes + overlay text into the init set. Lazy-init for any missed characters.
## Pre-Rendered Background Textures
Alternative to `_render_vf()` for backgrounds where characters don't need to change every frame. Pre-bake a static ASCII texture once at init, then multiply by a per-cell color field each frame. One matrix multiply vs thousands of bitmap blits.
Use when: background layer uses a fixed character palette and only color/brightness varies per frame. NOT suitable for layers where character selection depends on a changing value field.
### Init: Bake the Texture
```python
# In GridLayer.__init__:
self._bg_row_idx = np.clip(
(np.arange(VH) - self.oy) // self.ch, 0, self.rows - 1
)
self._bg_col_idx = np.clip(
(np.arange(VW) - self.ox) // self.cw, 0, self.cols - 1
)
self._bg_textures = {}
def make_bg_texture(self, palette):
"""Pre-render a static ASCII texture (grayscale float32) once."""
if palette not in self._bg_textures:
texture = np.zeros((VH, VW), dtype=np.float32)
rng = random.Random(12345)
ch_list = [c for c in palette if c != " " and c in self.bm]
if not ch_list:
ch_list = list(self.bm.keys())[:5]
for row in range(self.rows):
y = self.oy + row * self.ch
if y + self.ch > VH:
break
for col in range(self.cols):
x = self.ox + col * self.cw
if x + self.cw > VW:
break
bm = self.bm[rng.choice(ch_list)]
texture[y:y+self.ch, x:x+self.cw] = bm
self._bg_textures[palette] = texture
return self._bg_textures[palette]
```
### Render: Color Field x Cached Texture
```python
def render_bg(self, color_field, palette=PAL_CIRCUIT):
"""Fast background: pre-rendered ASCII texture * per-cell color field.
color_field: (rows, cols, 3) uint8. Returns (VH, VW, 3) uint8."""
texture = self.make_bg_texture(palette)
# Expand cell colors to pixel coords via pre-computed index maps
color_px = color_field[
self._bg_row_idx[:, None], self._bg_col_idx[None, :]
].astype(np.float32)
return (texture[:, :, None] * color_px).astype(np.uint8)
```
### Usage in a Scene
```python
# Build per-cell color from effect fields (cheap — rows*cols, not VH*VW)
hue = ((t * 0.05 + val * 0.2) % 1.0).astype(np.float32)
R, G, B = hsv2rgb(hue, np.full_like(val, 0.5), val)
color_field = mkc(R, G, B, g.rows, g.cols) # (rows, cols, 3) uint8
# Render background — single matrix multiply, no per-cell loop
canvas_bg = g.render_bg(color_field, PAL_DENSE)
```
The texture init loop runs once and is cached per palette. Per-frame cost is one fancy-index lookup + one broadcast multiply — orders of magnitude faster than the per-cell bitmap blit loop in `render()` for dense backgrounds.
## Coordinate Array Caching
Pre-compute all grid-relative coordinate arrays at init, not per-frame:
@@ -215,8 +346,8 @@ all_rows = []
all_cols = []
all_fades = []
for c in range(cols):
head = int(state["ry"][c])
trail_len = state["rln"][c]
head = int(S["ry"][c])
trail_len = S["rln"][c]
for i in range(trail_len):
row = head - i
if 0 <= row < rows:
@@ -254,6 +385,57 @@ for fi in range(n_cols):
# Now map fire_val to chars and colors in one vectorized pass
```
## PIL String Rendering for Text-Heavy Scenes
Alternative to per-cell bitmap blitting when rendering many long text strings (scrolling tickers, typewriter sequences, idea floods). Uses PIL's native `ImageDraw.text()` which renders an entire string in one C call, vs one Python-loop bitmap blit per character.
Typical win: a scene with 56 ticker rows renders 56 PIL `text()` calls instead of ~10K individual bitmap blits.
Use when: scene renders many rows of readable text strings. NOT suitable for sparse or spatially-scattered single characters (use normal `render()` for those).
```python
from PIL import Image, ImageDraw
def render_text_layer(grid, rows_data, font):
"""Render dense text rows via PIL instead of per-cell bitmap blitting.
Args:
grid: GridLayer instance (for oy, ch, ox, font metrics)
rows_data: list of (row_index, text_string, rgb_tuple) — one per row
font: PIL ImageFont instance (grid.font)
Returns:
uint8 array (VH, VW, 3) — canvas with rendered text
"""
img = Image.new("RGB", (VW, VH), (0, 0, 0))
draw = ImageDraw.Draw(img)
for row_idx, text, color in rows_data:
y = grid.oy + row_idx * grid.ch
if y + grid.ch > VH:
break
draw.text((grid.ox, y), text, fill=color, font=font)
return np.array(img)
```
### Usage in a Ticker Scene
```python
# Build ticker data (text + color per row)
rows_data = []
for row in range(n_tickers):
text = build_ticker_text(row, t) # scrolling substring
color = hsv2rgb_scalar(hue, 0.85, bri) # (R, G, B) tuple
rows_data.append((row, text, color))
# One PIL pass instead of thousands of bitmap blits
canvas_tickers = render_text_layer(g_md, rows_data, g_md.font)
# Blend with other layers normally
result = blend_canvas(canvas_bg, canvas_tickers, "screen", 0.9)
```
This is purely a rendering optimization — same visual output, fewer draw calls. The grid's `render()` method is still needed for sparse character fields where characters are placed individually based on value fields.
## Bloom Optimization
**Do NOT use `scipy.ndimage.uniform_filter`** -- measured at 424ms/frame.
@@ -433,3 +615,82 @@ Scale with hardware. Baseline: 1080p, 24fps, ~180ms/frame/worker.
At 720p: multiply times by ~0.5. At 4K: multiply by ~4.
Heavier effects (many particles, dense grids, extra shader passes) add ~20-50%.
---
## Temp File Cleanup
Rendering generates intermediate files that accumulate across runs. Clean up after the final concat/mux step.
### Files to Clean
| File type | Source | Location |
|-----------|--------|----------|
| WAV extracts | `ffmpeg -i input.mp3 ... tmp.wav` | `tempfile.mktemp()` or project dir |
| Segment clips | `render_clip()` output | `segments/seg_00.mp4` etc. |
| Concat list | ffmpeg concat demuxer input | `segments/concat.txt` |
| ffmpeg stderr logs | piped to file for debugging | `*.log` in project dir |
| Feature cache | pickled numpy arrays | `*.pkl` or `*.npz` |
### Cleanup Function
```python
import glob
import tempfile
import shutil
def cleanup_render_artifacts(segments_dir="segments", keep_final=True):
"""Remove intermediate files after successful render.
Call this AFTER verifying the final output exists and plays correctly.
Args:
segments_dir: directory containing segment clips and concat list
keep_final: if True, only delete intermediates (not the final output)
"""
removed = []
# 1. Segment clips
if os.path.isdir(segments_dir):
shutil.rmtree(segments_dir)
removed.append(f"directory: {segments_dir}")
# 2. Temporary WAV files
for wav in glob.glob("*.wav"):
if wav.startswith("tmp") or wav.startswith("extracted_"):
os.remove(wav)
removed.append(wav)
# 3. ffmpeg stderr logs
for log in glob.glob("ffmpeg_*.log"):
os.remove(log)
removed.append(log)
# 4. Feature cache (optional — useful to keep for re-renders)
# for cache in glob.glob("features_*.npz"):
# os.remove(cache)
# removed.append(cache)
print(f"Cleaned {len(removed)} artifacts: {removed}")
return removed
```
### Integration with Render Pipeline
Call cleanup at the end of the main render script, after the final output is verified:
```python
# At end of main()
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
cleanup_render_artifacts(segments_dir="segments")
print(f"Done. Output: {output_path}")
else:
print("WARNING: final output missing or empty — skipping cleanup")
```
### Temp File Best Practices
- Use `tempfile.mkdtemp()` for segment directories — avoids polluting the project dir
- Name WAV extracts with `tempfile.mktemp(suffix=".wav")` so they're in the OS temp dir
- For debugging, set `KEEP_INTERMEDIATES=1` env var to skip cleanup
- Feature caches (`.npz`) are cheap to store and expensive to recompute — default to keeping them
@@ -1,5 +1,15 @@
# Scene System Reference
**Cross-references:**
- Grid system, palettes, color (HSV + OKLAB): `architecture.md`
- Effect building blocks (value fields, noise, SDFs, particles): `effects.md`
- `_render_vf()`, blend modes, tonemap, masking: `composition.md`
- Shader pipeline, feedback buffer, ShaderChain: `shaders.md`
- Complete scene examples at every complexity level: `examples.md`
- Input sources (audio features, video features): `inputs.md`
- Performance tuning, portrait CLI: `optimization.md`
- Common bugs (state leaks, frame drops): `troubleshooting.md`
Scenes are the top-level creative unit. Each scene is a time-bounded segment with its own effect function, shader chain, feedback configuration, and tone-mapping gamma.
## Scene Protocol (v2)
@@ -12,7 +22,7 @@ def fx_scene_name(r, f, t, S) -> canvas:
Args:
r: Renderer instance — access multiple grids via r.get_grid("sm")
f: dict of audio/video features, all values normalized to [0, 1]
t: time in seconds (global, not local to scene)
t: time in seconds local to scene (0.0 at scene start)
S: dict for persistent state (particles, rain columns, etc.)
Returns:
@@ -20,6 +30,20 @@ def fx_scene_name(r, f, t, S) -> canvas:
"""
```
**Local time convention:** Scene functions receive `t` starting at 0.0 for the first frame of the scene, regardless of where the scene appears in the timeline. The render loop subtracts the scene's start time before calling the function:
```python
# In render_clip:
t_local = fi / FPS - scene_start
canvas = fx_fn(r, feat, t_local, S)
```
This makes scenes reorderable without modifying their code. Compute scene progress as:
```python
progress = min(t / scene_duration, 1.0) # 0→1 over the scene
```
This replaces the v1 protocol where scenes returned `(chars, colors)` tuples. The v2 protocol gives scenes full control over multi-grid rendering and pixel-level composition internally.
### The Renderer Class
@@ -2,6 +2,15 @@
Post-processing effects applied to the pixel canvas (`numpy uint8 array, shape (H,W,3)`) after character rendering and before encoding. Also covers **pixel-level blend modes**, **feedback buffers**, and the **ShaderChain** compositor.
**Cross-references:**
- Grid system, palettes, color (HSV + OKLAB): `architecture.md`
- Effect building blocks (value fields, noise, SDFs): `effects.md`
- `_render_vf()`, blend modes, tonemap, masking: `composition.md`
- Scene protocol, render_clip, SCENES table: `scenes.md`
- Complete scene examples with shader usage: `examples.md`
- Performance tuning (frame budget, worker count): `optimization.md`
- Encoding pitfalls (ffmpeg flags, color space): `troubleshooting.md`
## Design Philosophy
The shader pipeline turns raw ASCII renders into cinematic output. The system is designed for **composability** — every shader, blend mode, and feedback transform is an independent building block. Combining them creates infinite visual variety from a small set of primitives.
@@ -1025,3 +1034,324 @@ cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
"-vf", f"fps={fps},scale={W}:{H}:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse",
"-loop", "0", output_gif]
```
### PNG Sequence
For frame-accurate editing, compositing in external tools (After Effects, Nuke), or lossless archival:
```python
import os
def output_png_sequence(frames, output_dir, W, H, fps, prefix="frame"):
"""Write frames as numbered PNGs. frames = iterable of uint8 (H,W,3) arrays."""
os.makedirs(output_dir, exist_ok=True)
# Method 1: Direct PIL write (no ffmpeg dependency)
from PIL import Image
for i, frame in enumerate(frames):
img = Image.fromarray(frame)
img.save(os.path.join(output_dir, f"{prefix}_{i:06d}.png"))
# Method 2: ffmpeg pipe (faster for large sequences)
cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{W}x{H}", "-r", str(fps), "-i", "pipe:0",
os.path.join(output_dir, f"{prefix}_%06d.png")]
```
Reassemble PNG sequence to video:
```bash
ffmpeg -framerate 24 -i frame_%06d.png -c:v libx264 -crf 18 -pix_fmt yuv420p output.mp4
```
### Alpha Channel / Transparent Background (RGBA)
For compositing ASCII art over other video or images. Uses RGBA canvas (4 channels) instead of RGB (3 channels):
```python
def create_rgba_canvas(H, W):
"""Transparent canvas — alpha channel starts at 0 (fully transparent)."""
return np.zeros((H, W, 4), dtype=np.uint8)
def render_char_rgba(canvas, row, col, char_img, color_rgb, alpha=255):
"""Render a character with alpha. char_img = PIL glyph mask (grayscale).
Alpha comes from the glyph mask — background stays transparent."""
r, g, b = color_rgb
y0, x0 = row * cell_h, col * cell_w
mask = np.array(char_img) # grayscale 0-255
canvas[y0:y0+cell_h, x0:x0+cell_w, 0] = np.maximum(canvas[y0:y0+cell_h, x0:x0+cell_w, 0], (mask * r / 255).astype(np.uint8))
canvas[y0:y0+cell_h, x0:x0+cell_w, 1] = np.maximum(canvas[y0:y0+cell_h, x0:x0+cell_w, 1], (mask * g / 255).astype(np.uint8))
canvas[y0:y0+cell_h, x0:x0+cell_w, 2] = np.maximum(canvas[y0:y0+cell_h, x0:x0+cell_w, 2], (mask * b / 255).astype(np.uint8))
canvas[y0:y0+cell_h, x0:x0+cell_w, 3] = np.maximum(canvas[y0:y0+cell_h, x0:x0+cell_w, 3], mask)
def blend_onto_background(rgba_canvas, bg_rgb):
"""Composite RGBA canvas over a solid or image background."""
alpha = rgba_canvas[:, :, 3:4].astype(np.float32) / 255.0
fg = rgba_canvas[:, :, :3].astype(np.float32)
bg = bg_rgb.astype(np.float32)
result = fg * alpha + bg * (1.0 - alpha)
return result.astype(np.uint8)
```
RGBA output via ffmpeg (ProRes 4444 for editing, WebM VP9 for web):
```bash
# ProRes 4444 — preserves alpha, widely supported in NLEs
ffmpeg -y -f rawvideo -pix_fmt rgba -s {W}x{H} -r {fps} -i pipe:0 \
-c:v prores_ks -profile:v 4444 -pix_fmt yuva444p10le output.mov
# WebM VP9 — alpha support for web/browser compositing
ffmpeg -y -f rawvideo -pix_fmt rgba -s {W}x{H} -r {fps} -i pipe:0 \
-c:v libvpx-vp9 -pix_fmt yuva420p -crf 30 -b:v 0 output.webm
# PNG sequence with alpha (lossless)
ffmpeg -y -f rawvideo -pix_fmt rgba -s {W}x{H} -r {fps} -i pipe:0 \
frame_%06d.png
```
**Key constraint**: shaders that operate on `(H,W,3)` arrays need adaptation for RGBA. Either apply shaders to the RGB channels only and preserve alpha, or write RGBA-aware versions:
```python
def apply_shader_rgba(canvas_rgba, shader_fn, **kwargs):
"""Apply an RGB shader to the color channels of an RGBA canvas."""
rgb = canvas_rgba[:, :, :3]
alpha = canvas_rgba[:, :, 3:4]
rgb_out = shader_fn(rgb, **kwargs)
return np.concatenate([rgb_out, alpha], axis=2)
```
---
## Real-Time Terminal Rendering
Live ASCII display in the terminal using ANSI escape codes. Useful for previewing scenes during development, live performances, and interactive parameter tuning.
### ANSI Color Escape Codes
```python
def rgb_to_ansi(r, g, b):
"""24-bit true color ANSI escape (supported by most modern terminals)."""
return f"\033[38;2;{r};{g};{b}m"
ANSI_RESET = "\033[0m"
ANSI_CLEAR = "\033[2J\033[H" # clear screen + cursor home
ANSI_HIDE_CURSOR = "\033[?25l"
ANSI_SHOW_CURSOR = "\033[?25h"
```
### Frame-to-ANSI Conversion
```python
def frame_to_ansi(chars, colors):
"""Convert char+color arrays to a single ANSI string for terminal output.
Args:
chars: (rows, cols) array of single characters
colors: (rows, cols, 3) uint8 RGB array
Returns:
str: ANSI-encoded frame ready for sys.stdout.write()
"""
rows, cols = chars.shape
lines = []
for r in range(rows):
parts = []
prev_color = None
for c in range(cols):
rgb = tuple(colors[r, c])
ch = chars[r, c]
if ch == " " or rgb == (0, 0, 0):
parts.append(" ")
else:
if rgb != prev_color:
parts.append(rgb_to_ansi(*rgb))
prev_color = rgb
parts.append(ch)
parts.append(ANSI_RESET)
lines.append("".join(parts))
return "\n".join(lines)
```
### Optimized: Delta Updates
Only redraw characters that changed since the last frame. Eliminates redundant terminal writes for static regions:
```python
def frame_to_ansi_delta(chars, colors, prev_chars, prev_colors):
"""Emit ANSI escapes only for cells that changed."""
rows, cols = chars.shape
parts = []
for r in range(rows):
for c in range(cols):
if (chars[r, c] != prev_chars[r, c] or
not np.array_equal(colors[r, c], prev_colors[r, c])):
parts.append(f"\033[{r+1};{c+1}H") # move cursor
rgb = tuple(colors[r, c])
parts.append(rgb_to_ansi(*rgb))
parts.append(chars[r, c])
return "".join(parts)
```
### Live Render Loop
```python
import sys
import time
def render_live(scene_fn, r, fps=24, duration=None):
"""Render a scene function live in the terminal.
Args:
scene_fn: v2 scene function (r, f, t, S) -> canvas
OR v1-style function that populates a grid
r: Renderer instance
fps: target frame rate
duration: seconds to run (None = run until Ctrl+C)
"""
frame_time = 1.0 / fps
S = {}
f = {} # synthesize features or connect to live audio
sys.stdout.write(ANSI_HIDE_CURSOR + ANSI_CLEAR)
sys.stdout.flush()
t0 = time.monotonic()
frame_count = 0
try:
while True:
t = time.monotonic() - t0
if duration and t > duration:
break
# Synthesize features from time (or connect to live audio via pyaudio)
f = synthesize_features(t)
# Render scene — for terminal, use a small grid
g = r.get_grid("sm")
# Option A: v2 scene → extract chars/colors from canvas (reverse render)
# Option B: call effect functions directly for chars/colors
canvas = scene_fn(r, f, t, S)
# For terminal display, render chars+colors directly
# (bypassing the pixel canvas — terminal uses character cells)
chars, colors = scene_to_terminal(scene_fn, r, f, t, S, g)
frame_str = ANSI_CLEAR + frame_to_ansi(chars, colors)
sys.stdout.write(frame_str)
sys.stdout.flush()
# Frame timing
elapsed = time.monotonic() - t0 - (frame_count * frame_time)
sleep_time = frame_time - elapsed
if sleep_time > 0:
time.sleep(sleep_time)
frame_count += 1
except KeyboardInterrupt:
pass
finally:
sys.stdout.write(ANSI_SHOW_CURSOR + ANSI_RESET + "\n")
sys.stdout.flush()
def scene_to_terminal(scene_fn, r, f, t, S, g):
"""Run effect functions and return (chars, colors) for terminal display.
For terminal mode, skip the pixel canvas and work with character arrays directly."""
# Effects that return (chars, colors) work directly
# For vf-based effects, render the value field + hue field to chars/colors:
val = vf_plasma(g, f, t, S)
hue = hf_time_cycle(0.08)(g, t)
mask = val > 0.03
chars = val2char(val, mask, PAL_DENSE)
R, G, B = hsv2rgb(hue, np.full_like(val, 0.8), val)
colors = mkc(R, G, B, g.rows, g.cols)
return chars, colors
```
### Curses-Based Rendering (More Robust)
For full-featured terminal UIs with proper resize handling and input:
```python
import curses
def render_curses(scene_fn, r, fps=24):
"""Curses-based live renderer with resize handling and key input."""
def _main(stdscr):
curses.start_color()
curses.use_default_colors()
curses.curs_set(0) # hide cursor
stdscr.nodelay(True) # non-blocking input
# Initialize color pairs (curses supports 256 colors)
# Map RGB to nearest curses color pair
color_cache = {}
next_pair = [1]
def get_color_pair(r, g, b):
key = (r >> 4, g >> 4, b >> 4) # quantize to reduce pairs
if key not in color_cache:
if next_pair[0] < curses.COLOR_PAIRS - 1:
ci = 16 + (r // 51) * 36 + (g // 51) * 6 + (b // 51) # 6x6x6 cube
curses.init_pair(next_pair[0], ci, -1)
color_cache[key] = next_pair[0]
next_pair[0] += 1
else:
return 0
return curses.color_pair(color_cache[key])
S = {}
f = {}
frame_time = 1.0 / fps
t0 = time.monotonic()
while True:
t = time.monotonic() - t0
f = synthesize_features(t)
# Adapt grid to terminal size
max_y, max_x = stdscr.getmaxyx()
g = r.get_grid_for_size(max_x, max_y) # dynamic grid sizing
chars, colors = scene_to_terminal(scene_fn, r, f, t, S, g)
rows, cols = chars.shape
for row in range(min(rows, max_y - 1)):
for col in range(min(cols, max_x - 1)):
ch = chars[row, col]
rgb = tuple(colors[row, col])
try:
stdscr.addch(row, col, ch, get_color_pair(*rgb))
except curses.error:
pass # ignore writes outside terminal bounds
stdscr.refresh()
# Handle input
key = stdscr.getch()
if key == ord('q'):
break
time.sleep(max(0, frame_time - (time.monotonic() - t0 - t)))
curses.wrapper(_main)
```
### Terminal Rendering Constraints
| Constraint | Value | Notes |
|-----------|-------|-------|
| Max practical grid | ~200x60 | Depends on terminal size |
| Color support | 24-bit (modern), 256 (fallback), 16 (minimal) | Check `$COLORTERM` for truecolor |
| Frame rate ceiling | ~30 fps | Terminal I/O is the bottleneck |
| Delta updates | 2-5x faster | Only worth it when <30% of cells change per frame |
| SSH latency | Kills performance | Local terminals only for real-time |
**Detect color support:**
```python
import os
def get_terminal_color_depth():
ct = os.environ.get("COLORTERM", "")
if ct in ("truecolor", "24bit"):
return 24
term = os.environ.get("TERM", "")
if "256color" in term:
return 8 # 256 colors
return 4 # 16 colors basic ANSI
```
@@ -1,5 +1,15 @@
# Troubleshooting Reference
**Cross-references:**
- Grid system, palettes, font selection: `architecture.md`
- Effect building blocks (value fields, noise, SDFs): `effects.md`
- `_render_vf()`, blend modes, tonemap: `composition.md`
- Scene protocol, render_clip, SCENES table: `scenes.md`
- Shader pipeline, feedback buffer, encoding: `shaders.md`
- Input sources (audio, video, TTS): `inputs.md`
- Performance tuning, hardware detection: `optimization.md`
- Complete scene examples: `examples.md`
Common bugs, gotchas, and platform-specific issues encountered during ASCII video development.
## NumPy Broadcasting
+297
View File
@@ -0,0 +1,297 @@
---
name: linear
description: Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies.
version: 1.0.0
author: Hermes Agent
license: MIT
prerequisites:
env_vars: [LINEAR_API_KEY]
commands: [curl]
metadata:
hermes:
tags: [Linear, Project Management, Issues, GraphQL, API, Productivity]
---
# Linear — Issue & Project Management
Manage Linear issues, projects, and teams directly via the GraphQL API using `curl`. No MCP server, no OAuth flow, no extra dependencies.
## Setup
1. Get a personal API key from **Linear Settings > API > Personal API keys**
2. Set `LINEAR_API_KEY` in your environment (via `hermes setup` or your env config)
## API Basics
- **Endpoint:** `https://api.linear.app/graphql` (POST)
- **Auth header:** `Authorization: $LINEAR_API_KEY` (no "Bearer" prefix for API keys)
- **All requests are POST** with `Content-Type: application/json`
- **Both UUIDs and short identifiers** (e.g., `ENG-123`) work for `issue(id:)`
Base curl pattern:
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ viewer { id name } }"}' | python3 -m json.tool
```
## Workflow States
Linear uses `WorkflowState` objects with a `type` field. **6 state types:**
| Type | Description |
|------|-------------|
| `triage` | Incoming issues needing review |
| `backlog` | Acknowledged but not yet planned |
| `unstarted` | Planned/ready but not started |
| `started` | Actively being worked on |
| `completed` | Done |
| `canceled` | Won't do |
Each team has its own named states (e.g., "In Progress" is type `started`). To change an issue's status, you need the `stateId` (UUID) of the target state — query workflow states first.
**Priority values:** 0 = None, 1 = Urgent, 2 = High, 3 = Medium, 4 = Low
## Common Queries
### Get current user
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ viewer { id name email } }"}' | python3 -m json.tool
```
### List teams
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ teams { nodes { id name key } } }"}' | python3 -m json.tool
```
### List workflow states for a team
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ workflowStates(filter: { team: { key: { eq: \"ENG\" } } }) { nodes { id name type } } }"}' | python3 -m json.tool
```
### List issues (first 20)
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issues(first: 20) { nodes { identifier title priority state { name type } assignee { name } team { key } url } pageInfo { hasNextPage endCursor } } }"}' | python3 -m json.tool
```
### List my assigned issues
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ viewer { assignedIssues(first: 25) { nodes { identifier title state { name type } priority url } } } }"}' | python3 -m json.tool
```
### Get a single issue (by identifier like ENG-123)
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issue(id: \"ENG-123\") { id identifier title description priority state { id name type } assignee { id name } team { key } project { name } labels { nodes { name } } comments { nodes { body user { name } createdAt } } url } }"}' | python3 -m json.tool
```
### Search issues by text
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issueSearch(query: \"bug login\", first: 10) { nodes { identifier title state { name } assignee { name } url } } }"}' | python3 -m json.tool
```
### Filter issues by state type
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issues(filter: { state: { type: { in: [\"started\"] } } }, first: 20) { nodes { identifier title state { name } assignee { name } } } }"}' | python3 -m json.tool
```
### Filter by team and assignee
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issues(filter: { team: { key: { eq: \"ENG\" } }, assignee: { email: { eq: \"user@example.com\" } } }, first: 20) { nodes { identifier title state { name } priority } } }"}' | python3 -m json.tool
```
### List projects
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ projects(first: 20) { nodes { id name description progress lead { name } teams { nodes { key } } url } } }"}' | python3 -m json.tool
```
### List team members
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ users { nodes { id name email active } } }"}' | python3 -m json.tool
```
### List labels
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issueLabels { nodes { id name color } } }"}' | python3 -m json.tool
```
## Common Mutations
### Create an issue
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"query": "mutation($input: IssueCreateInput!) { issueCreate(input: $input) { success issue { id identifier title url } } }",
"variables": {
"input": {
"teamId": "TEAM_UUID",
"title": "Fix login bug",
"description": "Users cannot login with SSO",
"priority": 2
}
}
}' | python3 -m json.tool
```
### Update issue status
First get the target state UUID from the workflow states query above, then:
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { stateId: \"STATE_UUID\" }) { success issue { identifier state { name type } } } }"}' | python3 -m json.tool
```
### Assign an issue
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { assigneeId: \"USER_UUID\" }) { success issue { identifier assignee { name } } } }"}' | python3 -m json.tool
```
### Set priority
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { priority: 1 }) { success issue { identifier priority } } }"}' | python3 -m json.tool
```
### Add a comment
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { commentCreate(input: { issueId: \"ISSUE_UUID\", body: \"Investigated. Root cause is X.\" }) { success comment { id body } } }"}' | python3 -m json.tool
```
### Set due date
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { dueDate: \"2026-04-01\" }) { success issue { identifier dueDate } } }"}' | python3 -m json.tool
```
### Add labels to an issue
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { labelIds: [\"LABEL_UUID_1\", \"LABEL_UUID_2\"] }) { success issue { identifier labels { nodes { name } } } } }"}' | python3 -m json.tool
```
### Add issue to a project
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "mutation { issueUpdate(id: \"ENG-123\", input: { projectId: \"PROJECT_UUID\" }) { success issue { identifier project { name } } } }"}' | python3 -m json.tool
```
### Create a project
```bash
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"query": "mutation($input: ProjectCreateInput!) { projectCreate(input: $input) { success project { id name url } } }",
"variables": {
"input": {
"name": "Q2 Auth Overhaul",
"description": "Replace legacy auth with OAuth2 and PKCE",
"teamIds": ["TEAM_UUID"]
}
}
}' | python3 -m json.tool
```
## Pagination
Linear uses Relay-style cursor pagination:
```bash
# First page
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issues(first: 20) { nodes { identifier title } pageInfo { hasNextPage endCursor } } }"}' | python3 -m json.tool
# Next page — use endCursor from previous response
curl -s -X POST https://api.linear.app/graphql \
-H "Authorization: $LINEAR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "{ issues(first: 20, after: \"CURSOR_FROM_PREVIOUS\") { nodes { identifier title } pageInfo { hasNextPage endCursor } } }"}' | python3 -m json.tool
```
Default page size: 50. Max: 250. Always use `first: N` to limit results.
## Filtering Reference
Comparators: `eq`, `neq`, `in`, `nin`, `lt`, `lte`, `gt`, `gte`, `contains`, `startsWith`, `containsIgnoreCase`
Combine filters with `or: [...]` for OR logic (default is AND within a filter object).
## Typical Workflow
1. **Query teams** to get team IDs and keys
2. **Query workflow states** for target team to get state UUIDs
3. **List or search issues** to find what needs work
4. **Create issues** with team ID, title, description, priority
5. **Update status** by setting `stateId` to the target workflow state
6. **Add comments** to track progress
7. **Mark complete** by setting `stateId` to the team's "completed" type state
## Rate Limits
- 5,000 requests/hour per API key
- 3,000,000 complexity points/hour
- Use `first: N` to limit results and reduce complexity cost
- Monitor `X-RateLimit-Requests-Remaining` response header
## Important Notes
- Always use `terminal` tool with `curl` for API calls — do NOT use `web_extract` or `browser`
- Always check the `errors` array in GraphQL responses — HTTP 200 can still contain errors
- If `stateId` is omitted when creating issues, Linear defaults to the first backlog state
- The `description` field supports Markdown
- Use `python3 -m json.tool` or `jq` to format JSON responses for readability
+3
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@@ -0,0 +1,3 @@
---
description: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
---
+202
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@@ -0,0 +1,202 @@
---
name: xitter
description: Interact with X/Twitter via the x-cli terminal client using official X API credentials. Use for posting, reading timelines, searching tweets, liking, retweeting, bookmarks, mentions, and user lookups.
version: 1.0.0
author: Siddharth Balyan + Hermes Agent
license: MIT
platforms: [linux, macos]
prerequisites:
commands: [uv]
env_vars: [X_API_KEY, X_API_SECRET, X_BEARER_TOKEN, X_ACCESS_TOKEN, X_ACCESS_TOKEN_SECRET]
metadata:
hermes:
tags: [twitter, x, social-media, x-cli]
homepage: https://github.com/Infatoshi/x-cli
---
# Xitter — X/Twitter via x-cli
Use `x-cli` for official X/Twitter API interactions from the terminal.
This skill is for:
- posting tweets, replies, and quote tweets
- searching tweets and reading timelines
- looking up users, followers, and following
- liking and retweeting
- checking mentions and bookmarks
This skill intentionally does not vendor a separate CLI implementation into Hermes. Install and use upstream `x-cli` instead.
## Important Cost / Access Note
X API access is not meaningfully free for most real usage. Expect to need paid or prepaid X developer access. If commands fail with permissions or quota errors, check your X developer plan first.
## Install
Install upstream `x-cli` with `uv`:
```bash
uv tool install git+https://github.com/Infatoshi/x-cli.git
```
Upgrade later with:
```bash
uv tool upgrade x-cli
```
Verify:
```bash
x-cli --help
```
## Credentials
You need these five values from the X Developer Portal:
- `X_API_KEY`
- `X_API_SECRET`
- `X_BEARER_TOKEN`
- `X_ACCESS_TOKEN`
- `X_ACCESS_TOKEN_SECRET`
Get them from:
- https://developer.x.com/en/portal/dashboard
### Why does X need 5 secrets?
Unfortunately, the official X API splits auth across both app-level and user-level credentials:
- `X_API_KEY` + `X_API_SECRET` identify your app
- `X_BEARER_TOKEN` is used for app-level read access
- `X_ACCESS_TOKEN` + `X_ACCESS_TOKEN_SECRET` let the CLI act as your user account for writes and authenticated actions
So yes — it is a lot of secrets for one integration, but this is the stable official API path and is still preferable to cookie/session scraping.
Setup requirements in the portal:
1. Create or open your app
2. In user authentication settings, set permissions to `Read and write`
3. Generate or regenerate the access token + access token secret after enabling write permissions
4. Save all five values carefully — missing any one of them will usually produce confusing auth or permission errors
Note: upstream `x-cli` expects the full credential set to be present, so even if you mostly care about read-only commands, it is simplest to configure all five.
## Cost / Friction Reality Check
If this setup feels heavier than it should be, that is because it is. Xs official developer flow is high-friction and often paid. This skill chooses the official API path because it is more stable and maintainable than browser-cookie/session approaches.
If the user wants the least brittle long-term setup, use this skill. If they want a zero-setup or unofficial path, that is a different trade-off and not what this skill is for.
## Where to Store Credentials
`x-cli` looks for credentials in `~/.config/x-cli/.env`.
If you already keep your X credentials in `~/.hermes/.env`, the cleanest setup is:
```bash
mkdir -p ~/.config/x-cli
ln -sf ~/.hermes/.env ~/.config/x-cli/.env
```
Or create a dedicated file:
```bash
mkdir -p ~/.config/x-cli
cat > ~/.config/x-cli/.env <<'EOF'
X_API_KEY=your_consumer_key
X_API_SECRET=your_secret_key
X_BEARER_TOKEN=your_bearer_token
X_ACCESS_TOKEN=your_access_token
X_ACCESS_TOKEN_SECRET=your_access_token_secret
EOF
chmod 600 ~/.config/x-cli/.env
```
## Quick Verification
```bash
x-cli user get openai
x-cli tweet search "from:NousResearch" --max 3
x-cli me mentions --max 5
```
If reads work but writes fail, regenerate the access token after confirming `Read and write` permissions.
## Common Commands
### Tweets
```bash
x-cli tweet post "hello world"
x-cli tweet get https://x.com/user/status/1234567890
x-cli tweet delete 1234567890
x-cli tweet reply 1234567890 "nice post"
x-cli tweet quote 1234567890 "worth reading"
x-cli tweet search "AI agents" --max 20
x-cli tweet metrics 1234567890
```
### Users
```bash
x-cli user get openai
x-cli user timeline openai --max 10
x-cli user followers openai --max 50
x-cli user following openai --max 50
```
### Self / Authenticated User
```bash
x-cli me mentions --max 20
x-cli me bookmarks --max 20
x-cli me bookmark 1234567890
x-cli me unbookmark 1234567890
```
### Quick Actions
```bash
x-cli like 1234567890
x-cli retweet 1234567890
```
## Output Modes
Use structured output when the agent needs to inspect fields programmatically:
```bash
x-cli -j tweet search "AI agents" --max 5
x-cli -p user get openai
x-cli -md tweet get 1234567890
x-cli -v -j tweet get 1234567890
```
Recommended defaults:
- `-j` for machine-readable output
- `-v` when you need timestamps, metrics, or metadata
- plain/default mode for quick human inspection
## Agent Workflow
1. Confirm `x-cli` is installed
2. Confirm credentials are present
3. Start with a read command (`user get`, `tweet search`, `me mentions`)
4. Use `-j` when extracting fields for later steps
5. Only perform write actions after confirming the target tweet/user and the user's intent
## Pitfalls
- **Paid API access**: many failures are plan/permission problems, not code problems.
- **403 oauth1-permissions**: regenerate the access token after enabling `Read and write`.
- **Reply restrictions**: X restricts many programmatic replies. `tweet quote` is often more reliable than `tweet reply`.
- **Rate limits**: expect per-endpoint limits and cooldown windows.
- **Credential drift**: if you rotate tokens in `~/.hermes/.env`, make sure `~/.config/x-cli/.env` still points at the current file.
## Notes
- Prefer official API workflows over cookie/session scraping.
- Use tweet URLs or IDs interchangeably — `x-cli` accepts both.
- If bookmark behavior changes upstream, check the upstream README first:
https://github.com/Infatoshi/x-cli
+18 -7
View File
@@ -129,6 +129,7 @@ class TestGetTextAuxiliaryClient:
def test_custom_endpoint_over_codex(self, monkeypatch, codex_auth_dir):
monkeypatch.setenv("OPENAI_BASE_URL", "http://localhost:1234/v1")
monkeypatch.setenv("OPENAI_API_KEY", "lm-studio-key")
monkeypatch.setenv("OPENAI_MODEL", "my-local-model")
# Override the autouse monkeypatch for codex
monkeypatch.setattr(
"agent.auxiliary_client._read_codex_access_token",
@@ -137,7 +138,7 @@ class TestGetTextAuxiliaryClient:
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client.OpenAI") as mock_openai:
client, model = get_text_auxiliary_client()
assert model == "gpt-4o-mini"
assert model == "my-local-model"
call_kwargs = mock_openai.call_args
assert call_kwargs.kwargs["base_url"] == "http://localhost:1234/v1"
@@ -150,9 +151,13 @@ class TestGetTextAuxiliaryClient:
from agent.auxiliary_client import CodexAuxiliaryClient
assert isinstance(client, CodexAuxiliaryClient)
def test_returns_none_when_nothing_available(self):
def test_returns_none_when_nothing_available(self, monkeypatch):
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.delenv("OPENAI_API_KEY", raising=False)
monkeypatch.delenv("OPENROUTER_API_KEY", raising=False)
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client._read_codex_access_token", return_value=None):
patch("agent.auxiliary_client._read_codex_access_token", return_value=None), \
patch("agent.auxiliary_client._resolve_api_key_provider", return_value=(None, None)):
client, model = get_text_auxiliary_client()
assert client is None
assert model is None
@@ -209,17 +214,21 @@ class TestVisionClientFallback:
monkeypatch.setenv("AUXILIARY_VISION_PROVIDER", "main")
monkeypatch.setenv("OPENAI_BASE_URL", "http://localhost:1234/v1")
monkeypatch.setenv("OPENAI_API_KEY", "local-key")
monkeypatch.setenv("OPENAI_MODEL", "my-local-model")
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client.OpenAI") as mock_openai:
client, model = get_vision_auxiliary_client()
assert client is not None
assert model == "gpt-4o-mini"
assert model == "my-local-model"
def test_vision_forced_main_returns_none_without_creds(self, monkeypatch):
"""Forced main with no credentials still returns None."""
monkeypatch.setenv("AUXILIARY_VISION_PROVIDER", "main")
monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
monkeypatch.delenv("OPENAI_API_KEY", raising=False)
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client._read_codex_access_token", return_value=None):
patch("agent.auxiliary_client._read_codex_access_token", return_value=None), \
patch("agent.auxiliary_client._resolve_api_key_provider", return_value=(None, None)):
client, model = get_vision_auxiliary_client()
assert client is None
assert model is None
@@ -305,21 +314,23 @@ class TestResolveForcedProvider:
def test_forced_main_uses_custom(self, monkeypatch):
monkeypatch.setenv("OPENAI_BASE_URL", "http://local:8080/v1")
monkeypatch.setenv("OPENAI_API_KEY", "local-key")
monkeypatch.setenv("OPENAI_MODEL", "my-local-model")
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client.OpenAI") as mock_openai:
client, model = _resolve_forced_provider("main")
assert model == "gpt-4o-mini"
assert model == "my-local-model"
def test_forced_main_skips_openrouter_nous(self, monkeypatch):
"""Even if OpenRouter key is set, 'main' skips it."""
monkeypatch.setenv("OPENROUTER_API_KEY", "or-key")
monkeypatch.setenv("OPENAI_BASE_URL", "http://local:8080/v1")
monkeypatch.setenv("OPENAI_API_KEY", "local-key")
monkeypatch.setenv("OPENAI_MODEL", "my-local-model")
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
patch("agent.auxiliary_client.OpenAI") as mock_openai:
client, model = _resolve_forced_provider("main")
# Should use custom endpoint, not OpenRouter
assert model == "gpt-4o-mini"
assert model == "my-local-model"
def test_forced_main_falls_to_codex(self, codex_auth_dir, monkeypatch):
with patch("agent.auxiliary_client._read_nous_auth", return_value=None), \
+41
View File
@@ -153,6 +153,47 @@ class TestGenerateSummaryNoneContent:
assert len(result) < len(msgs)
class TestNonStringContent:
"""Regression: content as dict (e.g., llama.cpp tool calls) must not crash."""
def test_dict_content_coerced_to_string(self):
mock_response = MagicMock()
mock_response.choices = [MagicMock()]
mock_response.choices[0].message.content = {"text": "some summary"}
with patch("agent.context_compressor.get_model_context_length", return_value=100000):
c = ContextCompressor(model="test", quiet_mode=True)
messages = [
{"role": "user", "content": "do something"},
{"role": "assistant", "content": "ok"},
]
with patch("agent.context_compressor.call_llm", return_value=mock_response):
summary = c._generate_summary(messages)
assert isinstance(summary, str)
assert "CONTEXT SUMMARY" in summary
def test_none_content_coerced_to_empty(self):
mock_response = MagicMock()
mock_response.choices = [MagicMock()]
mock_response.choices[0].message.content = None
with patch("agent.context_compressor.get_model_context_length", return_value=100000):
c = ContextCompressor(model="test", quiet_mode=True)
messages = [
{"role": "user", "content": "do something"},
{"role": "assistant", "content": "ok"},
]
with patch("agent.context_compressor.call_llm", return_value=mock_response):
summary = c._generate_summary(messages)
# None content → empty string → "[CONTEXT SUMMARY]: " prefix added
assert summary is not None
assert "CONTEXT SUMMARY" in summary
class TestCompressWithClient:
def test_summarization_path(self):
mock_client = MagicMock()
+8
View File
@@ -23,6 +23,14 @@ class TestApplyCacheMarker:
_apply_cache_marker(msg, MARKER)
assert msg["cache_control"] == MARKER
def test_empty_string_content_gets_top_level_marker(self):
"""Empty text blocks cannot have cache_control (Anthropic rejects them)."""
msg = {"role": "assistant", "content": ""}
_apply_cache_marker(msg, MARKER)
assert msg["cache_control"] == MARKER
# Must NOT wrap into [{"type": "text", "text": "", "cache_control": ...}]
assert msg["content"] == ""
def test_string_content_wrapped_in_list(self):
msg = {"role": "user", "content": "Hello"}
_apply_cache_marker(msg, MARKER)
+6 -5
View File
@@ -1,6 +1,7 @@
"""Tests for gateway/channel_directory.py — channel resolution and display."""
import json
import os
from pathlib import Path
from unittest.mock import patch
@@ -122,7 +123,7 @@ class TestResolveChannelName:
class TestBuildFromSessions:
def _write_sessions(self, tmp_path, sessions_data):
"""Write sessions.json at the path _build_from_sessions expects."""
sessions_path = tmp_path / ".hermes" / "sessions" / "sessions.json"
sessions_path = tmp_path / "sessions" / "sessions.json"
sessions_path.parent.mkdir(parents=True)
sessions_path.write_text(json.dumps(sessions_data))
@@ -152,7 +153,7 @@ class TestBuildFromSessions:
},
})
with patch.object(Path, "home", return_value=tmp_path):
with patch.dict(os.environ, {"HERMES_HOME": str(tmp_path)}):
entries = _build_from_sessions("telegram")
assert len(entries) == 2
@@ -161,7 +162,7 @@ class TestBuildFromSessions:
assert "Bob" in names
def test_missing_sessions_file(self, tmp_path):
with patch.object(Path, "home", return_value=tmp_path):
with patch.dict(os.environ, {"HERMES_HOME": str(tmp_path)}):
entries = _build_from_sessions("telegram")
assert entries == []
@@ -171,7 +172,7 @@ class TestBuildFromSessions:
"s2": {"origin": {"platform": "telegram", "chat_id": "123", "chat_name": "X"}},
})
with patch.object(Path, "home", return_value=tmp_path):
with patch.dict(os.environ, {"HERMES_HOME": str(tmp_path)}):
entries = _build_from_sessions("telegram")
assert len(entries) == 1
@@ -202,7 +203,7 @@ class TestBuildFromSessions:
},
})
with patch.object(Path, "home", return_value=tmp_path):
with patch.dict(os.environ, {"HERMES_HOME": str(tmp_path)}):
entries = _build_from_sessions("telegram")
ids = {entry["id"] for entry in entries}
+148
View File
@@ -0,0 +1,148 @@
"""Tests for the config.yaml → env var bridge logic in gateway/run.py.
Specifically tests that top-level `cwd:` and `backend:` in config.yaml
are correctly bridged to TERMINAL_CWD / TERMINAL_ENV env vars as
convenience aliases for `terminal.cwd` / `terminal.backend`.
The bridge logic is module-level code in gateway/run.py, so we test
the semantics by reimplementing the relevant config bridge snippet and
asserting the expected env var outcomes.
"""
import os
import json
import pytest
def _simulate_config_bridge(cfg: dict, initial_env: dict | None = None):
"""Simulate the gateway config bridge logic from gateway/run.py.
Returns the resulting env dict (only TERMINAL_* and MESSAGING_CWD keys).
"""
env = dict(initial_env or {})
# --- Replicate lines 54-56: generic top-level bridge (for context) ---
for key, val in cfg.items():
if isinstance(val, (str, int, float, bool)) and key not in env:
env[key] = str(val)
# --- Replicate lines 59-87: terminal config bridge ---
terminal_cfg = cfg.get("terminal", {})
if terminal_cfg and isinstance(terminal_cfg, dict):
terminal_env_map = {
"backend": "TERMINAL_ENV",
"cwd": "TERMINAL_CWD",
"timeout": "TERMINAL_TIMEOUT",
}
for cfg_key, env_var in terminal_env_map.items():
if cfg_key in terminal_cfg:
val = terminal_cfg[cfg_key]
if isinstance(val, list):
env[env_var] = json.dumps(val)
else:
env[env_var] = str(val)
# --- NEW: top-level aliases (the fix being tested) ---
top_level_aliases = {
"cwd": "TERMINAL_CWD",
"backend": "TERMINAL_ENV",
}
for alias_key, alias_env in top_level_aliases.items():
if alias_env not in env:
alias_val = cfg.get(alias_key)
if isinstance(alias_val, str) and alias_val.strip():
env[alias_env] = alias_val.strip()
# --- Replicate lines 144-147: MESSAGING_CWD fallback ---
configured_cwd = env.get("TERMINAL_CWD", "")
if not configured_cwd or configured_cwd in (".", "auto", "cwd"):
messaging_cwd = env.get("MESSAGING_CWD") or "/root" # Path.home() for root
env["TERMINAL_CWD"] = messaging_cwd
return env
class TestTopLevelCwdAlias:
"""Top-level `cwd:` should be treated as `terminal.cwd`."""
def test_top_level_cwd_sets_terminal_cwd(self):
cfg = {"cwd": "/home/hermes/projects"}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_CWD"] == "/home/hermes/projects"
def test_top_level_backend_sets_terminal_env(self):
cfg = {"backend": "docker"}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_ENV"] == "docker"
def test_top_level_cwd_and_backend(self):
cfg = {"backend": "local", "cwd": "/home/hermes/projects"}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_CWD"] == "/home/hermes/projects"
assert result["TERMINAL_ENV"] == "local"
def test_nested_terminal_takes_precedence_over_top_level(self):
"""terminal.cwd should win over top-level cwd."""
cfg = {
"cwd": "/should/not/use",
"terminal": {"cwd": "/home/hermes/real"},
}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_CWD"] == "/home/hermes/real"
def test_nested_terminal_backend_takes_precedence(self):
cfg = {
"backend": "should-not-use",
"terminal": {"backend": "docker"},
}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_ENV"] == "docker"
def test_no_cwd_falls_back_to_messaging_cwd(self):
cfg = {}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/home/hermes/projects"})
assert result["TERMINAL_CWD"] == "/home/hermes/projects"
def test_no_cwd_no_messaging_cwd_falls_back_to_home(self):
cfg = {}
result = _simulate_config_bridge(cfg)
assert result["TERMINAL_CWD"] == "/root" # Path.home() for root user
def test_dot_cwd_triggers_messaging_fallback(self):
"""cwd: '.' should trigger MESSAGING_CWD fallback."""
cfg = {"cwd": "."}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/home/hermes"})
# "." is stripped but truthy, so it gets set as TERMINAL_CWD
# Then the MESSAGING_CWD fallback does NOT trigger since TERMINAL_CWD
# is set and not in (".", "auto", "cwd").
# Wait — "." IS in the fallback list! So this should fall through.
# Actually the alias sets it to ".", then the messaging fallback
# checks if it's in (".", "auto", "cwd") and overrides.
assert result["TERMINAL_CWD"] == "/home/hermes"
def test_auto_cwd_triggers_messaging_fallback(self):
cfg = {"cwd": "auto"}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/home/hermes"})
assert result["TERMINAL_CWD"] == "/home/hermes"
def test_empty_cwd_ignored(self):
cfg = {"cwd": ""}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/home/hermes"})
assert result["TERMINAL_CWD"] == "/home/hermes"
def test_whitespace_only_cwd_ignored(self):
cfg = {"cwd": " "}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/fallback"})
assert result["TERMINAL_CWD"] == "/fallback"
def test_messaging_cwd_env_var_works(self):
"""MESSAGING_CWD in initial env should be picked up as fallback."""
cfg = {}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/home/hermes/projects"})
assert result["TERMINAL_CWD"] == "/home/hermes/projects"
def test_top_level_cwd_beats_messaging_cwd(self):
"""Explicit top-level cwd should take precedence over MESSAGING_CWD."""
cfg = {"cwd": "/from/config"}
result = _simulate_config_bridge(cfg, {"MESSAGING_CWD": "/from/env"})
assert result["TERMINAL_CWD"] == "/from/config"
@@ -27,6 +27,9 @@ def _ensure_discord_mock():
discord_mod.Color = SimpleNamespace(orange=lambda: 1, green=lambda: 2, blue=lambda: 3, red=lambda: 4)
discord_mod.Interaction = object
discord_mod.Embed = MagicMock
discord_mod.app_commands = SimpleNamespace(
describe=lambda **kwargs: (lambda fn: fn),
)
ext_mod = MagicMock()
commands_mod = MagicMock()
@@ -0,0 +1,9 @@
import inspect
from gateway.platforms.discord import DiscordAdapter
def test_discord_media_methods_accept_metadata_kwarg():
for method_name in ("send_voice", "send_image_file", "send_image"):
signature = inspect.signature(getattr(DiscordAdapter, method_name))
assert "metadata" in signature.parameters, method_name
@@ -0,0 +1,435 @@
"""Tests for native Discord slash command fast-paths (thread creation & auto-thread)."""
from types import SimpleNamespace
from unittest.mock import AsyncMock, MagicMock, patch
import sys
import pytest
from gateway.config import PlatformConfig
def _ensure_discord_mock():
if "discord" in sys.modules and hasattr(sys.modules["discord"], "__file__"):
return
discord_mod = MagicMock()
discord_mod.Intents.default.return_value = MagicMock()
discord_mod.DMChannel = type("DMChannel", (), {})
discord_mod.Thread = type("Thread", (), {})
discord_mod.ForumChannel = type("ForumChannel", (), {})
discord_mod.Interaction = object
discord_mod.app_commands = SimpleNamespace(
describe=lambda **kwargs: (lambda fn: fn),
)
ext_mod = MagicMock()
commands_mod = MagicMock()
commands_mod.Bot = MagicMock
ext_mod.commands = commands_mod
sys.modules.setdefault("discord", discord_mod)
sys.modules.setdefault("discord.ext", ext_mod)
sys.modules.setdefault("discord.ext.commands", commands_mod)
_ensure_discord_mock()
from gateway.platforms.discord import DiscordAdapter # noqa: E402
class FakeTree:
def __init__(self):
self.commands = {}
def command(self, *, name, description):
def decorator(fn):
self.commands[name] = fn
return fn
return decorator
@pytest.fixture
def adapter():
config = PlatformConfig(enabled=True, token="***")
adapter = DiscordAdapter(config)
adapter._client = SimpleNamespace(
tree=FakeTree(),
get_channel=lambda _id: None,
fetch_channel=AsyncMock(),
user=SimpleNamespace(id=99999, name="HermesBot"),
)
return adapter
# ------------------------------------------------------------------
# /thread slash command registration
# ------------------------------------------------------------------
@pytest.mark.asyncio
async def test_registers_native_thread_slash_command(adapter):
adapter._handle_thread_create_slash = AsyncMock()
adapter._register_slash_commands()
command = adapter._client.tree.commands["thread"]
interaction = SimpleNamespace(
response=SimpleNamespace(defer=AsyncMock()),
)
await command(interaction, name="Planning", message="", auto_archive_duration=1440)
interaction.response.defer.assert_awaited_once_with(ephemeral=True)
adapter._handle_thread_create_slash.assert_awaited_once_with(interaction, "Planning", "", 1440)
# ------------------------------------------------------------------
# _handle_thread_create_slash — success, session dispatch, failure
# ------------------------------------------------------------------
@pytest.mark.asyncio
async def test_handle_thread_create_slash_reports_success(adapter):
created_thread = SimpleNamespace(id=555, name="Planning", send=AsyncMock())
parent_channel = SimpleNamespace(create_thread=AsyncMock(return_value=created_thread), send=AsyncMock())
interaction_channel = SimpleNamespace(parent=parent_channel)
interaction = SimpleNamespace(
channel=interaction_channel,
channel_id=123,
user=SimpleNamespace(display_name="Jezza", id=42),
guild=SimpleNamespace(name="TestGuild"),
followup=SimpleNamespace(send=AsyncMock()),
)
await adapter._handle_thread_create_slash(interaction, "Planning", "Kickoff", 1440)
parent_channel.create_thread.assert_awaited_once_with(
name="Planning",
auto_archive_duration=1440,
reason="Requested by Jezza via /thread",
)
created_thread.send.assert_awaited_once_with("Kickoff")
# Thread link shown to user
interaction.followup.send.assert_awaited()
args, kwargs = interaction.followup.send.await_args
assert "<#555>" in args[0]
assert kwargs["ephemeral"] is True
@pytest.mark.asyncio
async def test_handle_thread_create_slash_dispatches_session_when_message_provided(adapter):
"""When a message is given, _dispatch_thread_session should be called."""
created_thread = SimpleNamespace(id=555, name="Planning", send=AsyncMock())
parent_channel = SimpleNamespace(create_thread=AsyncMock(return_value=created_thread))
interaction = SimpleNamespace(
channel=SimpleNamespace(parent=parent_channel),
channel_id=123,
user=SimpleNamespace(display_name="Jezza", id=42),
guild=SimpleNamespace(name="TestGuild"),
followup=SimpleNamespace(send=AsyncMock()),
)
adapter._dispatch_thread_session = AsyncMock()
await adapter._handle_thread_create_slash(interaction, "Planning", "Hello Hermes", 1440)
adapter._dispatch_thread_session.assert_awaited_once_with(
interaction, "555", "Planning", "Hello Hermes",
)
@pytest.mark.asyncio
async def test_handle_thread_create_slash_no_dispatch_without_message(adapter):
"""Without a message, no session dispatch should occur."""
created_thread = SimpleNamespace(id=555, name="Planning", send=AsyncMock())
parent_channel = SimpleNamespace(create_thread=AsyncMock(return_value=created_thread))
interaction = SimpleNamespace(
channel=SimpleNamespace(parent=parent_channel),
channel_id=123,
user=SimpleNamespace(display_name="Jezza", id=42),
guild=SimpleNamespace(name="TestGuild"),
followup=SimpleNamespace(send=AsyncMock()),
)
adapter._dispatch_thread_session = AsyncMock()
await adapter._handle_thread_create_slash(interaction, "Planning", "", 1440)
adapter._dispatch_thread_session.assert_not_awaited()
@pytest.mark.asyncio
async def test_handle_thread_create_slash_falls_back_to_seed_message(adapter):
created_thread = SimpleNamespace(id=555, name="Planning")
seed_message = SimpleNamespace(id=777, create_thread=AsyncMock(return_value=created_thread))
channel = SimpleNamespace(
create_thread=AsyncMock(side_effect=RuntimeError("direct failed")),
send=AsyncMock(return_value=seed_message),
)
interaction = SimpleNamespace(
channel=channel,
channel_id=123,
user=SimpleNamespace(display_name="Jezza", id=42),
guild=SimpleNamespace(name="TestGuild"),
followup=SimpleNamespace(send=AsyncMock()),
)
await adapter._handle_thread_create_slash(interaction, "Planning", "Kickoff", 1440)
channel.send.assert_awaited_once_with("Kickoff")
seed_message.create_thread.assert_awaited_once_with(
name="Planning",
auto_archive_duration=1440,
reason="Requested by Jezza via /thread",
)
interaction.followup.send.assert_awaited()
@pytest.mark.asyncio
async def test_handle_thread_create_slash_reports_failure(adapter):
channel = SimpleNamespace(
create_thread=AsyncMock(side_effect=RuntimeError("direct failed")),
send=AsyncMock(side_effect=RuntimeError("nope")),
)
interaction = SimpleNamespace(
channel=channel,
channel_id=123,
user=SimpleNamespace(display_name="Jezza", id=42),
followup=SimpleNamespace(send=AsyncMock()),
)
await adapter._handle_thread_create_slash(interaction, "Planning", "", 1440)
interaction.followup.send.assert_awaited_once()
args, kwargs = interaction.followup.send.await_args
assert "Failed to create thread:" in args[0]
assert "nope" in args[0]
assert kwargs["ephemeral"] is True
# ------------------------------------------------------------------
# _dispatch_thread_session — builds correct event and routes it
# ------------------------------------------------------------------
@pytest.mark.asyncio
async def test_dispatch_thread_session_builds_thread_event(adapter):
"""Dispatched event should have chat_type=thread and chat_id=thread_id."""
interaction = SimpleNamespace(
user=SimpleNamespace(display_name="Jezza", id=42),
guild=SimpleNamespace(name="TestGuild"),
)
captured_events = []
async def capture_handle(event):
captured_events.append(event)
adapter.handle_message = capture_handle
await adapter._dispatch_thread_session(interaction, "555", "Planning", "Hello!")
assert len(captured_events) == 1
event = captured_events[0]
assert event.text == "Hello!"
assert event.source.chat_id == "555"
assert event.source.chat_type == "thread"
assert event.source.thread_id == "555"
assert "TestGuild" in event.source.chat_name
# ------------------------------------------------------------------
# Auto-thread: _auto_create_thread
# ------------------------------------------------------------------
@pytest.mark.asyncio
async def test_auto_create_thread_uses_message_content_as_name(adapter):
thread = SimpleNamespace(id=999, name="Hello world")
message = SimpleNamespace(
content="Hello world, how are you?",
create_thread=AsyncMock(return_value=thread),
)
result = await adapter._auto_create_thread(message)
assert result is thread
message.create_thread.assert_awaited_once()
call_kwargs = message.create_thread.await_args[1]
assert call_kwargs["name"] == "Hello world, how are you?"
assert call_kwargs["auto_archive_duration"] == 1440
@pytest.mark.asyncio
async def test_auto_create_thread_truncates_long_names(adapter):
long_text = "a" * 200
thread = SimpleNamespace(id=999, name="truncated")
message = SimpleNamespace(
content=long_text,
create_thread=AsyncMock(return_value=thread),
)
result = await adapter._auto_create_thread(message)
assert result is thread
call_kwargs = message.create_thread.await_args[1]
assert len(call_kwargs["name"]) <= 80
assert call_kwargs["name"].endswith("...")
@pytest.mark.asyncio
async def test_auto_create_thread_returns_none_on_failure(adapter):
message = SimpleNamespace(
content="Hello",
create_thread=AsyncMock(side_effect=RuntimeError("no perms")),
)
result = await adapter._auto_create_thread(message)
assert result is None
# ------------------------------------------------------------------
# Auto-thread integration in _handle_message
# ------------------------------------------------------------------
import discord as _discord_mod # noqa: E402 — mock or real, used below
class _FakeTextChannel:
"""A channel that is NOT a discord.Thread or discord.DMChannel."""
def __init__(self, channel_id=100, name="general", guild_name="TestGuild"):
self.id = channel_id
self.name = name
self.guild = SimpleNamespace(name=guild_name, id=1)
self.topic = None
class _FakeThreadChannel(_discord_mod.Thread):
"""isinstance(ch, discord.Thread) → True."""
def __init__(self, channel_id=200, name="existing-thread", guild_name="TestGuild", parent_id=100):
# Don't call super().__init__ — mock Thread is just an empty type
self.id = channel_id
self.name = name
self.guild = SimpleNamespace(name=guild_name, id=1)
self.topic = None
self.parent = SimpleNamespace(id=parent_id, name="general", guild=SimpleNamespace(name=guild_name, id=1))
def _fake_message(channel, *, content="Hello", author_id=42, display_name="Jezza"):
return SimpleNamespace(
author=SimpleNamespace(id=author_id, display_name=display_name, bot=False),
content=content,
channel=channel,
attachments=[],
mentions=[],
reference=None,
created_at=None,
id=12345,
)
@pytest.mark.asyncio
async def test_auto_thread_creates_thread_and_redirects(adapter, monkeypatch):
"""When DISCORD_AUTO_THREAD=true, a new thread is created and the event routes there."""
monkeypatch.setenv("DISCORD_AUTO_THREAD", "true")
monkeypatch.setenv("DISCORD_REQUIRE_MENTION", "false")
thread = SimpleNamespace(id=999, name="Hello")
adapter._auto_create_thread = AsyncMock(return_value=thread)
captured_events = []
async def capture_handle(event):
captured_events.append(event)
adapter.handle_message = capture_handle
msg = _fake_message(_FakeTextChannel(), content="Hello world")
await adapter._handle_message(msg)
adapter._auto_create_thread.assert_awaited_once_with(msg)
assert len(captured_events) == 1
event = captured_events[0]
assert event.source.chat_id == "999" # redirected to thread
assert event.source.chat_type == "thread"
assert event.source.thread_id == "999"
@pytest.mark.asyncio
async def test_auto_thread_disabled_by_default(adapter, monkeypatch):
"""Without DISCORD_AUTO_THREAD, messages stay in the channel."""
monkeypatch.delenv("DISCORD_AUTO_THREAD", raising=False)
monkeypatch.setenv("DISCORD_REQUIRE_MENTION", "false")
adapter._auto_create_thread = AsyncMock()
captured_events = []
async def capture_handle(event):
captured_events.append(event)
adapter.handle_message = capture_handle
msg = _fake_message(_FakeTextChannel())
await adapter._handle_message(msg)
adapter._auto_create_thread.assert_not_awaited()
assert len(captured_events) == 1
assert captured_events[0].source.chat_id == "100" # stays in channel
@pytest.mark.asyncio
async def test_auto_thread_skips_threads_and_dms(adapter, monkeypatch):
"""Auto-thread should not create threads inside existing threads."""
monkeypatch.setenv("DISCORD_AUTO_THREAD", "true")
monkeypatch.setenv("DISCORD_REQUIRE_MENTION", "false")
adapter._auto_create_thread = AsyncMock()
captured_events = []
async def capture_handle(event):
captured_events.append(event)
adapter.handle_message = capture_handle
msg = _fake_message(_FakeThreadChannel())
await adapter._handle_message(msg)
adapter._auto_create_thread.assert_not_awaited() # should NOT auto-thread
# ------------------------------------------------------------------
# Config bridge
# ------------------------------------------------------------------
def test_discord_auto_thread_config_bridge(monkeypatch, tmp_path):
"""discord.auto_thread in config.yaml should be bridged to DISCORD_AUTO_THREAD env var."""
import yaml
from pathlib import Path
# Write a config.yaml the loader will find
hermes_dir = tmp_path / ".hermes"
hermes_dir.mkdir()
config_path = hermes_dir / "config.yaml"
config_path.write_text(yaml.dump({
"discord": {"auto_thread": True},
}))
monkeypatch.delenv("DISCORD_AUTO_THREAD", raising=False)
monkeypatch.setenv("HERMES_HOME", str(hermes_dir))
monkeypatch.setattr(Path, "home", lambda: tmp_path)
from gateway.config import load_gateway_config
load_gateway_config()
import os
assert os.getenv("DISCORD_AUTO_THREAD") == "true"
+29 -11
View File
@@ -208,7 +208,7 @@ class TestAdapterInit:
def test_watch_filters_parsed(self):
config = PlatformConfig(
enabled=True, token="t",
enabled=True, token="***",
extra={
"watch_domains": ["climate", "binary_sensor"],
"watch_entities": ["sensor.special"],
@@ -220,15 +220,25 @@ class TestAdapterInit:
assert adapter._watch_domains == {"climate", "binary_sensor"}
assert adapter._watch_entities == {"sensor.special"}
assert adapter._ignore_entities == {"sensor.uptime", "sensor.cpu"}
assert adapter._watch_all is False
assert adapter._cooldown_seconds == 120
def test_watch_all_parsed(self):
config = PlatformConfig(
enabled=True, token="***",
extra={"watch_all": True},
)
adapter = HomeAssistantAdapter(config)
assert adapter._watch_all is True
def test_defaults_when_no_extra(self, monkeypatch):
monkeypatch.setenv("HASS_TOKEN", "tok")
config = PlatformConfig(enabled=True, token="tok")
config = PlatformConfig(enabled=True, token="***")
adapter = HomeAssistantAdapter(config)
assert adapter._watch_domains == set()
assert adapter._watch_entities == set()
assert adapter._ignore_entities == set()
assert adapter._watch_all is False
assert adapter._cooldown_seconds == 30
@@ -260,7 +270,7 @@ def _make_event(entity_id, old_state, new_state, old_attrs=None, new_attrs=None)
class TestEventFilteringPipeline:
@pytest.mark.asyncio
async def test_ignored_entity_not_forwarded(self):
adapter = _make_adapter(ignore_entities=["sensor.uptime"])
adapter = _make_adapter(watch_all=True, ignore_entities=["sensor.uptime"])
await adapter._handle_ha_event(_make_event("sensor.uptime", "100", "101"))
adapter.handle_message.assert_not_called()
@@ -298,26 +308,34 @@ class TestEventFilteringPipeline:
assert "10W" in msg_event.text and "20W" in msg_event.text
@pytest.mark.asyncio
async def test_no_filters_passes_everything(self):
async def test_no_filters_blocks_everything(self):
"""Without watch_domains, watch_entities, or watch_all, events are dropped."""
adapter = _make_adapter(cooldown_seconds=0)
await adapter._handle_ha_event(_make_event("cover.blinds", "closed", "open"))
adapter.handle_message.assert_not_called()
@pytest.mark.asyncio
async def test_watch_all_passes_everything(self):
"""With watch_all=True and no specific filters, all events pass through."""
adapter = _make_adapter(watch_all=True, cooldown_seconds=0)
await adapter._handle_ha_event(_make_event("cover.blinds", "closed", "open"))
adapter.handle_message.assert_called_once()
@pytest.mark.asyncio
async def test_same_state_not_forwarded(self):
adapter = _make_adapter(cooldown_seconds=0)
adapter = _make_adapter(watch_all=True, cooldown_seconds=0)
await adapter._handle_ha_event(_make_event("light.x", "on", "on"))
adapter.handle_message.assert_not_called()
@pytest.mark.asyncio
async def test_empty_entity_id_skipped(self):
adapter = _make_adapter()
adapter = _make_adapter(watch_all=True)
await adapter._handle_ha_event({"data": {"entity_id": ""}})
adapter.handle_message.assert_not_called()
@pytest.mark.asyncio
async def test_message_event_has_correct_source(self):
adapter = _make_adapter(cooldown_seconds=0)
adapter = _make_adapter(watch_all=True, cooldown_seconds=0)
await adapter._handle_ha_event(
_make_event("light.test", "off", "on",
new_attrs={"friendly_name": "Test Light"})
@@ -336,7 +354,7 @@ class TestEventFilteringPipeline:
class TestCooldown:
@pytest.mark.asyncio
async def test_cooldown_blocks_rapid_events(self):
adapter = _make_adapter(cooldown_seconds=60)
adapter = _make_adapter(watch_all=True, cooldown_seconds=60)
event = _make_event("sensor.temp", "20", "21",
new_attrs={"friendly_name": "Temp"})
@@ -351,7 +369,7 @@ class TestCooldown:
@pytest.mark.asyncio
async def test_cooldown_expires(self):
adapter = _make_adapter(cooldown_seconds=1)
adapter = _make_adapter(watch_all=True, cooldown_seconds=1)
event = _make_event("sensor.temp", "20", "21",
new_attrs={"friendly_name": "Temp"})
@@ -368,7 +386,7 @@ class TestCooldown:
@pytest.mark.asyncio
async def test_different_entities_independent_cooldowns(self):
adapter = _make_adapter(cooldown_seconds=60)
adapter = _make_adapter(watch_all=True, cooldown_seconds=60)
await adapter._handle_ha_event(
_make_event("sensor.a", "1", "2", new_attrs={"friendly_name": "A"})
@@ -387,7 +405,7 @@ class TestCooldown:
@pytest.mark.asyncio
async def test_zero_cooldown_passes_all(self):
adapter = _make_adapter(cooldown_seconds=0)
adapter = _make_adapter(watch_all=True, cooldown_seconds=0)
for i in range(5):
await adapter._handle_ha_event(
+22 -1
View File
@@ -182,7 +182,7 @@ class TestBuildSessionContextPrompt:
platforms={
Platform.DISCORD: PlatformConfig(
enabled=True,
token="fake-discord-token",
token="fake-d...oken",
),
},
)
@@ -197,6 +197,27 @@ class TestBuildSessionContextPrompt:
prompt = build_session_context_prompt(ctx)
assert "Discord" in prompt
assert "cannot search" in prompt.lower() or "do not have access" in prompt.lower()
def test_slack_prompt_includes_platform_notes(self):
config = GatewayConfig(
platforms={
Platform.SLACK: PlatformConfig(enabled=True, token="fake"),
},
)
source = SessionSource(
platform=Platform.SLACK,
chat_id="C123",
chat_name="general",
chat_type="group",
user_name="bob",
)
ctx = build_session_context(source, config)
prompt = build_session_context_prompt(ctx)
assert "Slack" in prompt
assert "cannot search" in prompt.lower()
assert "pin" in prompt.lower()
def test_discord_prompt_with_channel_topic(self):
"""Channel topic should appear in the session context prompt."""
+40 -4
View File
@@ -1,14 +1,21 @@
"""Tests for hermes doctor helpers."""
"""Tests for hermes_cli.doctor."""
import os
import sys
import types
from argparse import Namespace
from types import SimpleNamespace
import pytest
import hermes_cli.doctor as doctor
from hermes_cli import doctor as doctor_mod
from hermes_cli.doctor import _has_provider_env_config
class TestProviderEnvDetection:
def test_detects_openai_api_key(self):
content = "OPENAI_BASE_URL=http://localhost:1234/v1\nOPENAI_API_KEY=sk-test-key\n"
content = "OPENAI_BASE_URL=http://localhost:1234/v1\nOPENAI_API_KEY=***"
assert _has_provider_env_config(content)
def test_detects_custom_endpoint_without_openrouter_key(self):
@@ -47,7 +54,7 @@ class TestDoctorToolAvailabilityOverrides:
class TestHonchoDoctorConfigDetection:
def test_reports_configured_when_enabled_with_api_key(self, monkeypatch):
fake_config = SimpleNamespace(enabled=True, api_key="honcho-test-key")
fake_config = SimpleNamespace(enabled=True, api_key="***")
monkeypatch.setattr(
"honcho_integration.client.HonchoClientConfig.from_global_config",
@@ -57,7 +64,7 @@ class TestHonchoDoctorConfigDetection:
assert doctor._honcho_is_configured_for_doctor()
def test_reports_not_configured_without_api_key(self, monkeypatch):
fake_config = SimpleNamespace(enabled=True, api_key=None)
fake_config = SimpleNamespace(enabled=True, api_key="")
monkeypatch.setattr(
"honcho_integration.client.HonchoClientConfig.from_global_config",
@@ -65,3 +72,32 @@ class TestHonchoDoctorConfigDetection:
)
assert not doctor._honcho_is_configured_for_doctor()
def test_run_doctor_sets_interactive_env_for_tool_checks(monkeypatch, tmp_path):
"""Doctor should present CLI-gated tools as available in CLI context."""
project_root = tmp_path / "project"
hermes_home = tmp_path / ".hermes"
project_root.mkdir()
hermes_home.mkdir()
monkeypatch.setattr(doctor_mod, "PROJECT_ROOT", project_root)
monkeypatch.setattr(doctor_mod, "HERMES_HOME", hermes_home)
monkeypatch.delenv("HERMES_INTERACTIVE", raising=False)
seen = {}
def fake_check_tool_availability(*args, **kwargs):
seen["interactive"] = os.getenv("HERMES_INTERACTIVE")
raise SystemExit(0)
fake_model_tools = types.SimpleNamespace(
check_tool_availability=fake_check_tool_availability,
TOOLSET_REQUIREMENTS={},
)
monkeypatch.setitem(sys.modules, "model_tools", fake_model_tools)
with pytest.raises(SystemExit):
doctor_mod.run_doctor(Namespace(fix=False))
assert seen["interactive"] == "1"
+47
View File
@@ -95,3 +95,50 @@ def test_custom_setup_clears_active_oauth_provider(tmp_path, monkeypatch):
assert reloaded["model"]["provider"] == "custom"
assert reloaded["model"]["base_url"] == "https://custom.example/v1"
assert reloaded["model"]["default"] == "custom/model"
def test_codex_setup_uses_runtime_access_token_for_live_model_list(tmp_path, monkeypatch):
monkeypatch.setenv("HERMES_HOME", str(tmp_path))
monkeypatch.setenv("OPENROUTER_API_KEY", "or-test-key")
_clear_provider_env(monkeypatch)
monkeypatch.setenv("OPENROUTER_API_KEY", "or-test-key")
config = load_config()
prompt_choices = iter([1, 0])
monkeypatch.setattr(
"hermes_cli.setup.prompt_choice",
lambda *args, **kwargs: next(prompt_choices),
)
monkeypatch.setattr("hermes_cli.setup.prompt", lambda *args, **kwargs: "")
monkeypatch.setattr("hermes_cli.auth.detect_external_credentials", lambda: [])
monkeypatch.setattr("hermes_cli.auth._login_openai_codex", lambda *args, **kwargs: None)
monkeypatch.setattr(
"hermes_cli.auth.resolve_codex_runtime_credentials",
lambda *args, **kwargs: {
"base_url": "https://chatgpt.com/backend-api/codex",
"api_key": "codex-access-token",
},
)
captured = {}
def _fake_get_codex_model_ids(access_token=None):
captured["access_token"] = access_token
return ["gpt-5.2-codex", "gpt-5.2"]
monkeypatch.setattr(
"hermes_cli.codex_models.get_codex_model_ids",
_fake_get_codex_model_ids,
)
setup_model_provider(config)
save_config(config)
reloaded = load_config()
assert captured["access_token"] == "codex-access-token"
assert isinstance(reloaded["model"], dict)
assert reloaded["model"]["provider"] == "openai-codex"
assert reloaded["model"]["default"] == "gpt-5.2-codex"
assert reloaded["model"]["base_url"] == "https://chatgpt.com/backend-api/codex"
+50
View File
@@ -7,6 +7,7 @@ from unittest.mock import patch, MagicMock
import pytest
from agent.prompt_caching import apply_anthropic_cache_control
from agent.anthropic_adapter import (
_is_oauth_token,
_refresh_oauth_token,
@@ -491,6 +492,55 @@ class TestConvertMessages:
assert isinstance(system, list)
assert system[0]["cache_control"] == {"type": "ephemeral"}
def test_assistant_cache_control_blocks_are_preserved(self):
messages = apply_anthropic_cache_control([
{"role": "system", "content": "System prompt"},
{"role": "assistant", "content": "Hello from assistant"},
])
_, result = convert_messages_to_anthropic(messages)
assistant_blocks = result[0]["content"]
assert assistant_blocks[0]["type"] == "text"
assert assistant_blocks[0]["text"] == "Hello from assistant"
assert assistant_blocks[0]["cache_control"] == {"type": "ephemeral"}
def test_tool_cache_control_is_preserved_on_tool_result_block(self):
messages = apply_anthropic_cache_control([
{"role": "system", "content": "System prompt"},
{"role": "tool", "tool_call_id": "tc_1", "content": "result"},
])
_, result = convert_messages_to_anthropic(messages)
tool_block = result[0]["content"][0]
assert tool_block["type"] == "tool_result"
assert tool_block["tool_use_id"] == "tc_1"
assert tool_block["content"] == "result"
assert tool_block["cache_control"] == {"type": "ephemeral"}
def test_empty_cached_assistant_tool_turn_converts_without_empty_text_block(self):
messages = apply_anthropic_cache_control([
{"role": "system", "content": "System prompt"},
{"role": "user", "content": "Find the skill"},
{
"role": "assistant",
"content": "",
"tool_calls": [
{"id": "tc_1", "function": {"name": "skill_view", "arguments": "{}"}},
],
},
{"role": "tool", "tool_call_id": "tc_1", "content": "result"},
])
_, result = convert_messages_to_anthropic(messages)
assistant_turn = next(msg for msg in result if msg["role"] == "assistant")
assistant_blocks = assistant_turn["content"]
assert all(not (b.get("type") == "text" and b.get("text") == "") for b in assistant_blocks)
assert any(b.get("type") == "tool_use" for b in assistant_blocks)
# ---------------------------------------------------------------------------
# Build kwargs
+139
View File
@@ -0,0 +1,139 @@
"""Regression tests for CLI fresh-session commands."""
from __future__ import annotations
import importlib
import os
import sys
from datetime import timedelta
from unittest.mock import MagicMock, patch
from hermes_state import SessionDB
from tools.todo_tool import TodoStore
class _FakeAgent:
def __init__(self, session_id: str, session_start):
self.session_id = session_id
self.session_start = session_start
self.model = "anthropic/claude-opus-4.6"
self._last_flushed_db_idx = 7
self._todo_store = TodoStore()
self._todo_store.write(
[{"id": "t1", "content": "unfinished task", "status": "in_progress"}]
)
self.flush_memories = MagicMock()
self._invalidate_system_prompt = MagicMock()
def _make_cli(env_overrides=None, config_overrides=None, **kwargs):
"""Create a HermesCLI instance with minimal mocking."""
_clean_config = {
"model": {
"default": "anthropic/claude-opus-4.6",
"base_url": "https://openrouter.ai/api/v1",
"provider": "auto",
},
"display": {"compact": False, "tool_progress": "all"},
"agent": {},
"terminal": {"env_type": "local"},
}
if config_overrides:
_clean_config.update(config_overrides)
clean_env = {"LLM_MODEL": "", "HERMES_MAX_ITERATIONS": ""}
if env_overrides:
clean_env.update(env_overrides)
prompt_toolkit_stubs = {
"prompt_toolkit": MagicMock(),
"prompt_toolkit.history": MagicMock(),
"prompt_toolkit.styles": MagicMock(),
"prompt_toolkit.patch_stdout": MagicMock(),
"prompt_toolkit.application": MagicMock(),
"prompt_toolkit.layout": MagicMock(),
"prompt_toolkit.layout.processors": MagicMock(),
"prompt_toolkit.filters": MagicMock(),
"prompt_toolkit.layout.dimension": MagicMock(),
"prompt_toolkit.layout.menus": MagicMock(),
"prompt_toolkit.widgets": MagicMock(),
"prompt_toolkit.key_binding": MagicMock(),
"prompt_toolkit.completion": MagicMock(),
"prompt_toolkit.formatted_text": MagicMock(),
}
with patch.dict(sys.modules, prompt_toolkit_stubs), patch.dict(
"os.environ", clean_env, clear=False
):
import cli as _cli_mod
_cli_mod = importlib.reload(_cli_mod)
with patch.object(_cli_mod, "get_tool_definitions", return_value=[]), patch.dict(
_cli_mod.__dict__, {"CLI_CONFIG": _clean_config}
):
return _cli_mod.HermesCLI(**kwargs)
def _prepare_cli_with_active_session(tmp_path):
cli = _make_cli()
cli._session_db = SessionDB(db_path=tmp_path / "state.db")
cli._session_db.create_session(session_id=cli.session_id, source="cli", model=cli.model)
cli.agent = _FakeAgent(cli.session_id, cli.session_start)
cli.conversation_history = [{"role": "user", "content": "hello"}]
old_session_start = cli.session_start - timedelta(seconds=1)
cli.session_start = old_session_start
cli.agent.session_start = old_session_start
return cli
def test_new_command_creates_real_fresh_session_and_resets_agent_state(tmp_path):
cli = _prepare_cli_with_active_session(tmp_path)
old_session_id = cli.session_id
old_session_start = cli.session_start
cli.process_command("/new")
assert cli.session_id != old_session_id
old_session = cli._session_db.get_session(old_session_id)
assert old_session is not None
assert old_session["end_reason"] == "new_session"
new_session = cli._session_db.get_session(cli.session_id)
assert new_session is not None
cli._session_db.append_message(cli.session_id, role="user", content="next turn")
assert cli.agent.session_id == cli.session_id
assert cli.agent._last_flushed_db_idx == 0
assert cli.agent._todo_store.read() == []
assert cli.session_start > old_session_start
assert cli.agent.session_start == cli.session_start
cli.agent.flush_memories.assert_called_once_with([{"role": "user", "content": "hello"}])
cli.agent._invalidate_system_prompt.assert_called_once()
def test_reset_command_is_alias_for_new_session(tmp_path):
cli = _prepare_cli_with_active_session(tmp_path)
old_session_id = cli.session_id
cli.process_command("/reset")
assert cli.session_id != old_session_id
assert cli._session_db.get_session(old_session_id)["end_reason"] == "new_session"
assert cli._session_db.get_session(cli.session_id) is not None
def test_clear_command_starts_new_session_before_redrawing(tmp_path):
cli = _prepare_cli_with_active_session(tmp_path)
cli.console = MagicMock()
cli.show_banner = MagicMock()
old_session_id = cli.session_id
cli.process_command("/clear")
assert cli.session_id != old_session_id
assert cli._session_db.get_session(old_session_id)["end_reason"] == "new_session"
assert cli._session_db.get_session(cli.session_id) is not None
cli.console.clear.assert_called_once()
cli.show_banner.assert_called_once()
assert cli.conversation_history == []
+52
View File
@@ -52,6 +52,58 @@ def test_get_codex_model_ids_falls_back_to_curated_defaults(tmp_path, monkeypatc
models = get_codex_model_ids()
assert models[: len(DEFAULT_CODEX_MODELS)] == DEFAULT_CODEX_MODELS
assert "gpt-5.4" in models
assert "gpt-5.3-codex-spark" in models
def test_get_codex_model_ids_adds_forward_compat_models_from_templates(monkeypatch):
monkeypatch.setattr(
"hermes_cli.codex_models._fetch_models_from_api",
lambda access_token: ["gpt-5.2-codex"],
)
models = get_codex_model_ids(access_token="codex-access-token")
assert models == ["gpt-5.2-codex", "gpt-5.3-codex", "gpt-5.4", "gpt-5.3-codex-spark"]
def test_model_command_uses_runtime_access_token_for_codex_list(monkeypatch):
from hermes_cli.main import _model_flow_openai_codex
captured = {}
monkeypatch.setattr(
"hermes_cli.auth.get_codex_auth_status",
lambda: {"logged_in": True},
)
monkeypatch.setattr(
"hermes_cli.auth.resolve_codex_runtime_credentials",
lambda *args, **kwargs: {"api_key": "codex-access-token"},
)
def _fake_get_codex_model_ids(access_token=None):
captured["access_token"] = access_token
return ["gpt-5.2-codex", "gpt-5.2"]
def _fake_prompt_model_selection(model_ids, current_model=""):
captured["model_ids"] = list(model_ids)
captured["current_model"] = current_model
return None
monkeypatch.setattr(
"hermes_cli.codex_models.get_codex_model_ids",
_fake_get_codex_model_ids,
)
monkeypatch.setattr(
"hermes_cli.auth._prompt_model_selection",
_fake_prompt_model_selection,
)
_model_flow_openai_codex({}, current_model="openai/gpt-5.4")
assert captured["access_token"] == "codex-access-token"
assert captured["model_ids"] == ["gpt-5.2-codex", "gpt-5.2"]
assert captured["current_model"] == "openai/gpt-5.4"
# ── Tests for _normalize_model_for_provider ──────────────────────────
+25 -25
View File
@@ -1,11 +1,10 @@
"""
Tests for ManagedServer tool_call_parser integration.
Tests for ManagedServer / tool-parser integration.
Validates that:
1. ManagedServer accepts tool_call_parser parameter (tool_call_support branch)
2. ServerManager.managed_server() passes tool_call_parser through
3. The parser's parse() output is correctly attached to ChatCompletion responses
4. hermes-agent's tool_call_parsers are compatible with ManagedServer's expectations
1. The installed atroposlib API still matches Hermes's expectations
2. Hermes's parser registry remains compatible with ManagedServer parsing
3. HermesAgentBaseEnv wires the selected parser into ServerManager correctly
These tests verify the contract between hermes-agent's environments/ code
and atroposlib's ManagedServer. They detect API incompatibilities early.
@@ -142,37 +141,38 @@ class TestParserCompatibility:
class TestBaseEnvCompatibility:
"""Test that hermes_base_env.py's managed_server() call matches the API."""
"""Test that hermes_base_env.py's tool-parser wiring matches the current API."""
def test_hermes_base_env_managed_server_call_pattern(self):
"""
Verify that hermes_base_env.py passes tool_call_parser to managed_server().
This is a source-level check the actual managed_server() call must match.
"""
def test_hermes_base_env_sets_server_manager_tool_parser(self):
"""Hermes wires parser selection through ServerManager.tool_parser."""
import ast
base_env_path = Path(__file__).parent.parent / "environments" / "hermes_base_env.py"
source = base_env_path.read_text()
tree = ast.parse(source)
# Find the managed_server() call
found_tool_call_parser_kwarg = False
found_assignment = False
for node in ast.walk(tree):
if isinstance(node, ast.Call):
# Look for self.server.managed_server(...)
if isinstance(node.func, ast.Attribute) and node.func.attr == "managed_server":
for kw in node.keywords:
if kw.arg == "tool_call_parser":
found_tool_call_parser_kwarg = True
if isinstance(node, ast.Assign):
for target in node.targets:
if isinstance(target, ast.Attribute) and target.attr == "tool_parser":
parent = target.value
if (
isinstance(parent, ast.Attribute)
and parent.attr == "server"
and isinstance(parent.value, ast.Name)
and parent.value.id == "self"
):
found_assignment = True
assert found_tool_call_parser_kwarg, (
"hermes_base_env.py should pass tool_call_parser= to managed_server()"
assert found_assignment, (
"hermes_base_env.py should set self.server.tool_parser from config.tool_call_parser"
)
def test_hermes_base_env_uses_get_parser(self):
"""Verify hermes_base_env imports and uses get_parser from tool_call_parsers."""
def test_hermes_base_env_uses_config_tool_call_parser(self):
"""Verify hermes_base_env uses the config field rather than a local parser instance."""
base_env_path = Path(__file__).parent.parent / "environments" / "hermes_base_env.py"
source = base_env_path.read_text()
assert "from environments.tool_call_parsers import get_parser" in source
assert "get_parser(" in source
assert 'tool_call_parser: str = Field(' in source
assert 'self.server.tool_parser = config.tool_call_parser' in source
+34
View File
@@ -0,0 +1,34 @@
"""Tests for minisweagent_path.py."""
from pathlib import Path
from minisweagent_path import discover_minisweagent_src
def test_discover_minisweagent_src_in_current_checkout(tmp_path):
repo = tmp_path / "repo"
src = repo / "mini-swe-agent" / "src"
src.mkdir(parents=True)
assert discover_minisweagent_src(repo) == src.resolve()
def test_discover_minisweagent_src_falls_back_from_worktree_to_main_checkout(tmp_path):
main_repo = tmp_path / "main-repo"
(main_repo / ".git" / "worktrees" / "wt1").mkdir(parents=True)
main_src = main_repo / "mini-swe-agent" / "src"
main_src.mkdir(parents=True)
worktree = tmp_path / "worktree"
worktree.mkdir()
(worktree / ".git").write_text(f"gitdir: {main_repo / '.git' / 'worktrees' / 'wt1'}\n", encoding="utf-8")
(worktree / "mini-swe-agent").mkdir() # empty placeholder, no src/
assert discover_minisweagent_src(worktree) == main_src.resolve()
def test_discover_minisweagent_src_returns_none_when_missing(tmp_path):
repo = tmp_path / "repo"
repo.mkdir()
assert discover_minisweagent_src(repo) is None
+13 -2
View File
@@ -1,6 +1,7 @@
"""Tests for user-defined quick commands that bypass the agent loop."""
import subprocess
from unittest.mock import MagicMock, patch, AsyncMock
from rich.text import Text
import pytest
@@ -9,6 +10,12 @@ import pytest
class TestCLIQuickCommands:
"""Test quick command dispatch in HermesCLI.process_command."""
@staticmethod
def _printed_plain(call_arg):
if isinstance(call_arg, Text):
return call_arg.plain
return str(call_arg)
def _make_cli(self, quick_commands):
from cli import HermesCLI
cli = HermesCLI.__new__(HermesCLI)
@@ -22,7 +29,9 @@ class TestCLIQuickCommands:
cli = self._make_cli({"dn": {"type": "exec", "command": "echo daily-note"}})
result = cli.process_command("/dn")
assert result is True
cli.console.print.assert_called_once_with("daily-note")
cli.console.print.assert_called_once()
printed = self._printed_plain(cli.console.print.call_args[0][0])
assert printed == "daily-note"
def test_exec_command_stderr_shown_on_no_stdout(self):
cli = self._make_cli({"err": {"type": "exec", "command": "echo error >&2"}})
@@ -57,7 +66,9 @@ class TestCLIQuickCommands:
cli = self._make_cli({"mygif": {"type": "exec", "command": "echo overridden"}})
with patch("cli._skill_commands", {"/mygif": {"name": "gif-search"}}):
cli.process_command("/mygif")
cli.console.print.assert_called_once_with("overridden")
cli.console.print.assert_called_once()
printed = self._printed_plain(cli.console.print.call_args[0][0])
assert printed == "overridden"
def test_unknown_command_still_shows_error(self):
cli = self._make_cli({})
+48 -1
View File
@@ -14,7 +14,7 @@ from unittest.mock import MagicMock, patch
import pytest
from honcho_integration.client import HonchoClientConfig
from run_agent import AIAgent
from run_agent import AIAgent, _inject_honcho_turn_context
from agent.prompt_builder import DEFAULT_AGENT_IDENTITY
@@ -1441,6 +1441,53 @@ class TestSystemPromptStability:
should_prefetch = bool(conversation_history) and recall_mode != "tools"
assert should_prefetch is True
def test_inject_honcho_turn_context_appends_system_note(self):
content = _inject_honcho_turn_context("hello", "## Honcho Memory\nprior context")
assert "hello" in content
assert "Honcho memory was retrieved from prior sessions" in content
assert "## Honcho Memory" in content
def test_honcho_continuing_session_keeps_turn_context_out_of_system_prompt(self, agent):
captured = {}
def _fake_api_call(api_kwargs):
captured.update(api_kwargs)
return _mock_response(content="done", finish_reason="stop")
agent._honcho = object()
agent._honcho_session_key = "session-1"
agent._honcho_config = SimpleNamespace(
ai_peer="hermes",
memory_mode="hybrid",
write_frequency="async",
recall_mode="hybrid",
)
agent._use_prompt_caching = False
conversation_history = [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi there"},
]
with (
patch.object(agent, "_honcho_prefetch", return_value="## Honcho Memory\nprior context"),
patch.object(agent, "_queue_honcho_prefetch"),
patch.object(agent, "_persist_session"),
patch.object(agent, "_save_trajectory"),
patch.object(agent, "_cleanup_task_resources"),
patch.object(agent, "_interruptible_api_call", side_effect=_fake_api_call),
):
result = agent.run_conversation("what were we doing?", conversation_history=conversation_history)
assert result["completed"] is True
api_messages = captured["messages"]
assert api_messages[0]["role"] == "system"
assert "prior context" not in api_messages[0]["content"]
current_user = api_messages[-1]
assert current_user["role"] == "user"
assert "what were we doing?" in current_user["content"]
assert "prior context" in current_user["content"]
assert "Honcho memory was retrieved from prior sessions" in current_user["content"]
def test_honcho_prefetch_runs_on_first_turn(self):
"""Honcho prefetch should run when conversation_history is empty."""
conversation_history = []
+1 -1
View File
@@ -808,7 +808,7 @@ class TestTryAttachClipboardImage:
with patch("hermes_cli.clipboard.save_clipboard_image", return_value=True):
cli._try_attach_clipboard_image()
path = cli._attached_images[0]
assert path.parent == Path.home() / ".hermes" / "images"
assert path.parent == Path(os.environ["HERMES_HOME"]) / "images"
assert path.name.startswith("clip_")
assert path.suffix == ".png"
+163
View File
@@ -246,6 +246,169 @@ class TestDelegateTask(unittest.TestCase):
self.assertEqual(kwargs["api_mode"], parent.api_mode)
class TestDelegateObservability(unittest.TestCase):
"""Tests for enriched metadata returned by _run_single_child."""
def test_observability_fields_present(self):
"""Completed child should return tool_trace, tokens, model, exit_reason."""
parent = _make_mock_parent(depth=0)
with patch("run_agent.AIAgent") as MockAgent:
mock_child = MagicMock()
mock_child.model = "claude-sonnet-4-6"
mock_child.session_prompt_tokens = 5000
mock_child.session_completion_tokens = 1200
mock_child.run_conversation.return_value = {
"final_response": "done",
"completed": True,
"interrupted": False,
"api_calls": 3,
"messages": [
{"role": "user", "content": "do something"},
{"role": "assistant", "tool_calls": [
{"id": "tc_1", "function": {"name": "web_search", "arguments": '{"query": "test"}'}}
]},
{"role": "tool", "tool_call_id": "tc_1", "content": '{"results": [1,2,3]}'},
{"role": "assistant", "content": "done"},
],
}
MockAgent.return_value = mock_child
result = json.loads(delegate_task(goal="Test observability", parent_agent=parent))
entry = result["results"][0]
# Core observability fields
self.assertEqual(entry["model"], "claude-sonnet-4-6")
self.assertEqual(entry["exit_reason"], "completed")
self.assertEqual(entry["tokens"]["input"], 5000)
self.assertEqual(entry["tokens"]["output"], 1200)
# Tool trace
self.assertEqual(len(entry["tool_trace"]), 1)
self.assertEqual(entry["tool_trace"][0]["tool"], "web_search")
self.assertIn("args_bytes", entry["tool_trace"][0])
self.assertIn("result_bytes", entry["tool_trace"][0])
self.assertEqual(entry["tool_trace"][0]["status"], "ok")
def test_tool_trace_detects_error(self):
"""Tool results containing 'error' should be marked as error status."""
parent = _make_mock_parent(depth=0)
with patch("run_agent.AIAgent") as MockAgent:
mock_child = MagicMock()
mock_child.model = "claude-sonnet-4-6"
mock_child.session_prompt_tokens = 0
mock_child.session_completion_tokens = 0
mock_child.run_conversation.return_value = {
"final_response": "failed",
"completed": True,
"interrupted": False,
"api_calls": 1,
"messages": [
{"role": "assistant", "tool_calls": [
{"id": "tc_1", "function": {"name": "terminal", "arguments": '{"cmd": "ls"}'}}
]},
{"role": "tool", "tool_call_id": "tc_1", "content": "Error: command not found"},
],
}
MockAgent.return_value = mock_child
result = json.loads(delegate_task(goal="Test error trace", parent_agent=parent))
trace = result["results"][0]["tool_trace"]
self.assertEqual(trace[0]["status"], "error")
def test_parallel_tool_calls_paired_correctly(self):
"""Parallel tool calls should each get their own result via tool_call_id matching."""
parent = _make_mock_parent(depth=0)
with patch("run_agent.AIAgent") as MockAgent:
mock_child = MagicMock()
mock_child.model = "claude-sonnet-4-6"
mock_child.session_prompt_tokens = 3000
mock_child.session_completion_tokens = 800
mock_child.run_conversation.return_value = {
"final_response": "done",
"completed": True,
"interrupted": False,
"api_calls": 1,
"messages": [
{"role": "assistant", "tool_calls": [
{"id": "tc_a", "function": {"name": "web_search", "arguments": '{"q": "a"}'}},
{"id": "tc_b", "function": {"name": "web_search", "arguments": '{"q": "b"}'}},
{"id": "tc_c", "function": {"name": "terminal", "arguments": '{"cmd": "ls"}'}},
]},
{"role": "tool", "tool_call_id": "tc_a", "content": '{"ok": true}'},
{"role": "tool", "tool_call_id": "tc_b", "content": "Error: rate limited"},
{"role": "tool", "tool_call_id": "tc_c", "content": "file1.txt\nfile2.txt"},
{"role": "assistant", "content": "done"},
],
}
MockAgent.return_value = mock_child
result = json.loads(delegate_task(goal="Test parallel", parent_agent=parent))
trace = result["results"][0]["tool_trace"]
# All three tool calls should have results
self.assertEqual(len(trace), 3)
# First: web_search → ok
self.assertEqual(trace[0]["tool"], "web_search")
self.assertEqual(trace[0]["status"], "ok")
self.assertIn("result_bytes", trace[0])
# Second: web_search → error
self.assertEqual(trace[1]["tool"], "web_search")
self.assertEqual(trace[1]["status"], "error")
self.assertIn("result_bytes", trace[1])
# Third: terminal → ok
self.assertEqual(trace[2]["tool"], "terminal")
self.assertEqual(trace[2]["status"], "ok")
self.assertIn("result_bytes", trace[2])
def test_exit_reason_interrupted(self):
"""Interrupted child should report exit_reason='interrupted'."""
parent = _make_mock_parent(depth=0)
with patch("run_agent.AIAgent") as MockAgent:
mock_child = MagicMock()
mock_child.model = "claude-sonnet-4-6"
mock_child.session_prompt_tokens = 0
mock_child.session_completion_tokens = 0
mock_child.run_conversation.return_value = {
"final_response": "",
"completed": False,
"interrupted": True,
"api_calls": 2,
"messages": [],
}
MockAgent.return_value = mock_child
result = json.loads(delegate_task(goal="Test interrupt", parent_agent=parent))
self.assertEqual(result["results"][0]["exit_reason"], "interrupted")
def test_exit_reason_max_iterations(self):
"""Child that didn't complete and wasn't interrupted hit max_iterations."""
parent = _make_mock_parent(depth=0)
with patch("run_agent.AIAgent") as MockAgent:
mock_child = MagicMock()
mock_child.model = "claude-sonnet-4-6"
mock_child.session_prompt_tokens = 0
mock_child.session_completion_tokens = 0
mock_child.run_conversation.return_value = {
"final_response": "",
"completed": False,
"interrupted": False,
"api_calls": 50,
"messages": [],
}
MockAgent.return_value = mock_child
result = json.loads(delegate_task(goal="Test max iter", parent_agent=parent))
self.assertEqual(result["results"][0]["exit_reason"], "max_iterations")
class TestBlockedTools(unittest.TestCase):
def test_blocked_tools_constant(self):
for tool in ["delegate_task", "clarify", "memory", "send_message", "execute_code"]:
+173
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@@ -0,0 +1,173 @@
"""Tests for provider env var blocklist in LocalEnvironment.
Verifies that Hermes-internal provider env vars (OPENAI_BASE_URL, etc.)
are stripped from subprocess environments so external CLIs are not
silently misrouted.
See: https://github.com/NousResearch/hermes-agent/issues/1002
"""
import os
import threading
from unittest.mock import MagicMock, patch
from tools.environments.local import (
LocalEnvironment,
_HERMES_PROVIDER_ENV_BLOCKLIST,
_HERMES_PROVIDER_ENV_FORCE_PREFIX,
)
def _make_fake_popen(captured: dict):
"""Return a fake Popen constructor that records the env kwarg."""
def fake_popen(cmd, **kwargs):
captured["env"] = kwargs.get("env", {})
proc = MagicMock()
proc.poll.return_value = 0
proc.returncode = 0
proc.stdout = iter([])
proc.stdout.close = lambda: None
proc.stdin = MagicMock()
return proc
return fake_popen
def _run_with_env(extra_os_env=None, self_env=None):
"""Execute a command via LocalEnvironment with mocked Popen
and return the env dict passed to the subprocess."""
captured = {}
fake_interrupt = threading.Event()
test_environ = {
"PATH": "/usr/bin:/bin",
"HOME": "/home/user",
"USER": "testuser",
}
if extra_os_env:
test_environ.update(extra_os_env)
env = LocalEnvironment(cwd="/tmp", timeout=10, env=self_env)
with patch("tools.environments.local._find_bash", return_value="/bin/bash"), \
patch("subprocess.Popen", side_effect=_make_fake_popen(captured)), \
patch("tools.terminal_tool._interrupt_event", fake_interrupt), \
patch.dict(os.environ, test_environ, clear=True):
env.execute("echo hello")
return captured.get("env", {})
class TestProviderEnvBlocklist:
"""Provider env vars loaded from ~/.hermes/.env must not leak."""
def test_blocked_vars_are_stripped(self):
"""OPENAI_BASE_URL and other provider vars must not appear in subprocess env."""
leaked_vars = {
"OPENAI_BASE_URL": "http://localhost:8000/v1",
"OPENAI_API_KEY": "sk-fake-key",
"OPENROUTER_API_KEY": "or-fake-key",
"ANTHROPIC_API_KEY": "ant-fake-key",
"LLM_MODEL": "anthropic/claude-opus-4-6",
}
result_env = _run_with_env(extra_os_env=leaked_vars)
for var in leaked_vars:
assert var not in result_env, f"{var} leaked into subprocess env"
def test_registry_derived_vars_are_stripped(self):
"""Vars from the provider registry (ANTHROPIC_TOKEN, ZAI_API_KEY, etc.)
must also be blocked not just the hand-written extras."""
registry_vars = {
"ANTHROPIC_TOKEN": "ant-tok",
"CLAUDE_CODE_OAUTH_TOKEN": "cc-tok",
"ZAI_API_KEY": "zai-key",
"Z_AI_API_KEY": "z-ai-key",
"GLM_API_KEY": "glm-key",
"KIMI_API_KEY": "kimi-key",
"MINIMAX_API_KEY": "mm-key",
"MINIMAX_CN_API_KEY": "mmcn-key",
}
result_env = _run_with_env(extra_os_env=registry_vars)
for var in registry_vars:
assert var not in result_env, f"{var} leaked into subprocess env"
def test_safe_vars_are_preserved(self):
"""Standard env vars (PATH, HOME, USER) must still be passed through."""
result_env = _run_with_env()
assert "HOME" in result_env
assert result_env["HOME"] == "/home/user"
assert "USER" in result_env
assert "PATH" in result_env
def test_self_env_blocked_vars_also_stripped(self):
"""Blocked vars in self.env are stripped; non-blocked vars pass through."""
result_env = _run_with_env(self_env={
"OPENAI_BASE_URL": "http://custom:9999/v1",
"MY_CUSTOM_VAR": "keep-this",
})
assert "OPENAI_BASE_URL" not in result_env
assert "MY_CUSTOM_VAR" in result_env
assert result_env["MY_CUSTOM_VAR"] == "keep-this"
class TestForceEnvOptIn:
"""Callers can opt in to passing a blocked var via _HERMES_FORCE_ prefix."""
def test_force_prefix_passes_blocked_var(self):
"""_HERMES_FORCE_OPENAI_API_KEY in self.env should inject OPENAI_API_KEY."""
result_env = _run_with_env(self_env={
f"{_HERMES_PROVIDER_ENV_FORCE_PREFIX}OPENAI_API_KEY": "sk-explicit",
})
assert "OPENAI_API_KEY" in result_env
assert result_env["OPENAI_API_KEY"] == "sk-explicit"
# The force-prefixed key itself must not appear
assert f"{_HERMES_PROVIDER_ENV_FORCE_PREFIX}OPENAI_API_KEY" not in result_env
def test_force_prefix_overrides_os_environ_block(self):
"""Force-prefix in self.env wins even when os.environ has the blocked var."""
result_env = _run_with_env(
extra_os_env={"OPENAI_BASE_URL": "http://leaked/v1"},
self_env={f"{_HERMES_PROVIDER_ENV_FORCE_PREFIX}OPENAI_BASE_URL": "http://intended/v1"},
)
assert result_env["OPENAI_BASE_URL"] == "http://intended/v1"
class TestBlocklistCoverage:
"""Sanity checks that the blocklist covers all known providers."""
def test_issue_1002_offenders(self):
"""Blocklist includes the main offenders from issue #1002."""
must_block = {
"OPENAI_BASE_URL",
"OPENAI_API_KEY",
"OPENROUTER_API_KEY",
"ANTHROPIC_API_KEY",
"LLM_MODEL",
}
assert must_block.issubset(_HERMES_PROVIDER_ENV_BLOCKLIST)
def test_registry_vars_are_in_blocklist(self):
"""Every api_key_env_var and base_url_env_var from PROVIDER_REGISTRY
must appear in the blocklist ensures no drift."""
from hermes_cli.auth import PROVIDER_REGISTRY
for pconfig in PROVIDER_REGISTRY.values():
for var in pconfig.api_key_env_vars:
assert var in _HERMES_PROVIDER_ENV_BLOCKLIST, (
f"Registry var {var} (provider={pconfig.id}) missing from blocklist"
)
if pconfig.base_url_env_var:
assert pconfig.base_url_env_var in _HERMES_PROVIDER_ENV_BLOCKLIST, (
f"Registry base_url_env_var {pconfig.base_url_env_var} "
f"(provider={pconfig.id}) missing from blocklist"
)
def test_extra_auth_vars_covered(self):
"""Non-registry auth vars (ANTHROPIC_TOKEN, CLAUDE_CODE_OAUTH_TOKEN)
must also be in the blocklist."""
extras = {"ANTHROPIC_TOKEN", "CLAUDE_CODE_OAUTH_TOKEN"}
assert extras.issubset(_HERMES_PROVIDER_ENV_BLOCKLIST)
+15
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@@ -0,0 +1,15 @@
import logging
from tools.terminal_tool import check_terminal_requirements
def test_local_terminal_requirements_do_not_depend_on_minisweagent(monkeypatch, caplog):
"""Local backend uses Hermes' own LocalEnvironment wrapper and should not
be marked unavailable just because `minisweagent` isn't importable."""
monkeypatch.setenv("TERMINAL_ENV", "local")
with caplog.at_level(logging.ERROR):
ok = check_terminal_requirements()
assert ok is True
assert "Terminal requirements check failed" not in caplog.text
@@ -0,0 +1,28 @@
"""Tests for terminal/file tool availability in local dev environments."""
import importlib
from model_tools import get_tool_definitions
terminal_tool_module = importlib.import_module("tools.terminal_tool")
class TestTerminalRequirements:
def test_local_backend_does_not_require_minisweagent_package(self, monkeypatch):
monkeypatch.setattr(
terminal_tool_module,
"_get_env_config",
lambda: {"env_type": "local"},
)
assert terminal_tool_module.check_terminal_requirements() is True
def test_terminal_and_file_tools_resolve_for_local_backend(self, monkeypatch):
monkeypatch.setattr(
terminal_tool_module,
"_get_env_config",
lambda: {"env_type": "local"},
)
tools = get_tool_definitions(enabled_toolsets=["terminal", "file"], quiet_mode=True)
names = {tool["function"]["name"] for tool in tools}
assert "terminal" in names
assert {"read_file", "write_file", "patch", "search_files"}.issubset(names)
+223
View File
@@ -0,0 +1,223 @@
"""Tests for transcription_tools.py — local (faster-whisper) and OpenAI providers.
Tests cover provider selection, config loading, validation, and transcription
dispatch. All external dependencies (faster_whisper, openai) are mocked.
"""
import json
import os
import tempfile
from pathlib import Path
from unittest.mock import MagicMock, patch, mock_open
import pytest
# ---------------------------------------------------------------------------
# Provider selection
# ---------------------------------------------------------------------------
class TestGetProvider:
"""_get_provider() picks the right backend based on config + availability."""
def test_local_when_available(self):
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", True):
from tools.transcription_tools import _get_provider
assert _get_provider({"provider": "local"}) == "local"
def test_local_fallback_to_openai(self, monkeypatch):
monkeypatch.setenv("VOICE_TOOLS_OPENAI_KEY", "sk-test")
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", False), \
patch("tools.transcription_tools._HAS_OPENAI", True):
from tools.transcription_tools import _get_provider
assert _get_provider({"provider": "local"}) == "openai"
def test_local_nothing_available(self, monkeypatch):
monkeypatch.delenv("VOICE_TOOLS_OPENAI_KEY", raising=False)
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", False), \
patch("tools.transcription_tools._HAS_OPENAI", False):
from tools.transcription_tools import _get_provider
assert _get_provider({"provider": "local"}) == "none"
def test_openai_when_key_set(self, monkeypatch):
monkeypatch.setenv("VOICE_TOOLS_OPENAI_KEY", "sk-test")
with patch("tools.transcription_tools._HAS_OPENAI", True):
from tools.transcription_tools import _get_provider
assert _get_provider({"provider": "openai"}) == "openai"
def test_openai_fallback_to_local(self, monkeypatch):
monkeypatch.delenv("VOICE_TOOLS_OPENAI_KEY", raising=False)
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", True), \
patch("tools.transcription_tools._HAS_OPENAI", True):
from tools.transcription_tools import _get_provider
assert _get_provider({"provider": "openai"}) == "local"
def test_default_provider_is_local(self):
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", True):
from tools.transcription_tools import _get_provider
assert _get_provider({}) == "local"
# ---------------------------------------------------------------------------
# File validation
# ---------------------------------------------------------------------------
class TestValidateAudioFile:
def test_missing_file(self, tmp_path):
from tools.transcription_tools import _validate_audio_file
result = _validate_audio_file(str(tmp_path / "nope.ogg"))
assert result is not None
assert "not found" in result["error"]
def test_unsupported_format(self, tmp_path):
f = tmp_path / "test.xyz"
f.write_bytes(b"data")
from tools.transcription_tools import _validate_audio_file
result = _validate_audio_file(str(f))
assert result is not None
assert "Unsupported" in result["error"]
def test_valid_file_returns_none(self, tmp_path):
f = tmp_path / "test.ogg"
f.write_bytes(b"fake audio data")
from tools.transcription_tools import _validate_audio_file
assert _validate_audio_file(str(f)) is None
def test_too_large(self, tmp_path):
import stat as stat_mod
f = tmp_path / "big.ogg"
f.write_bytes(b"x")
from tools.transcription_tools import _validate_audio_file, MAX_FILE_SIZE
real_stat = f.stat()
with patch.object(type(f), "stat", return_value=os.stat_result((
real_stat.st_mode, real_stat.st_ino, real_stat.st_dev,
real_stat.st_nlink, real_stat.st_uid, real_stat.st_gid,
MAX_FILE_SIZE + 1, # st_size
real_stat.st_atime, real_stat.st_mtime, real_stat.st_ctime,
))):
result = _validate_audio_file(str(f))
assert result is not None
assert "too large" in result["error"]
# ---------------------------------------------------------------------------
# Local transcription
# ---------------------------------------------------------------------------
class TestTranscribeLocal:
def test_successful_transcription(self, tmp_path):
audio_file = tmp_path / "test.ogg"
audio_file.write_bytes(b"fake audio")
mock_segment = MagicMock()
mock_segment.text = "Hello world"
mock_info = MagicMock()
mock_info.language = "en"
mock_info.duration = 2.5
mock_model = MagicMock()
mock_model.transcribe.return_value = ([mock_segment], mock_info)
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", True), \
patch("tools.transcription_tools.WhisperModel", return_value=mock_model), \
patch("tools.transcription_tools._local_model", None):
from tools.transcription_tools import _transcribe_local
result = _transcribe_local(str(audio_file), "base")
assert result["success"] is True
assert result["transcript"] == "Hello world"
def test_not_installed(self):
with patch("tools.transcription_tools._HAS_FASTER_WHISPER", False):
from tools.transcription_tools import _transcribe_local
result = _transcribe_local("/tmp/test.ogg", "base")
assert result["success"] is False
assert "not installed" in result["error"]
# ---------------------------------------------------------------------------
# OpenAI transcription
# ---------------------------------------------------------------------------
class TestTranscribeOpenAI:
def test_no_key(self, monkeypatch):
monkeypatch.delenv("VOICE_TOOLS_OPENAI_KEY", raising=False)
from tools.transcription_tools import _transcribe_openai
result = _transcribe_openai("/tmp/test.ogg", "whisper-1")
assert result["success"] is False
assert "VOICE_TOOLS_OPENAI_KEY" in result["error"]
def test_successful_transcription(self, monkeypatch, tmp_path):
monkeypatch.setenv("VOICE_TOOLS_OPENAI_KEY", "sk-test")
audio_file = tmp_path / "test.ogg"
audio_file.write_bytes(b"fake audio")
mock_client = MagicMock()
mock_client.audio.transcriptions.create.return_value = "Hello from OpenAI"
with patch("tools.transcription_tools._HAS_OPENAI", True), \
patch("tools.transcription_tools.OpenAI", return_value=mock_client):
from tools.transcription_tools import _transcribe_openai
result = _transcribe_openai(str(audio_file), "whisper-1")
assert result["success"] is True
assert result["transcript"] == "Hello from OpenAI"
# ---------------------------------------------------------------------------
# Main transcribe_audio() dispatch
# ---------------------------------------------------------------------------
class TestTranscribeAudio:
def test_dispatches_to_local(self, tmp_path):
audio_file = tmp_path / "test.ogg"
audio_file.write_bytes(b"fake audio")
with patch("tools.transcription_tools._load_stt_config", return_value={"provider": "local"}), \
patch("tools.transcription_tools._get_provider", return_value="local"), \
patch("tools.transcription_tools._transcribe_local", return_value={"success": True, "transcript": "hi"}) as mock_local:
from tools.transcription_tools import transcribe_audio
result = transcribe_audio(str(audio_file))
assert result["success"] is True
mock_local.assert_called_once()
def test_dispatches_to_openai(self, tmp_path):
audio_file = tmp_path / "test.ogg"
audio_file.write_bytes(b"fake audio")
with patch("tools.transcription_tools._load_stt_config", return_value={"provider": "openai"}), \
patch("tools.transcription_tools._get_provider", return_value="openai"), \
patch("tools.transcription_tools._transcribe_openai", return_value={"success": True, "transcript": "hi"}) as mock_openai:
from tools.transcription_tools import transcribe_audio
result = transcribe_audio(str(audio_file))
assert result["success"] is True
mock_openai.assert_called_once()
def test_no_provider_returns_error(self, tmp_path):
audio_file = tmp_path / "test.ogg"
audio_file.write_bytes(b"fake audio")
with patch("tools.transcription_tools._load_stt_config", return_value={}), \
patch("tools.transcription_tools._get_provider", return_value="none"):
from tools.transcription_tools import transcribe_audio
result = transcribe_audio(str(audio_file))
assert result["success"] is False
assert "No STT provider" in result["error"]
def test_invalid_file_returns_error(self):
from tools.transcription_tools import transcribe_audio
result = transcribe_audio("/nonexistent/file.ogg")
assert result["success"] is False
assert "not found" in result["error"]
+58
View File
@@ -276,12 +276,70 @@ def _run_single_child(
else:
status = "failed"
# Build tool trace from conversation messages (already in memory).
# Uses tool_call_id to correctly pair parallel tool calls with results.
tool_trace: list[Dict[str, Any]] = []
trace_by_id: Dict[str, Dict[str, Any]] = {}
messages = result.get("messages") or []
if isinstance(messages, list):
for msg in messages:
if not isinstance(msg, dict):
continue
if msg.get("role") == "assistant":
for tc in (msg.get("tool_calls") or []):
fn = tc.get("function", {})
entry_t = {
"tool": fn.get("name", "unknown"),
"args_bytes": len(fn.get("arguments", "")),
}
tool_trace.append(entry_t)
tc_id = tc.get("id")
if tc_id:
trace_by_id[tc_id] = entry_t
elif msg.get("role") == "tool":
content = msg.get("content", "")
is_error = bool(
content and "error" in content[:80].lower()
)
result_meta = {
"result_bytes": len(content),
"status": "error" if is_error else "ok",
}
# Match by tool_call_id for parallel calls
tc_id = msg.get("tool_call_id")
target = trace_by_id.get(tc_id) if tc_id else None
if target is not None:
target.update(result_meta)
elif tool_trace:
# Fallback for messages without tool_call_id
tool_trace[-1].update(result_meta)
# Determine exit reason
if interrupted:
exit_reason = "interrupted"
elif completed:
exit_reason = "completed"
else:
exit_reason = "max_iterations"
# Extract token counts (safe for mock objects)
_input_tokens = getattr(child, "session_prompt_tokens", 0)
_output_tokens = getattr(child, "session_completion_tokens", 0)
_model = getattr(child, "model", None)
entry: Dict[str, Any] = {
"task_index": task_index,
"status": status,
"summary": summary,
"api_calls": api_calls,
"duration_seconds": duration,
"model": _model if isinstance(_model, str) else None,
"exit_reason": exit_reason,
"tokens": {
"input": _input_tokens if isinstance(_input_tokens, (int, float)) else 0,
"output": _output_tokens if isinstance(_output_tokens, (int, float)) else 0,
},
"tool_trace": tool_trace,
}
if status == "failed":
entry["error"] = result.get("error", "Subagent did not produce a response.")
+4 -2
View File
@@ -5,18 +5,20 @@ import os
import subprocess
from pathlib import Path
from hermes_cli.config import get_hermes_home
def get_sandbox_dir() -> Path:
"""Return the host-side root for all sandbox storage (Docker workspaces,
Singularity overlays/SIF cache, etc.).
Configurable via TERMINAL_SANDBOX_DIR. Defaults to ~/.hermes/sandboxes/.
Configurable via TERMINAL_SANDBOX_DIR. Defaults to {HERMES_HOME}/sandboxes/.
"""
custom = os.getenv("TERMINAL_SANDBOX_DIR")
if custom:
p = Path(custom)
else:
p = Path.home() / ".hermes" / "sandboxes"
p = get_hermes_home() / "sandboxes"
p.mkdir(parents=True, exist_ok=True)
return p
+58 -1
View File
@@ -16,6 +16,52 @@ from tools.environments.base import BaseEnvironment
# printf (no trailing newline) keeps the boundaries clean for splitting.
_OUTPUT_FENCE = "__HERMES_FENCE_a9f7b3__"
# Hermes-internal env vars that should NOT leak into terminal subprocesses.
# These are loaded from ~/.hermes/.env for Hermes' own LLM/provider calls
# but can break external CLIs (e.g. codex) that also honor them.
# See: https://github.com/NousResearch/hermes-agent/issues/1002
#
# Built dynamically from the provider registry so new providers are
# automatically covered without manual blocklist maintenance.
_HERMES_PROVIDER_ENV_FORCE_PREFIX = "_HERMES_FORCE_"
def _build_provider_env_blocklist() -> frozenset:
"""Derive the blocklist from the provider registry + known extras.
Automatically picks up api_key_env_vars and base_url_env_var from
every registered provider, so adding a new provider to auth.py is
enough no manual list to keep in sync.
"""
blocked: set[str] = set()
try:
from hermes_cli.auth import PROVIDER_REGISTRY
for pconfig in PROVIDER_REGISTRY.values():
blocked.update(pconfig.api_key_env_vars)
if pconfig.base_url_env_var:
blocked.add(pconfig.base_url_env_var)
except ImportError:
pass
# Vars not in the registry but still Hermes-internal / conflict-prone
blocked.update({
"OPENAI_BASE_URL",
"OPENAI_API_KEY",
"OPENAI_API_BASE", # legacy alias
"OPENAI_ORG_ID",
"OPENAI_ORGANIZATION",
"OPENROUTER_API_KEY",
"ANTHROPIC_BASE_URL",
"ANTHROPIC_TOKEN", # OAuth token (not in registry as env var)
"CLAUDE_CODE_OAUTH_TOKEN",
"LLM_MODEL",
})
return frozenset(blocked)
_HERMES_PROVIDER_ENV_BLOCKLIST = _build_provider_env_blocklist()
def _find_bash() -> str:
"""Find bash for command execution.
@@ -192,7 +238,18 @@ class LocalEnvironment(BaseEnvironment):
# Ensure PATH always includes standard dirs — systemd services
# and some terminal multiplexers inherit a minimal PATH.
_SANE_PATH = "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
run_env = dict(os.environ | self.env)
# Strip Hermes-internal provider vars so external CLIs
# (e.g. codex) are not silently misrouted. Callers that
# truly need a blocked var can opt in by prefixing the key
# with _HERMES_FORCE_ in self.env (e.g. _HERMES_FORCE_OPENAI_API_KEY).
merged = dict(os.environ | self.env)
run_env = {}
for k, v in merged.items():
if k.startswith(_HERMES_PROVIDER_ENV_FORCE_PREFIX):
real_key = k[len(_HERMES_PROVIDER_ENV_FORCE_PREFIX):]
run_env[real_key] = v
elif k not in _HERMES_PROVIDER_ENV_BLOCKLIST:
run_env[k] = v
existing_path = run_env.get("PATH", "")
if "/usr/bin" not in existing_path.split(":"):
run_env["PATH"] = f"{existing_path}:{_SANE_PATH}" if existing_path else _SANE_PATH
+2 -1
View File
@@ -13,12 +13,13 @@ import uuid
from pathlib import Path
from typing import Any, Dict, Optional
from hermes_cli.config import get_hermes_home
from tools.environments.base import BaseEnvironment
from tools.interrupt import is_interrupted
logger = logging.getLogger(__name__)
_SNAPSHOT_STORE = Path.home() / ".hermes" / "modal_snapshots.json"
_SNAPSHOT_STORE = get_hermes_home() / "modal_snapshots.json"
def _load_snapshots() -> Dict[str, str]:
+2 -1
View File
@@ -16,12 +16,13 @@ import uuid
from pathlib import Path
from typing import Any, Dict, Optional
from hermes_cli.config import get_hermes_home
from tools.environments.base import BaseEnvironment
from tools.interrupt import is_interrupted
logger = logging.getLogger(__name__)
_SNAPSHOT_STORE = Path.home() / ".hermes" / "singularity_snapshots.json"
_SNAPSHOT_STORE = get_hermes_home() / "singularity_snapshots.json"
def _load_snapshots() -> Dict[str, str]:
+10 -4
View File
@@ -42,16 +42,18 @@ import time
import uuid
_IS_WINDOWS = platform.system() == "Windows"
from tools.environments.local import _find_shell
from tools.environments.local import _find_shell, _HERMES_PROVIDER_ENV_BLOCKLIST
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional
from hermes_cli.config import get_hermes_home
logger = logging.getLogger(__name__)
# Checkpoint file for crash recovery (gateway only)
CHECKPOINT_PATH = Path(os.path.expanduser("~/.hermes/processes.json"))
CHECKPOINT_PATH = get_hermes_home() / "processes.json"
# Limits
MAX_OUTPUT_CHARS = 200_000 # 200KB rolling output buffer
@@ -153,7 +155,9 @@ class ProcessRegistry:
else:
from ptyprocess import PtyProcess as _PtyProcessCls
user_shell = _find_shell()
pty_env = os.environ | (env_vars or {})
pty_env = {k: v for k, v in os.environ.items()
if k not in _HERMES_PROVIDER_ENV_BLOCKLIST}
pty_env.update(env_vars or {})
pty_env["PYTHONUNBUFFERED"] = "1"
pty_proc = _PtyProcessCls.spawn(
[user_shell, "-lic", command],
@@ -194,7 +198,9 @@ class ProcessRegistry:
# Force unbuffered output for Python scripts so progress is visible
# during background execution (libraries like tqdm/datasets buffer when
# stdout is a pipe, hiding output from process(action="poll")).
bg_env = os.environ | (env_vars or {})
bg_env = {k: v for k, v in os.environ.items()
if k not in _HERMES_PROVIDER_ENV_BLOCKLIST}
bg_env.update(env_vars or {})
bg_env["PYTHONUNBUFFERED"] = "1"
proc = subprocess.Popen(
[user_shell, "-lic", command],
+29 -15
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@@ -26,6 +26,7 @@ Usage:
result = terminal_tool("python server.py", background=True)
"""
import importlib.util
import json
import logging
import os
@@ -53,10 +54,11 @@ logger = logging.getLogger(__name__)
from tools.interrupt import set_interrupt as set_interrupt_event, is_interrupted, _interrupt_event
# Add mini-swe-agent to path if not installed
mini_swe_path = Path(__file__).parent.parent / "mini-swe-agent" / "src"
if mini_swe_path.exists():
sys.path.insert(0, str(mini_swe_path))
# Add mini-swe-agent to path if not installed. In git worktrees the populated
# submodule may live in the main checkout rather than the worktree itself.
from minisweagent_path import ensure_minisweagent_on_path
ensure_minisweagent_on_path(Path(__file__).resolve().parent.parent)
# =============================================================================
@@ -1124,46 +1126,58 @@ def terminal_tool(
def check_terminal_requirements() -> bool:
"""Check if all requirements for the terminal tool are met."""
"""Check if all requirements for the terminal tool are met.
Important: local and singularity backends now use Hermes' own environment
wrappers directly and do not require the ``minisweagent`` Python package to
be installed. Docker and Modal still rely on mini-swe-agent internals.
"""
config = _get_env_config()
env_type = config["env_type"]
try:
if env_type == "local":
from minisweagent.environments.local import LocalEnvironment
# Local execution uses Hermes' own LocalEnvironment wrapper and does
# not depend on minisweagent being importable.
return True
elif env_type == "docker":
from minisweagent.environments.docker import DockerEnvironment
ensure_minisweagent_on_path(Path(__file__).resolve().parent.parent)
if importlib.util.find_spec("minisweagent") is None:
logger.error("mini-swe-agent is required for docker terminal backend but is not importable")
return False
# Check if docker is available (use find_docker for macOS PATH issues)
from tools.environments.docker import find_docker
import subprocess
docker = find_docker()
if not docker:
logger.error("Docker executable not found in PATH or common install locations")
return False
result = subprocess.run([docker, "version"], capture_output=True, timeout=5)
return result.returncode == 0
elif env_type == "singularity":
from minisweagent.environments.singularity import SingularityEnvironment
# Check if singularity/apptainer is available
import subprocess
import shutil
executable = shutil.which("apptainer") or shutil.which("singularity")
if executable:
result = subprocess.run([executable, "--version"], capture_output=True, timeout=5)
return result.returncode == 0
return False
elif env_type == "ssh":
from tools.environments.ssh import SSHEnvironment
# Check that host and user are configured
return bool(config.get("ssh_host")) and bool(config.get("ssh_user"))
elif env_type == "modal":
from minisweagent.environments.extra.swerex_modal import SwerexModalEnvironment
ensure_minisweagent_on_path(Path(__file__).resolve().parent.parent)
if importlib.util.find_spec("minisweagent") is None:
logger.error("mini-swe-agent is required for modal terminal backend but is not importable")
return False
# Check for modal token
return os.getenv("MODAL_TOKEN_ID") is not None or Path.home().joinpath(".modal.toml").exists()
elif env_type == "daytona":
from daytona import Daytona
return os.getenv("DAYTONA_API_KEY") is not None
else:
return False
except Exception as e:
+220 -124
View File
@@ -2,18 +2,19 @@
"""
Transcription Tools Module
Provides speech-to-text transcription using OpenAI's Whisper API.
Used by the messaging gateway to automatically transcribe voice messages
sent by users on Telegram, Discord, WhatsApp, and Slack.
Provides speech-to-text transcription with two providers:
Supported models:
- whisper-1 (cheapest, good quality)
- gpt-4o-mini-transcribe (better quality, higher cost)
- gpt-4o-transcribe (best quality, highest cost)
- **local** (default, free) faster-whisper running locally, no API key needed.
Auto-downloads the model (~150 MB for ``base``) on first use.
- **openai** OpenAI Whisper API, requires ``VOICE_TOOLS_OPENAI_KEY``.
Used by the messaging gateway to automatically transcribe voice messages
sent by users on Telegram, Discord, WhatsApp, Slack, and Signal.
Supported input formats: mp3, mp4, mpeg, mpga, m4a, wav, webm, ogg
Usage:
Usage::
from tools.transcription_tools import transcribe_audio
result = transcribe_audio("/path/to/audio.ogg")
@@ -28,27 +29,205 @@ from typing import Optional, Dict, Any
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Optional imports — graceful degradation
# ---------------------------------------------------------------------------
# Default STT model -- cheapest and widely available
DEFAULT_STT_MODEL = "whisper-1"
try:
from faster_whisper import WhisperModel
_HAS_FASTER_WHISPER = True
except ImportError:
_HAS_FASTER_WHISPER = False
WhisperModel = None # type: ignore[assignment,misc]
try:
from openai import OpenAI, APIError, APIConnectionError, APITimeoutError
_HAS_OPENAI = True
except ImportError:
_HAS_OPENAI = False
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
DEFAULT_PROVIDER = "local"
DEFAULT_LOCAL_MODEL = "base"
DEFAULT_OPENAI_MODEL = "whisper-1"
# Supported audio formats
SUPPORTED_FORMATS = {".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm", ".ogg"}
MAX_FILE_SIZE = 25 * 1024 * 1024 # 25 MB
# Maximum file size (25MB - OpenAI limit)
MAX_FILE_SIZE = 25 * 1024 * 1024
# Singleton for the local model — loaded once, reused across calls
_local_model: Optional["WhisperModel"] = None
_local_model_name: Optional[str] = None
# ---------------------------------------------------------------------------
# Config helpers
# ---------------------------------------------------------------------------
def _load_stt_config() -> dict:
"""Load the ``stt`` section from user config, falling back to defaults."""
try:
from hermes_cli.config import load_config
return load_config().get("stt", {})
except Exception:
return {}
def _get_provider(stt_config: dict) -> str:
"""Determine which STT provider to use.
Priority:
1. Explicit config value (``stt.provider``)
2. Auto-detect: local if faster-whisper available, else openai if key set
3. Disabled (returns "none")
"""
provider = stt_config.get("provider", DEFAULT_PROVIDER)
if provider == "local":
if _HAS_FASTER_WHISPER:
return "local"
# Local requested but not available — fall back to openai if possible
if _HAS_OPENAI and os.getenv("VOICE_TOOLS_OPENAI_KEY"):
logger.info("faster-whisper not installed, falling back to OpenAI Whisper API")
return "openai"
return "none"
if provider == "openai":
if _HAS_OPENAI and os.getenv("VOICE_TOOLS_OPENAI_KEY"):
return "openai"
# OpenAI requested but no key — fall back to local if possible
if _HAS_FASTER_WHISPER:
logger.info("VOICE_TOOLS_OPENAI_KEY not set, falling back to local faster-whisper")
return "local"
return "none"
return provider # Unknown — let it fail downstream
# ---------------------------------------------------------------------------
# Shared validation
# ---------------------------------------------------------------------------
def _validate_audio_file(file_path: str) -> Optional[Dict[str, Any]]:
"""Validate the audio file. Returns an error dict or None if OK."""
audio_path = Path(file_path)
if not audio_path.exists():
return {"success": False, "transcript": "", "error": f"Audio file not found: {file_path}"}
if not audio_path.is_file():
return {"success": False, "transcript": "", "error": f"Path is not a file: {file_path}"}
if audio_path.suffix.lower() not in SUPPORTED_FORMATS:
return {
"success": False,
"transcript": "",
"error": f"Unsupported format: {audio_path.suffix}. Supported: {', '.join(sorted(SUPPORTED_FORMATS))}",
}
try:
file_size = audio_path.stat().st_size
if file_size > MAX_FILE_SIZE:
return {
"success": False,
"transcript": "",
"error": f"File too large: {file_size / (1024*1024):.1f}MB (max {MAX_FILE_SIZE / (1024*1024):.0f}MB)",
}
except OSError as e:
return {"success": False, "transcript": "", "error": f"Failed to access file: {e}"}
return None
# ---------------------------------------------------------------------------
# Provider: local (faster-whisper)
# ---------------------------------------------------------------------------
def _transcribe_local(file_path: str, model_name: str) -> Dict[str, Any]:
"""Transcribe using faster-whisper (local, free)."""
global _local_model, _local_model_name
if not _HAS_FASTER_WHISPER:
return {"success": False, "transcript": "", "error": "faster-whisper not installed"}
try:
# Lazy-load the model (downloads on first use, ~150 MB for 'base')
if _local_model is None or _local_model_name != model_name:
logger.info("Loading faster-whisper model '%s' (first load downloads the model)...", model_name)
_local_model = WhisperModel(model_name, device="auto", compute_type="auto")
_local_model_name = model_name
segments, info = _local_model.transcribe(file_path, beam_size=5)
transcript = " ".join(segment.text.strip() for segment in segments)
logger.info(
"Transcribed %s via local whisper (%s, lang=%s, %.1fs audio)",
Path(file_path).name, model_name, info.language, info.duration,
)
return {"success": True, "transcript": transcript}
except Exception as e:
logger.error("Local transcription failed: %s", e, exc_info=True)
return {"success": False, "transcript": "", "error": f"Local transcription failed: {e}"}
# ---------------------------------------------------------------------------
# Provider: openai (Whisper API)
# ---------------------------------------------------------------------------
def _transcribe_openai(file_path: str, model_name: str) -> Dict[str, Any]:
"""Transcribe using OpenAI Whisper API (paid)."""
api_key = os.getenv("VOICE_TOOLS_OPENAI_KEY")
if not api_key:
return {"success": False, "transcript": "", "error": "VOICE_TOOLS_OPENAI_KEY not set"}
if not _HAS_OPENAI:
return {"success": False, "transcript": "", "error": "openai package not installed"}
try:
client = OpenAI(api_key=api_key, base_url="https://api.openai.com/v1")
with open(file_path, "rb") as audio_file:
transcription = client.audio.transcriptions.create(
model=model_name,
file=audio_file,
response_format="text",
)
transcript_text = str(transcription).strip()
logger.info("Transcribed %s via OpenAI API (%s, %d chars)",
Path(file_path).name, model_name, len(transcript_text))
return {"success": True, "transcript": transcript_text}
except PermissionError:
return {"success": False, "transcript": "", "error": f"Permission denied: {file_path}"}
except APIConnectionError as e:
return {"success": False, "transcript": "", "error": f"Connection error: {e}"}
except APITimeoutError as e:
return {"success": False, "transcript": "", "error": f"Request timeout: {e}"}
except APIError as e:
return {"success": False, "transcript": "", "error": f"API error: {e}"}
except Exception as e:
logger.error("OpenAI transcription failed: %s", e, exc_info=True)
return {"success": False, "transcript": "", "error": f"Transcription failed: {e}"}
# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------
def transcribe_audio(file_path: str, model: Optional[str] = None) -> Dict[str, Any]:
"""
Transcribe an audio file using OpenAI's Whisper API.
Transcribe an audio file using the configured STT provider.
This function calls the OpenAI Audio Transcriptions endpoint directly
(not via OpenRouter, since Whisper isn't available there).
Provider priority:
1. User config (``stt.provider`` in config.yaml)
2. Auto-detect: local faster-whisper if available, else OpenAI API
Args:
file_path: Absolute path to the audio file to transcribe.
model: Whisper model to use. Defaults to config or "whisper-1".
model: Override the model. If None, uses config or provider default.
Returns:
dict with keys:
@@ -56,114 +235,31 @@ def transcribe_audio(file_path: str, model: Optional[str] = None) -> Dict[str, A
- "transcript" (str): The transcribed text (empty on failure)
- "error" (str, optional): Error message if success is False
"""
api_key = os.getenv("VOICE_TOOLS_OPENAI_KEY")
if not api_key:
return {
"success": False,
"transcript": "",
"error": "VOICE_TOOLS_OPENAI_KEY not set",
}
# Validate input
error = _validate_audio_file(file_path)
if error:
return error
audio_path = Path(file_path)
# Validate file exists
if not audio_path.exists():
return {
"success": False,
"transcript": "",
"error": f"Audio file not found: {file_path}",
}
if not audio_path.is_file():
return {
"success": False,
"transcript": "",
"error": f"Path is not a file: {file_path}",
}
# Validate file extension
if audio_path.suffix.lower() not in SUPPORTED_FORMATS:
return {
"success": False,
"transcript": "",
"error": f"Unsupported file format: {audio_path.suffix}. Supported formats: {', '.join(sorted(SUPPORTED_FORMATS))}",
}
# Validate file size
try:
file_size = audio_path.stat().st_size
if file_size > MAX_FILE_SIZE:
return {
"success": False,
"transcript": "",
"error": f"File too large: {file_size / (1024*1024):.1f}MB (max {MAX_FILE_SIZE / (1024*1024)}MB)",
}
except OSError as e:
logger.error("Failed to get file size for %s: %s", file_path, e, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"Failed to access file: {e}",
}
# Load config and determine provider
stt_config = _load_stt_config()
provider = _get_provider(stt_config)
# Use provided model, or fall back to default
if model is None:
model = DEFAULT_STT_MODEL
if provider == "local":
local_cfg = stt_config.get("local", {})
model_name = model or local_cfg.get("model", DEFAULT_LOCAL_MODEL)
return _transcribe_local(file_path, model_name)
try:
from openai import OpenAI, APIError, APIConnectionError, APITimeoutError
if provider == "openai":
openai_cfg = stt_config.get("openai", {})
model_name = model or openai_cfg.get("model", DEFAULT_OPENAI_MODEL)
return _transcribe_openai(file_path, model_name)
client = OpenAI(api_key=api_key, base_url="https://api.openai.com/v1")
with open(file_path, "rb") as audio_file:
transcription = client.audio.transcriptions.create(
model=model,
file=audio_file,
response_format="text",
)
# The response is a plain string when response_format="text"
transcript_text = str(transcription).strip()
logger.info("Transcribed %s (%d chars)", audio_path.name, len(transcript_text))
return {
"success": True,
"transcript": transcript_text,
}
except PermissionError:
logger.error("Permission denied accessing file: %s", file_path, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"Permission denied: {file_path}",
}
except APIConnectionError as e:
logger.error("API connection error during transcription: %s", e, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"Connection error: {e}",
}
except APITimeoutError as e:
logger.error("API timeout during transcription: %s", e, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"Request timeout: {e}",
}
except APIError as e:
logger.error("OpenAI API error during transcription: %s", e, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"API error: {e}",
}
except Exception as e:
logger.error("Unexpected error during transcription: %s", e, exc_info=True)
return {
"success": False,
"transcript": "",
"error": f"Transcription failed: {e}",
}
# No provider available
return {
"success": False,
"transcript": "",
"error": (
"No STT provider available. Install faster-whisper for free local "
"transcription, or set VOICE_TOOLS_OPENAI_KEY for the OpenAI Whisper API."
),
}
+1 -1
View File
@@ -65,7 +65,7 @@ DEFAULT_ELEVENLABS_VOICE_ID = "pNInz6obpgDQGcFmaJgB" # Adam
DEFAULT_ELEVENLABS_MODEL_ID = "eleven_multilingual_v2"
DEFAULT_OPENAI_MODEL = "gpt-4o-mini-tts"
DEFAULT_OPENAI_VOICE = "alloy"
DEFAULT_OUTPUT_DIR = os.path.expanduser("~/.hermes/audio_cache")
DEFAULT_OUTPUT_DIR = str(Path(os.getenv("HERMES_HOME", Path.home() / ".hermes")) / "audio_cache")
MAX_TEXT_LENGTH = 4000
+9 -4
View File
@@ -44,8 +44,12 @@ hermes setup # Or configure everything at once
| **Nous Portal** | Subscription-based, zero-config | OAuth login via `hermes model` |
| **OpenAI Codex** | ChatGPT OAuth, uses Codex models | Device code auth via `hermes model` |
| **Anthropic** | Claude models directly (Pro/Max or API key) | API key or Claude Code setup-token |
| **OpenRouter** | 200+ models, pay-per-use | Enter your API key |
| **Custom Endpoint** | VLLM, SGLang, any OpenAI-compatible API | Set base URL + API key |
| **OpenRouter** | Multi-provider routing across many models | Enter your API key |
| **Z.AI** | GLM / Zhipu-hosted models | Set `GLM_API_KEY` / `ZAI_API_KEY` |
| **Kimi / Moonshot** | Moonshot-hosted coding and chat models | Set `KIMI_API_KEY` |
| **MiniMax** | International MiniMax endpoint | Set `MINIMAX_API_KEY` |
| **MiniMax China** | China-region MiniMax endpoint | Set `MINIMAX_CN_API_KEY` |
| **Custom Endpoint** | VLLM, SGLang, or any OpenAI-compatible API | Set base URL + API key |
:::tip
You can switch providers at any time with `hermes model` — no code changes, no lock-in.
@@ -119,7 +123,7 @@ hermes config set terminal.backend ssh # Remote server
### Connect messaging platforms
Chat with Hermes from your phone via Telegram, Discord, Slack, or WhatsApp:
Chat with Hermes from your phone or other surfaces via Telegram, Discord, Slack, WhatsApp, Signal, Email, or Home Assistant:
```bash
hermes gateway setup # Interactive platform configuration
@@ -138,6 +142,7 @@ The agent will set up a cron job that runs automatically via the gateway.
```bash
hermes skills search kubernetes
hermes skills install openai/skills/k8s
hermes skills install official/security/1password
```
Or use the `/skills` slash command inside chat.
@@ -175,5 +180,5 @@ mcp_servers:
- **[CLI Guide](../user-guide/cli.md)** — Master the terminal interface
- **[Configuration](../user-guide/configuration.md)** — Customize your setup
- **[Messaging Gateway](../user-guide/messaging/index.md)** — Connect Telegram, Discord, Slack, WhatsApp
- **[Messaging Gateway](../user-guide/messaging/index.md)** — Connect Telegram, Discord, Slack, WhatsApp, Signal, Email, or Home Assistant
- **[Tools & Toolsets](../user-guide/features/tools.md)** — Explore available capabilities
+335 -217
View File
@@ -1,231 +1,349 @@
---
sidebar_position: 1
title: "CLI Commands Reference"
description: "Comprehensive reference for all hermes CLI commands and slash commands"
description: "Authoritative reference for Hermes terminal commands and command families"
---
# CLI Commands Reference
## Terminal Commands
This page covers the **terminal commands** you run from your shell.
These are commands you run from your shell.
For in-chat slash commands, see [Slash Commands Reference](./slash-commands.md).
### Core Commands
## Global entrypoint
```bash
hermes [global-options] <command> [subcommand/options]
```
### Global options
| Option | Description |
|--------|-------------|
| `--version`, `-V` | Show version and exit. |
| `--resume <session>`, `-r <session>` | Resume a previous session by ID or title. |
| `--continue [name]`, `-c [name]` | Resume the most recent session, or the most recent session matching a title. |
| `--worktree`, `-w` | Start in an isolated git worktree for parallel-agent workflows. |
| `--yolo` | Bypass dangerous-command approval prompts. |
| `--pass-session-id` | Include the session ID in the agent's system prompt. |
## Top-level commands
| Command | Purpose |
|---------|---------|
| `hermes chat` | Interactive or one-shot chat with the agent. |
| `hermes model` | Interactively choose the default provider and model. |
| `hermes gateway` | Run or manage the messaging gateway service. |
| `hermes setup` | Interactive setup wizard for all or part of the configuration. |
| `hermes whatsapp` | Configure and pair the WhatsApp bridge. |
| `hermes login` / `logout` | Authenticate with OAuth-backed providers. |
| `hermes status` | Show agent, auth, and platform status. |
| `hermes cron` | Inspect and tick the cron scheduler. |
| `hermes doctor` | Diagnose config and dependency issues. |
| `hermes config` | Show, edit, migrate, and query configuration files. |
| `hermes pairing` | Approve or revoke messaging pairing codes. |
| `hermes skills` | Browse, install, publish, audit, and configure skills. |
| `hermes honcho` | Manage Honcho cross-session memory integration. |
| `hermes tools` | Configure enabled tools per platform. |
| `hermes sessions` | Browse, export, prune, rename, and delete sessions. |
| `hermes insights` | Show token/cost/activity analytics. |
| `hermes claw` | OpenClaw migration helpers. |
| `hermes version` | Show version information. |
| `hermes update` | Pull latest code and reinstall dependencies. |
| `hermes uninstall` | Remove Hermes from the system. |
## `hermes chat`
```bash
hermes chat [options]
```
Common options:
| Option | Description |
|--------|-------------|
| `-q`, `--query "..."` | One-shot, non-interactive prompt. |
| `-m`, `--model <model>` | Override the model for this run. |
| `-t`, `--toolsets <csv>` | Enable a comma-separated set of toolsets. |
| `--provider <provider>` | Force a provider: `auto`, `openrouter`, `nous`, `openai-codex`, `anthropic`, `zai`, `kimi-coding`, `minimax`, `minimax-cn`. |
| `-v`, `--verbose` | Verbose output. |
| `-Q`, `--quiet` | Programmatic mode: suppress banner/spinner/tool previews. |
| `--resume <session>` / `--continue [name]` | Resume a session directly from `chat`. |
| `--worktree` | Create an isolated git worktree for this run. |
| `--checkpoints` | Enable filesystem checkpoints before destructive file changes. |
| `--yolo` | Skip approval prompts. |
| `--pass-session-id` | Pass the session ID into the system prompt. |
Examples:
```bash
hermes
hermes chat -q "Summarize the latest PRs"
hermes chat --provider openrouter --model anthropic/claude-sonnet-4.6
hermes chat --toolsets web,terminal,skills
hermes chat --quiet -q "Return only JSON"
hermes chat --worktree -q "Review this repo and open a PR"
```
## `hermes model`
Interactive provider + model selector.
```bash
hermes model
```
Use this when you want to:
- switch default providers
- log into OAuth-backed providers during model selection
- pick from provider-specific model lists
- save the new default into config
## `hermes gateway`
```bash
hermes gateway <subcommand>
```
Subcommands:
| Subcommand | Description |
|------------|-------------|
| `run` | Run the gateway in the foreground. |
| `start` | Start the installed gateway service. |
| `stop` | Stop the service. |
| `restart` | Restart the service. |
| `status` | Show service status. |
| `install` | Install as a user service (`systemd` on Linux, `launchd` on macOS). |
| `uninstall` | Remove the installed service. |
| `setup` | Interactive messaging-platform setup. |
## `hermes setup`
```bash
hermes setup [model|terminal|gateway|tools|agent] [--non-interactive] [--reset]
```
Use the full wizard or jump into one section:
| Section | Description |
|---------|-------------|
| `model` | Provider and model setup. |
| `terminal` | Terminal backend and sandbox setup. |
| `gateway` | Messaging platform setup. |
| `tools` | Enable/disable tools per platform. |
| `agent` | Agent behavior settings. |
Options:
| Option | Description |
|--------|-------------|
| `--non-interactive` | Use defaults / environment values without prompts. |
| `--reset` | Reset configuration to defaults before setup. |
## `hermes whatsapp`
```bash
hermes whatsapp
```
Runs the WhatsApp pairing/setup flow, including mode selection and QR-code pairing.
## `hermes login` / `hermes logout`
```bash
hermes login [--provider nous|openai-codex] [--portal-url ...] [--inference-url ...]
hermes logout [--provider nous|openai-codex]
```
`login` supports:
- Nous Portal OAuth/device flow
- OpenAI Codex OAuth/device flow
Useful options for `login`:
- `--no-browser`
- `--timeout <seconds>`
- `--ca-bundle <pem>`
- `--insecure`
## `hermes status`
```bash
hermes status [--all] [--deep]
```
| Option | Description |
|--------|-------------|
| `--all` | Show all details in a shareable redacted format. |
| `--deep` | Run deeper checks that may take longer. |
## `hermes cron`
```bash
hermes cron <list|status|tick>
```
| Subcommand | Description |
|------------|-------------|
| `list` | Show scheduled jobs. |
| `status` | Check whether the cron scheduler is running. |
| `tick` | Run due jobs once and exit. |
## `hermes doctor`
```bash
hermes doctor [--fix]
```
| Option | Description |
|--------|-------------|
| `--fix` | Attempt automatic repairs where possible. |
## `hermes config`
```bash
hermes config <subcommand>
```
Subcommands:
| Subcommand | Description |
|------------|-------------|
| `show` | Show current config values. |
| `edit` | Open `config.yaml` in your editor. |
| `set <key> <value>` | Set a config value. |
| `path` | Print the config file path. |
| `env-path` | Print the `.env` file path. |
| `check` | Check for missing or stale config. |
| `migrate` | Add newly introduced options interactively. |
## `hermes pairing`
```bash
hermes pairing <list|approve|revoke|clear-pending>
```
| Subcommand | Description |
|------------|-------------|
| `list` | Show pending and approved users. |
| `approve <platform> <code>` | Approve a pairing code. |
| `revoke <platform> <user-id>` | Revoke a user's access. |
| `clear-pending` | Clear pending pairing codes. |
## `hermes skills`
```bash
hermes skills <subcommand>
```
Subcommands:
| Subcommand | Description |
|------------|-------------|
| `browse` | Paginated browser for skill registries. |
| `search` | Search skill registries. |
| `install` | Install a skill. |
| `inspect` | Preview a skill without installing it. |
| `list` | List installed skills. |
| `audit` | Re-scan installed hub skills. |
| `uninstall` | Remove a hub-installed skill. |
| `publish` | Publish a skill to a registry. |
| `snapshot` | Export/import skill configurations. |
| `tap` | Manage custom skill sources. |
| `config` | Interactive enable/disable configuration for skills by platform. |
Common examples:
```bash
hermes skills browse
hermes skills browse --source official
hermes skills search kubernetes
hermes skills inspect official/security/1password
hermes skills install official/migration/openclaw-migration
hermes skills config
```
## `hermes honcho`
```bash
hermes honcho <subcommand>
```
Subcommands:
| Subcommand | Description |
|------------|-------------|
| `setup` | Interactive Honcho setup wizard. |
| `status` | Show current Honcho config and connection status. |
| `sessions` | List known Honcho session mappings. |
| `map` | Map the current directory to a Honcho session name. |
| `peer` | Show or update peer names and dialectic reasoning level. |
| `mode` | Show or set memory mode: `hybrid`, `honcho`, or `local`. |
| `tokens` | Show or set token budgets for context and dialectic. |
| `identity` | Seed or show the AI peer identity representation. |
| `migrate` | Migration guide from openclaw-honcho to Hermes Honcho. |
## `hermes tools`
```bash
hermes tools [--summary]
```
| Option | Description |
|--------|-------------|
| `--summary` | Print the current enabled-tools summary and exit. |
Without `--summary`, this launches the interactive per-platform tool configuration UI.
## `hermes sessions`
```bash
hermes sessions <subcommand>
```
Subcommands:
| Subcommand | Description |
|------------|-------------|
| `list` | List recent sessions. |
| `browse` | Interactive session picker with search and resume. |
| `export <output> [--session-id ID]` | Export sessions to JSONL. |
| `delete <session-id>` | Delete one session. |
| `prune` | Delete old sessions. |
| `stats` | Show session-store statistics. |
| `rename <session-id> <title>` | Set or change a session title. |
## `hermes insights`
```bash
hermes insights [--days N] [--source platform]
```
| Option | Description |
|--------|-------------|
| `--days <n>` | Analyze the last `n` days (default: 30). |
| `--source <platform>` | Filter by source such as `cli`, `telegram`, or `discord`. |
## `hermes claw`
```bash
hermes claw migrate
```
Used to migrate settings, memories, skills, and keys from OpenClaw to Hermes.
## Maintenance commands
| Command | Description |
|---------|-------------|
| `hermes` | Start interactive chat (default) |
| `hermes chat -q "Hello"` | Single query mode (non-interactive) |
| `hermes chat --continue` / `-c` | Resume the most recent session |
| `hermes chat -c "my project"` | Resume a session by name (latest in lineage) |
| `hermes chat --resume <id>` / `-r <id>` | Resume a specific session by ID or title |
| `hermes chat --model <name>` | Use a specific model |
| `hermes chat --provider <name>` | Force a provider (`nous`, `openrouter`, `zai`, `kimi-coding`, `minimax`, `minimax-cn`) |
| `hermes chat --toolsets "web,terminal"` / `-t` | Use specific toolsets |
| `hermes chat --verbose` | Enable verbose/debug output |
| `hermes --worktree` / `-w` | Start in an isolated git worktree (for parallel agents) |
| `hermes --checkpoints` | Enable filesystem checkpoints before destructive file operations |
| `hermes version` | Print version information. |
| `hermes update` | Pull latest changes and reinstall dependencies. |
| `hermes uninstall [--full] [--yes]` | Remove Hermes, optionally deleting all config/data. |
### Provider & Model Management
## See also
| Command | Description |
|---------|-------------|
| `hermes model` | Switch provider and model interactively |
| `hermes login` | OAuth login to a provider (use `--provider` to specify) |
| `hermes logout` | Clear provider authentication |
### Configuration
| Command | Description |
|---------|-------------|
| `hermes setup` | Full setup wizard — configures provider, model, terminal, and messaging all at once |
| `hermes config` | View current configuration |
| `hermes config edit` | Open config.yaml in your editor |
| `hermes config set KEY VAL` | Set a specific value |
| `hermes config check` | Check for missing config (useful after updates) |
| `hermes config migrate` | Interactively add missing options |
| `hermes tools` | Interactive tool configuration per platform |
| `hermes status` | Show configuration status (including auth) |
| `hermes doctor` | Diagnose issues |
### Maintenance
| Command | Description |
|---------|-------------|
| `hermes update` | Update to latest version |
| `hermes uninstall` | Uninstall (can keep configs for later reinstall) |
| `hermes version` | Show version info |
### Gateway (Messaging + Cron)
| Command | Description |
|---------|-------------|
| `hermes gateway` | Run gateway in foreground |
| `hermes gateway setup` | Configure messaging platforms interactively |
| `hermes gateway install` | Install as system service (Linux/macOS) |
| `hermes gateway start` | Start the service |
| `hermes gateway stop` | Stop the service |
| `hermes gateway restart` | Restart the service |
| `hermes gateway status` | Check service status |
| `hermes gateway uninstall` | Uninstall the system service |
| `hermes whatsapp` | Pair WhatsApp via QR code |
### Skills
| Command | Description |
|---------|-------------|
| `hermes skills browse` | Browse all available skills with pagination (official first) |
| `hermes skills search <query>` | Search skill registries |
| `hermes skills install <identifier>` | Install a skill (with security scan) |
| `hermes skills inspect <identifier>` | Preview before installing |
| `hermes skills list` | List installed skills |
| `hermes skills list --source hub` | List hub-installed skills only |
| `hermes skills audit` | Re-scan all hub skills |
| `hermes skills uninstall <name>` | Remove a hub skill |
| `hermes skills publish <path> --to github --repo owner/repo` | Publish a skill |
| `hermes skills snapshot export <file>` | Export skill config |
| `hermes skills snapshot import <file>` | Import from snapshot |
| `hermes skills tap add <repo>` | Add a custom source |
| `hermes skills tap remove <repo>` | Remove a source |
| `hermes skills tap list` | List custom sources |
### Cron & Pairing
| Command | Description |
|---------|-------------|
| `hermes cron list` | View scheduled jobs |
| `hermes cron status` | Check if cron scheduler is running |
| `hermes cron tick` | Manually trigger a cron tick |
| `hermes pairing list` | View pending + approved users |
| `hermes pairing approve <platform> <code>` | Approve a pairing code |
| `hermes pairing revoke <platform> <user_id>` | Remove user access |
| `hermes pairing clear-pending` | Clear all pending pairing requests |
### Sessions
| Command | Description |
|---------|-------------|
| `hermes sessions list` | Browse past sessions (shows title, preview, last active) |
| `hermes sessions rename <id> <title>` | Set or change a session's title |
| `hermes sessions export <id>` | Export a session |
| `hermes sessions delete <id>` | Delete a specific session |
| `hermes sessions prune` | Remove old sessions |
| `hermes sessions stats` | Show session statistics |
### Insights
| Command | Description |
|---------|-------------|
| `hermes insights` | Show usage analytics for the last 30 days |
| `hermes insights --days 7` | Analyze a custom time window |
| `hermes insights --source telegram` | Filter by platform |
---
## Slash Commands (Inside Chat)
Type `/` in the interactive CLI to see an autocomplete dropdown.
### Navigation & Control
| Command | Description |
|---------|-------------|
| `/help` | Show available commands |
| `/quit` | Exit the CLI (aliases: `/exit`, `/q`) |
| `/clear` | Clear screen and reset conversation |
| `/new` | Start a new conversation |
| `/reset` | Reset conversation only (keep screen) |
### Tools & Configuration
| Command | Description |
|---------|-------------|
| `/tools` | List all available tools |
| `/toolsets` | List available toolsets |
| `/model [provider:model]` | Show or change the current model (supports `provider:model` syntax to switch providers) |
| `/provider` | Show available providers with auth status |
| `/config` | Show current configuration |
| `/prompt [text]` | View/set custom system prompt |
| `/personality [name]` | Set a predefined personality |
| `/reasoning [arg]` | Manage reasoning effort and display. Args: effort level (`none`, `low`, `medium`, `high`, `xhigh`) or display toggle (`show`, `hide`). No args shows current state. |
### Conversation
| Command | Description |
|---------|-------------|
| `/history` | Show conversation history |
| `/retry` | Retry the last message |
| `/undo` | Remove the last user/assistant exchange |
| `/save` | Save the current conversation |
| `/compress` | Manually compress conversation context |
| `/title [name]` | Set or show the current session's title |
| `/usage` | Show token usage for this session |
| `/insights [--days N]` | Show usage insights and analytics (last 30 days) |
#### /compress
Manually triggers context compression on the current conversation. This summarizes middle turns of the conversation while preserving the first 3 and last 4 turns, significantly reducing token count. Useful when:
- The conversation is getting long and you want to reduce costs
- You're approaching the model's context limit
- You want to continue the conversation without starting fresh
Requirements: at least 4 messages in the conversation. The configured model (or `compression.summary_model` from config) is used to generate the summary. After compression, the session continues seamlessly with the compressed history.
Reports the result as: `Compressed: X → Y messages, ~N → ~M tokens`.
:::tip
Compression also happens automatically when approaching context limits (configurable via `compression.threshold` in `config.yaml`). Use `/compress` when you want to trigger it early.
:::
### Media & Input
| Command | Description |
|---------|-------------|
| `/paste` | Check clipboard for an image and attach it (see [Vision & Image Paste](/docs/user-guide/features/vision)) |
### Skills & Scheduling
| Command | Description |
|---------|-------------|
| `/cron` | Manage scheduled tasks |
| `/skills` | Browse, search, install, inspect, or manage skills |
| `/platforms` | Show gateway/messaging platform status |
| `/verbose` | Cycle tool progress: off → new → all → verbose |
| `/<skill-name>` | Invoke any installed skill |
### Gateway-Only Commands
These work in messaging platforms (Telegram, Discord, Slack, WhatsApp) but not the interactive CLI:
| Command | Description |
|---------|-------------|
| `/stop` | Stop the running agent (no follow-up message) |
| `/sethome` | Set this chat as the home channel |
| `/status` | Show session info |
| `/reload-mcp` | Reload MCP servers from config |
| `/rollback` | List filesystem checkpoints for the current directory |
| `/rollback <N>` | Restore files to checkpoint #N |
| `/update` | Update Hermes Agent to the latest version |
---
## Keybindings
| Key | Action |
|-----|--------|
| `Enter` | Send message |
| `Alt+Enter` / `Ctrl+J` | New line (multi-line input) |
| `Alt+V` | Paste image from clipboard (see [Vision & Image Paste](/docs/user-guide/features/vision)) |
| `Ctrl+V` | Paste text + auto-check for clipboard image |
| `Ctrl+C` | Clear input/images, interrupt agent, or exit (contextual) |
| `Ctrl+D` | Exit |
| `Tab` | Autocomplete slash commands |
:::tip
Commands are case-insensitive — `/HELP` works the same as `/help`.
:::
:::info Image paste keybindings
`Alt+V` works in most terminals but **not** in VSCode's integrated terminal (VSCode intercepts Alt+key combos). `Ctrl+V` only triggers an image check when the clipboard also contains text (terminals don't send paste events for image-only clipboard). The `/paste` command is the universal fallback. See the [full compatibility table](/docs/user-guide/features/vision#platform-compatibility).
:::
- [Slash Commands Reference](./slash-commands.md)
- [CLI Interface](../user-guide/cli.md)
- [Sessions](../user-guide/sessions.md)
- [Skills System](../user-guide/features/skills.md)
- [Skins & Themes](../user-guide/features/skins.md)
+45 -18
View File
@@ -13,9 +13,12 @@ All variables go in `~/.hermes/.env`. You can also set them with `hermes config
| Variable | Description |
|----------|-------------|
| `OPENROUTER_API_KEY` | OpenRouter API key (recommended for flexibility) |
| `OPENROUTER_BASE_URL` | Override the OpenRouter-compatible base URL |
| `OPENAI_API_KEY` | API key for custom OpenAI-compatible endpoints (used with `OPENAI_BASE_URL`) |
| `OPENAI_BASE_URL` | Base URL for custom endpoint (VLLM, SGLang, etc.) |
| `GLM_API_KEY` | z.ai / ZhipuAI GLM API key ([z.ai](https://z.ai)) |
| `ZAI_API_KEY` | Alias for `GLM_API_KEY` |
| `Z_AI_API_KEY` | Alias for `GLM_API_KEY` |
| `GLM_BASE_URL` | Override z.ai base URL (default: `https://api.z.ai/api/paas/v4`) |
| `KIMI_API_KEY` | Kimi / Moonshot AI API key ([moonshot.ai](https://platform.moonshot.ai)) |
| `KIMI_BASE_URL` | Override Kimi base URL (default: `https://api.moonshot.ai/v1`) |
@@ -35,11 +38,14 @@ All variables go in `~/.hermes/.env`. You can also set them with `hermes config
| Variable | Description |
|----------|-------------|
| `HERMES_INFERENCE_PROVIDER` | Override provider selection: `auto`, `openrouter`, `nous`, `anthropic`, `zai`, `kimi-coding`, `minimax`, `minimax-cn` (default: `auto`) |
| `HERMES_INFERENCE_PROVIDER` | Override provider selection: `auto`, `openrouter`, `nous`, `openai-codex`, `anthropic`, `zai`, `kimi-coding`, `minimax`, `minimax-cn` (default: `auto`) |
| `HERMES_PORTAL_BASE_URL` | Override Nous Portal URL (for development/testing) |
| `NOUS_INFERENCE_BASE_URL` | Override Nous inference API URL |
| `HERMES_NOUS_MIN_KEY_TTL_SECONDS` | Min agent key TTL before re-mint (default: 1800 = 30min) |
| `HERMES_NOUS_TIMEOUT_SECONDS` | HTTP timeout for Nous credential / token flows |
| `HERMES_DUMP_REQUESTS` | Dump API request payloads to log files (`true`/`false`) |
| `HERMES_PREFILL_MESSAGES_FILE` | Path to a JSON file of ephemeral prefill messages injected at API-call time |
| `HERMES_TIMEZONE` | IANA timezone override (for example `America/New_York`) |
## Tool APIs
@@ -96,46 +102,67 @@ All variables go in `~/.hermes/.env`. You can also set them with `hermes config
| Variable | Description |
|----------|-------------|
| `TELEGRAM_BOT_TOKEN` | Telegram bot token (from @BotFather) |
| `TELEGRAM_ALLOWED_USERS` | Comma-separated user IDs allowed to use bot |
| `TELEGRAM_HOME_CHANNEL` | Default channel for cron delivery |
| `TELEGRAM_HOME_CHANNEL_NAME` | Display name for home channel |
| `TELEGRAM_ALLOWED_USERS` | Comma-separated user IDs allowed to use the bot |
| `TELEGRAM_HOME_CHANNEL` | Default Telegram chat/channel for cron delivery |
| `TELEGRAM_HOME_CHANNEL_NAME` | Display name for the Telegram home channel |
| `DISCORD_BOT_TOKEN` | Discord bot token |
| `DISCORD_ALLOWED_USERS` | Comma-separated user IDs allowed to use bot |
| `DISCORD_HOME_CHANNEL` | Default channel for cron delivery |
| `DISCORD_HOME_CHANNEL_NAME` | Display name for home channel |
| `DISCORD_ALLOWED_USERS` | Comma-separated Discord user IDs allowed to use the bot |
| `DISCORD_HOME_CHANNEL` | Default Discord channel for cron delivery |
| `DISCORD_HOME_CHANNEL_NAME` | Display name for the Discord home channel |
| `DISCORD_REQUIRE_MENTION` | Require an @mention before responding in server channels |
| `DISCORD_FREE_RESPONSE_CHANNELS` | Comma-separated channel IDs where mention is not required |
| `DISCORD_AUTO_THREAD` | Auto-thread long replies when supported |
| `SLACK_BOT_TOKEN` | Slack bot token (`xoxb-...`) |
| `SLACK_APP_TOKEN` | Slack app-level token (`xapp-...`, required for Socket Mode) |
| `SLACK_ALLOWED_USERS` | Comma-separated Slack user IDs |
| `SLACK_HOME_CHANNEL` | Default Slack channel for cron delivery |
| `WHATSAPP_ENABLED` | Enable WhatsApp bridge (`true`/`false`) |
| `SLACK_HOME_CHANNEL_NAME` | Display name for the Slack home channel |
| `WHATSAPP_ENABLED` | Enable the WhatsApp bridge (`true`/`false`) |
| `WHATSAPP_MODE` | `bot` (separate number) or `self-chat` (message yourself) |
| `WHATSAPP_ALLOWED_USERS` | Comma-separated phone numbers (with country code) |
| `SIGNAL_HTTP_URL` | signal-cli daemon HTTP endpoint (e.g., `http://127.0.0.1:8080`) |
| `SIGNAL_ACCOUNT` | Bot phone number in E.164 format (e.g., `+15551234567`) |
| `WHATSAPP_ALLOWED_USERS` | Comma-separated phone numbers (with country code, no `+`) |
| `SIGNAL_HTTP_URL` | signal-cli daemon HTTP endpoint (for example `http://127.0.0.1:8080`) |
| `SIGNAL_ACCOUNT` | Bot phone number in E.164 format |
| `SIGNAL_ALLOWED_USERS` | Comma-separated E.164 phone numbers or UUIDs |
| `SIGNAL_GROUP_ALLOWED_USERS` | Comma-separated group IDs, or `*` for all groups (omit to disable groups) |
| `MESSAGING_CWD` | Working directory for terminal in messaging (default: `~`) |
| `SIGNAL_GROUP_ALLOWED_USERS` | Comma-separated group IDs, or `*` for all groups |
| `SIGNAL_HOME_CHANNEL_NAME` | Display name for the Signal home channel |
| `SIGNAL_IGNORE_STORIES` | Ignore Signal stories/status updates |
| `SIGNAL_ALLOW_ALL_USERS` | Allow all Signal users without an allowlist |
| `EMAIL_ADDRESS` | Email address for the Email gateway adapter |
| `EMAIL_PASSWORD` | Password or app password for the email account |
| `EMAIL_IMAP_HOST` | IMAP hostname for the email adapter |
| `EMAIL_IMAP_PORT` | IMAP port |
| `EMAIL_SMTP_HOST` | SMTP hostname for the email adapter |
| `EMAIL_SMTP_PORT` | SMTP port |
| `EMAIL_ALLOWED_USERS` | Comma-separated email addresses allowed to message the bot |
| `EMAIL_HOME_ADDRESS` | Default recipient for proactive email delivery |
| `EMAIL_HOME_ADDRESS_NAME` | Display name for the email home target |
| `EMAIL_POLL_INTERVAL` | Email polling interval in seconds |
| `EMAIL_ALLOW_ALL_USERS` | Allow all inbound email senders |
| `HASS_TOKEN` | Home Assistant Long-Lived Access Token (enables HA platform + tools) |
| `HASS_URL` | Home Assistant URL (default: `http://homeassistant.local:8123`) |
| `MESSAGING_CWD` | Working directory for terminal commands in messaging mode (default: `~`) |
| `GATEWAY_ALLOWED_USERS` | Comma-separated user IDs allowed across all platforms |
| `GATEWAY_ALLOW_ALL_USERS` | Allow all users without allowlist (`true`/`false`, default: `false`) |
| `GATEWAY_ALLOW_ALL_USERS` | Allow all users without allowlists (`true`/`false`, default: `false`) |
## Agent Behavior
| Variable | Description |
|----------|-------------|
| `HERMES_MAX_ITERATIONS` | Max tool-calling iterations per conversation (default: 60) |
| `HERMES_TOOL_PROGRESS` | Send progress messages when using tools (`true`/`false`) |
| `HERMES_TOOL_PROGRESS_MODE` | `all` (every call, default) or `new` (only when tool changes) |
| `HERMES_TOOL_PROGRESS` | Deprecated compatibility variable for tool progress display. Prefer `display.tool_progress` in `config.yaml`. |
| `HERMES_TOOL_PROGRESS_MODE` | Deprecated compatibility variable for tool progress mode. Prefer `display.tool_progress` in `config.yaml`. |
| `HERMES_HUMAN_DELAY_MODE` | Response pacing: `off`/`natural`/`custom` |
| `HERMES_HUMAN_DELAY_MIN_MS` | Custom delay range minimum (ms) |
| `HERMES_HUMAN_DELAY_MAX_MS` | Custom delay range maximum (ms) |
| `HERMES_QUIET` | Suppress non-essential output (`true`/`false`) |
| `HERMES_API_TIMEOUT` | LLM API call timeout in seconds (default: `900`) |
| `HERMES_EXEC_ASK` | Enable execution approval prompts in gateway mode (`true`/`false`) |
## Session Settings
| Variable | Description |
|----------|-------------|
| `SESSION_IDLE_MINUTES` | Reset sessions after N minutes of inactivity (default: 120) |
| `SESSION_IDLE_MINUTES` | Reset sessions after N minutes of inactivity (default: 1440) |
| `SESSION_RESET_HOUR` | Daily reset hour in 24h format (default: 4 = 4am) |
## Context Compression
@@ -143,7 +170,7 @@ All variables go in `~/.hermes/.env`. You can also set them with `hermes config
| Variable | Description |
|----------|-------------|
| `CONTEXT_COMPRESSION_ENABLED` | Enable auto-compression (default: `true`) |
| `CONTEXT_COMPRESSION_THRESHOLD` | Trigger at this % of limit (default: 0.85) |
| `CONTEXT_COMPRESSION_THRESHOLD` | Trigger at this % of limit (default: 0.50) |
| `CONTEXT_COMPRESSION_MODEL` | Model for summaries |
## Provider Routing (config.yaml only)
@@ -0,0 +1,51 @@
---
sidebar_position: 6
title: "Official Optional Skills Catalog"
description: "Catalog of official optional skills available from the repository"
---
# Official Optional Skills Catalog
Official optional skills live in the repository under `optional-skills/`. Install them with `hermes skills install official/<category>/<skill>` or browse them with `hermes skills browse --source official`.
## autonomous-ai-agents
| Skill | Description | Path |
|-------|-------------|------|
| `blackbox` | Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key. | `autonomous-ai-agents/blackbox` |
## blockchain
| Skill | Description | Path |
|-------|-------------|------|
| `solana` | Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required. | `blockchain/solana` |
## email
| Skill | Description | Path |
|-------|-------------|------|
| `agentmail` | Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to). | `email/agentmail` |
## health
| Skill | Description | Path |
|-------|-------------|------|
| `neuroskill-bci` | Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or Open… | `health/neuroskill-bci` |
## migration
| Skill | Description | Path |
|-------|-------------|------|
| `openclaw-migration` | Migrate a user's OpenClaw customization footprint into Hermes Agent. Imports Hermes-compatible memories, SOUL.md, command allowlists, user skills, and selected workspace assets from ~/.openclaw, then reports exactly what could not be migrated and why. | `migration/openclaw-migration` |
## research
| Skill | Description | Path |
|-------|-------------|------|
| `qmd` | Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration. | `research/qmd` |
## security
| Skill | Description | Path |
|-------|-------------|------|
| `1password` | Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands. | `security/1password` |
+243
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@@ -0,0 +1,243 @@
---
sidebar_position: 5
title: "Bundled Skills Catalog"
description: "Catalog of bundled skills that ship with Hermes Agent"
---
# Bundled Skills Catalog
Hermes ships with a large built-in skill library copied into `~/.hermes/skills/` on install. This page catalogs the bundled skills that live in the repository under `skills/`.
## apple
Apple/macOS-specific skills — iMessage, Reminders, Notes, FindMy, and macOS automation. These skills only load on macOS systems.
| Skill | Description | Path |
|-------|-------------|------|
| `apple-notes` | Manage Apple Notes via the memo CLI on macOS (create, view, search, edit). | `apple/apple-notes` |
| `apple-reminders` | Manage Apple Reminders via remindctl CLI (list, add, complete, delete). | `apple/apple-reminders` |
| `findmy` | Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture. | `apple/findmy` |
| `imessage` | Send and receive iMessages/SMS via the imsg CLI on macOS. | `apple/imessage` |
## autonomous-ai-agents
Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.
| Skill | Description | Path |
|-------|-------------|------|
| `claude-code` | Delegate coding tasks to Claude Code (Anthropic's CLI agent). Use for building features, refactoring, PR reviews, and iterative coding. Requires the claude CLI installed. | `autonomous-ai-agents/claude-code` |
| `codex` | Delegate coding tasks to OpenAI Codex CLI agent. Use for building features, refactoring, PR reviews, and batch issue fixing. Requires the codex CLI and a git repository. | `autonomous-ai-agents/codex` |
| `hermes-agent-spawning` | Spawn additional Hermes Agent instances as autonomous subprocesses for independent long-running tasks. Supports non-interactive one-shot mode (-q) and interactive PTY mode for multi-turn collaboration. Different from delegate_task — this runs a full separate hermes process. | `autonomous-ai-agents/hermes-agent` |
| `opencode` | Delegate coding tasks to OpenCode CLI agent for feature implementation, refactoring, PR review, and long-running autonomous sessions. Requires the opencode CLI installed and authenticated. | `autonomous-ai-agents/opencode` |
## creative
Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
| Skill | Description | Path |
|-------|-------------|------|
| `ascii-art` | Generate ASCII art using pyfiglet (571 fonts), cowsay, boxes, toilet, image-to-ascii, remote APIs (asciified, ascii.co.uk), and LLM fallback. No API keys required. | `creative/ascii-art` |
| `ascii-video` | "Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid… | `creative/ascii-video` |
| `excalidraw` | Create hand-drawn style diagrams using Excalidraw JSON format. Generate .excalidraw files for architecture diagrams, flowcharts, sequence diagrams, concept maps, and more. Files can be opened at excalidraw.com or uploaded for shareable links. | `creative/excalidraw` |
## dogfood
| Skill | Description | Path |
|-------|-------------|------|
| `dogfood` | Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports | `dogfood` |
## email
Skills for sending, receiving, searching, and managing email from the terminal.
| Skill | Description | Path |
|-------|-------------|------|
| `himalaya` | CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language). | `email/himalaya` |
## gaming
Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
| Skill | Description | Path |
|-------|-------------|------|
| `minecraft-modpack-server` | Set up a modded Minecraft server from a CurseForge/Modrinth server pack zip. Covers NeoForge/Forge install, Java version, JVM tuning, firewall, LAN config, backups, and launch scripts. | `gaming/minecraft-modpack-server` |
| `pokemon-player` | Play Pokemon games autonomously via headless emulation. Starts a game server, reads structured game state from RAM, makes strategic decisions, and sends button inputs — all from the terminal. | `gaming/pokemon-player` |
## github
GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
| Skill | Description | Path |
|-------|-------------|------|
| `codebase-inspection` | Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats. | `github/codebase-inspection` |
| `github-auth` | Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically. | `github/github-auth` |
| `github-code-review` | Review code changes by analyzing git diffs, leaving inline comments on PRs, and performing thorough pre-push review. Works with gh CLI or falls back to git + GitHub REST API via curl. | `github/github-code-review` |
| `github-issues` | Create, manage, triage, and close GitHub issues. Search existing issues, add labels, assign people, and link to PRs. Works with gh CLI or falls back to git + GitHub REST API via curl. | `github/github-issues` |
| `github-pr-workflow` | Full pull request lifecycle — create branches, commit changes, open PRs, monitor CI status, auto-fix failures, and merge. Works with gh CLI or falls back to git + GitHub REST API via curl. | `github/github-pr-workflow` |
| `github-repo-management` | Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl. | `github/github-repo-management` |
## leisure
| Skill | Description | Path |
|-------|-------------|------|
| `find-nearby` | Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed. | `leisure/find-nearby` |
## mcp
Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Includes the built-in native MCP client (configure servers in config.yaml for automatic tool discovery) and the mcporter CLI bridge for ad-hoc server interaction.
| Skill | Description | Path |
|-------|-------------|------|
| `mcporter` | Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, and CLI/type generation. | `mcp/mcporter` |
| `native-mcp` | Built-in MCP (Model Context Protocol) client that connects to external MCP servers, discovers their tools, and registers them as native Hermes Agent tools. Supports stdio and HTTP transports with automatic reconnection, security filtering, and zero-config tool injection. | `mcp/native-mcp` |
## media
Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
| Skill | Description | Path |
|-------|-------------|------|
| `gif-search` | Search and download GIFs from Tenor using curl. No dependencies beyond curl and jq. Useful for finding reaction GIFs, creating visual content, and sending GIFs in chat. | `media/gif-search` |
| `heartmula` | Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support. | `media/heartmula` |
| `songsee` | Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation. | `media/songsee` |
| `youtube-content` | Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). | `media/youtube-content` |
## mlops/cloud
GPU cloud providers and serverless compute platforms for ML workloads.
| Skill | Description | Path |
|-------|-------------|------|
| `lambda-labs-gpu-cloud` | Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training. | `mlops/cloud/lambda-labs` |
| `modal-serverless-gpu` | Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling. | `mlops/cloud/modal` |
## mlops/evaluation
Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
| Skill | Description | Path |
|-------|-------------|------|
| `evaluating-llms-harness` | Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Sup… | `mlops/evaluation/lm-evaluation-harness` |
| `huggingface-tokenizers` | Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in &lt;20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use… | `mlops/evaluation/huggingface-tokenizers` |
| `nemo-curator` | GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality t… | `mlops/evaluation/nemo-curator` |
| `sparse-autoencoder-training` | Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language m… | `mlops/evaluation/saelens` |
| `weights-and-biases` | Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform | `mlops/evaluation/weights-and-biases` |
## mlops/inference
Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
| Skill | Description | Path |
|-------|-------------|------|
| `gguf-quantization` | GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements. | `mlops/inference/gguf` |
| `guidance` | Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework | `mlops/inference/guidance` |
| `instructor` | Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library | `mlops/inference/instructor` |
| `llama-cpp` | Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU. | `mlops/inference/llama-cpp` |
| `obliteratus` | Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets ac… | `mlops/inference/obliteratus` |
| `outlines` | Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library | `mlops/inference/outlines` |
| `serving-llms-vllm` | Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), an… | `mlops/inference/vllm` |
| `tensorrt-llm` | Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and mult… | `mlops/inference/tensorrt-llm` |
## mlops/models
Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
| Skill | Description | Path |
|-------|-------------|------|
| `audiocraft-audio-generation` | PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation. | `mlops/models/audiocraft` |
| `clip` | OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpo… | `mlops/models/clip` |
| `llava` | Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language cha… | `mlops/models/llava` |
| `segment-anything-model` | Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image. | `mlops/models/segment-anything` |
| `stable-diffusion-image-generation` | State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines. | `mlops/models/stable-diffusion` |
| `whisper` | OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio proc… | `mlops/models/whisper` |
## mlops/research
ML research frameworks for building and optimizing AI systems with declarative programming.
| Skill | Description | Path |
|-------|-------------|------|
| `dspy` | Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming | `mlops/research/dspy` |
## mlops/training
Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
| Skill | Description | Path |
|-------|-------------|------|
| `axolotl` | Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support | `mlops/training/axolotl` |
| `distributed-llm-pretraining-torchtitan` | Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing. | `mlops/training/torchtitan` |
| `fine-tuning-with-trl` | Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Tr… | `mlops/training/trl-fine-tuning` |
| `grpo-rl-training` | Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training | `mlops/training/grpo-rl-training` |
| `hermes-atropos-environments` | Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or f… | `mlops/training/hermes-atropos-environments` |
| `huggingface-accelerate` | Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard. | `mlops/training/accelerate` |
| `optimizing-attention-flash` | Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (&gt;512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA,… | `mlops/training/flash-attention` |
| `peft-fine-tuning` | Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train &lt;1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library i… | `mlops/training/peft` |
| `pytorch-fsdp` | Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2 | `mlops/training/pytorch-fsdp` |
| `pytorch-lightning` | High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices. | `mlops/training/pytorch-lightning` |
| `simpo-training` | Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO. | `mlops/training/simpo` |
| `slime-rl-training` | Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling. | `mlops/training/slime` |
| `unsloth` | Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization | `mlops/training/unsloth` |
## mlops/vector-databases
Vector similarity search and embedding databases for RAG, semantic search, and AI application backends.
| Skill | Description | Path |
|-------|-------------|------|
| `chroma` | Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best… | `mlops/vector-databases/chroma` |
| `faiss` | Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without… | `mlops/vector-databases/faiss` |
| `pinecone` | Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (&lt;100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for server… | `mlops/vector-databases/pinecone` |
| `qdrant-vector-search` | High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance. | `mlops/vector-databases/qdrant` |
## note-taking
Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
| Skill | Description | Path |
|-------|-------------|------|
| `obsidian` | Read, search, and create notes in the Obsidian vault. | `note-taking/obsidian` |
## productivity
Skills for document creation, presentations, spreadsheets, and other productivity workflows.
| Skill | Description | Path |
|-------|-------------|------|
| `google-workspace` | Gmail, Calendar, Drive, Contacts, Sheets, and Docs integration via Python. Uses OAuth2 with automatic token refresh. No external binaries needed — runs entirely with Google's Python client libraries in the Hermes venv. | `productivity/google-workspace` |
| `nano-pdf` | Edit PDFs with natural-language instructions using the nano-pdf CLI. Modify text, fix typos, update titles, and make content changes to specific pages without manual editing. | `productivity/nano-pdf` |
| `notion` | Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal. | `productivity/notion` |
| `ocr-and-documents` | Extract text from PDFs and scanned documents. Use web_extract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill. | `productivity/ocr-and-documents` |
| `powerpoint` | "Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in a… | `productivity/powerpoint` |
## research
Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
| Skill | Description | Path |
|-------|-------------|------|
| `arxiv` | Search and retrieve academic papers from arXiv using their free REST API. No API key needed. Search by keyword, author, category, or ID. Combine with web_extract or the ocr-and-documents skill to read full paper content. | `research/arxiv` |
| `blogwatcher` | Monitor blogs and RSS/Atom feeds for updates using the blogwatcher CLI. Add blogs, scan for new articles, and track what you've read. | `research/blogwatcher` |
| `domain-intel` | Passive domain reconnaissance using Python stdlib. Subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. | `research/domain-intel` |
| `duckduckgo-search` | Free web search via DuckDuckGo — text, news, images, videos. No API key needed. Use the Python DDGS library or CLI to search, then web_extract for full content. | `research/duckduckgo-search` |
| `ml-paper-writing` | Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verificatio… | `research/ml-paper-writing` |
| `polymarket` | Query Polymarket prediction market data — search markets, get prices, orderbooks, and price history. Read-only via public REST APIs, no API key needed. | `research/polymarket` |
## smart-home
Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
| Skill | Description | Path |
|-------|-------------|------|
| `openhue` | Control Philips Hue lights, rooms, and scenes via the OpenHue CLI. Turn lights on/off, adjust brightness, color, color temperature, and activate scenes. | `smart-home/openhue` |
## software-development
| Skill | Description | Path |
|-------|-------------|------|
| `code-review` | Guidelines for performing thorough code reviews with security and quality focus | `software-development/code-review` |
| `requesting-code-review` | Use when completing tasks, implementing major features, or before merging. Validates work meets requirements through systematic review process. | `software-development/requesting-code-review` |
| `subagent-driven-development` | Use when executing implementation plans with independent tasks. Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality). | `software-development/subagent-driven-development` |
| `systematic-debugging` | Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first. | `software-development/systematic-debugging` |
| `test-driven-development` | Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach. | `software-development/test-driven-development` |
| `writing-plans` | Use when you have a spec or requirements for a multi-step task. Creates comprehensive implementation plans with bite-sized tasks, exact file paths, and complete code examples. | `software-development/writing-plans` |
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---
sidebar_position: 2
title: "Slash Commands Reference"
description: "Complete reference for interactive CLI and messaging slash commands"
---
# Slash Commands Reference
Hermes has two slash-command surfaces:
- **Interactive CLI slash commands** — handled by `cli.py` / `hermes_cli/commands.py`
- **Messaging slash commands** — handled by `gateway/run.py`
Installed skills are also exposed as dynamic slash commands on both surfaces.
## Interactive CLI slash commands
Type `/` in the CLI to open the autocomplete menu. Built-in commands are case-insensitive.
### Session
| Command | Description |
|---------|-------------|
| `/new` | Start a new conversation (reset history) |
| `/reset` | Reset conversation only (keep screen) |
| `/clear` | Clear screen and reset conversation (fresh start) |
| `/history` | Show conversation history |
| `/save` | Save the current conversation |
| `/retry` | Retry the last message (resend to agent) |
| `/undo` | Remove the last user/assistant exchange |
| `/title` | Set a title for the current session (usage: /title My Session Name) |
| `/compress` | Manually compress conversation context (flush memories + summarize) |
| `/rollback` | List or restore filesystem checkpoints (usage: /rollback [number]) |
| `/background` | Run a prompt in the background (usage: /background &lt;prompt&gt;) |
### Configuration
| Command | Description |
|---------|-------------|
| `/config` | Show current configuration |
| `/model` | Show or change the current model |
| `/provider` | Show available providers and current provider |
| `/prompt` | View/set custom system prompt |
| `/personality` | Set a predefined personality |
| `/verbose` | Cycle tool progress display: off → new → all → verbose |
| `/reasoning` | Manage reasoning effort and display (usage: /reasoning [level\|show\|hide]) |
| `/skin` | Show or change the display skin/theme |
### Tools & Skills
| Command | Description |
|---------|-------------|
| `/tools` | List available tools |
| `/toolsets` | List available toolsets |
| `/skills` | Search, install, inspect, or manage skills from online registries |
| `/cron` | Manage scheduled tasks (list, add, remove) |
| `/reload-mcp` | Reload MCP servers from config.yaml |
### Info
| Command | Description |
|---------|-------------|
| `/help` | Show this help message |
| `/usage` | Show token usage for the current session |
| `/insights` | Show usage insights and analytics (last 30 days) |
| `/platforms` | Show gateway/messaging platform status |
| `/paste` | Check clipboard for an image and attach it |
### Exit
| Command | Description |
|---------|-------------|
| `/quit` | Exit the CLI (also: /exit, /q) |
### Dynamic CLI slash commands
| Command | Description |
|---------|-------------|
| `/<skill-name>` | Load any installed skill as an on-demand command. Example: `/gif-search`, `/github-pr-workflow`, `/excalidraw`. |
| `/skills ...` | Search, browse, inspect, install, audit, publish, and configure skills from registries and the official optional-skills catalog. |
### Quick commands
User-defined quick commands from `quick_commands` in `~/.hermes/config.yaml` are also available as slash commands. These are resolved at dispatch time, not shown in the built-in autocomplete/help tables.
## Messaging slash commands
The messaging gateway supports the following built-in commands inside Telegram, Discord, Slack, WhatsApp, Signal, Email, and Home Assistant chats:
| Command | Description |
|---------|-------------|
| `/new` | Start a new conversation. |
| `/reset` | Reset conversation history. |
| `/status` | Show session info. |
| `/stop` | Interrupt the running agent without queuing a follow-up prompt. |
| `/model [provider:model]` | Show or change the model, including provider switches. |
| `/provider` | Show provider availability and auth status. |
| `/personality [name]` | Set a personality overlay for the session. |
| `/retry` | Retry the last message. |
| `/undo` | Remove the last exchange. |
| `/sethome` | Mark the current chat as the platform home channel for deliveries. |
| `/compress` | Manually compress conversation context. |
| `/title [name]` | Set or show the session title. |
| `/resume [name]` | Resume a previously named session. |
| `/usage` | Show token usage for the current session. |
| `/insights [days]` | Show usage analytics. |
| `/reasoning [level\|show\|hide]` | Change reasoning effort or toggle reasoning display. |
| `/rollback [number]` | List or restore filesystem checkpoints. |
| `/background &lt;prompt&gt;` | Run a prompt in a separate background session. |
| `/reload-mcp` | Reload MCP servers from config. |
| `/update` | Update Hermes Agent to the latest version. |
| `/help` | Show messaging help. |
| `/<skill-name>` | Invoke any installed skill by name. |
## Notes
- `/skin`, `/tools`, `/toolsets`, `/config`, `/prompt`, `/cron`, `/skills`, `/platforms`, `/paste`, and `/verbose` are **CLI-only** commands.
- `/status`, `/stop`, `/sethome`, `/resume`, `/background`, and `/update` are **messaging-only** commands.
- `/reload-mcp` and `/rollback` work in **both** the CLI and the messaging gateway.
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---
sidebar_position: 3
title: "Built-in Tools Reference"
description: "Authoritative reference for Hermes built-in tools, grouped by toolset"
---
# Built-in Tools Reference
This page documents the built-in Hermes tool registry as it exists in code. Availability can still vary by platform, credentials, and enabled toolsets.
## `browser` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `browser_back` | Navigate back to the previous page in browser history. Requires browser_navigate to be called first. | — |
| `browser_click` | Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first. | — |
| `browser_close` | Close the browser session and release resources. Call this when done with browser tasks to free up Browserbase session quota. | — |
| `browser_console` | Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requi… | — |
| `browser_get_images` | Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first. | — |
| `browser_navigate` | Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For simple information retrieval, prefer web_search or web_extract (faster, cheaper). Use browser tools when you need… | — |
| `browser_press` | Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first. | — |
| `browser_scroll` | Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first. | — |
| `browser_snapshot` | Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: comp… | — |
| `browser_type` | Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first. | — |
| `browser_vision` | Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snaps… | — |
## `clarify` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `clarify` | Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes: 1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option. 2.… | — |
## `code_execution` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `execute_code` | Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (… | — |
## `cronjob` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `list_cronjobs` | List all scheduled cronjobs with their IDs, schedules, and status. Use this to: - See what jobs are currently scheduled - Find job IDs for removal with remove_cronjob - Check job status and next run times Returns job_id, name, schedule, re… | — |
| `remove_cronjob` | Remove a scheduled cronjob by its ID. Use list_cronjobs first to find the job_id of the job you want to remove. Jobs that have completed their repeat count are auto-removed, but you can use this to cancel a job before it completes. | — |
| `schedule_cronjob` | Schedule an automated task to run the agent on a schedule. ⚠️ CRITICAL: The cronjob runs in a FRESH SESSION with NO CONTEXT from this conversation. The prompt must be COMPLETELY SELF-CONTAINED with ALL necessary information including: - Fu… | — |
## `delegation` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `delegate_task` | Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window. TWO… | — |
## `file` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `patch` | Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing… | — |
| `read_file` | Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM\|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. NOTE: Cannot read images o… | — |
| `search_files` | Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents. Content search (target='content'): Regex search inside files. Output modes: full matches with line… | — |
| `write_file` | Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. | — |
## `homeassistant` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `ha_call_service` | Call a Home Assistant service to control a device. Use ha_list_services to discover available services and their parameters for each domain. | — |
| `ha_get_state` | Get the detailed state of a single Home Assistant entity, including all attributes (brightness, color, temperature setpoint, sensor readings, etc.). | — |
| `ha_list_entities` | List Home Assistant entities. Optionally filter by domain (light, switch, climate, sensor, binary_sensor, cover, fan, etc.) or by area name (living room, kitchen, bedroom, etc.). | — |
| `ha_list_services` | List available Home Assistant services (actions) for device control. Shows what actions can be performed on each device type and what parameters they accept. Use this to discover how to control devices found via ha_list_entities. | — |
## `honcho` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `honcho_conclude` | Write a conclusion about the user back to Honcho's memory. Conclusions are persistent facts that build the user's profile — preferences, corrections, clarifications, project context, or anything the user tells you that should be remembered… | — |
| `honcho_context` | Ask Honcho a natural language question and get a synthesized answer. Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. Can query about any peer: the user (default), the AI assistant, or any named p… | — |
| `honcho_profile` | Retrieve the user's peer card from Honcho — a curated list of key facts about them (name, role, preferences, communication style, patterns). Fast, no LLM reasoning, minimal cost. Use this at conversation start or when you need a quick fact… | — |
| `honcho_search` | Semantic search over Honcho's stored context about the user. Returns raw excerpts ranked by relevance to your query — no LLM synthesis. Cheaper and faster than honcho_context. Good when you want to find specific past facts and reason over… | — |
## `image_gen` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `image_generate` | Generate high-quality images from text prompts using FLUX 2 Pro model with automatic 2x upscaling. Creates detailed, artistic images that are automatically upscaled for hi-rez results. Returns a single upscaled image URL. Display it using… | FAL_KEY |
## `memory` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `memory` | Save important information to persistent memory that survives across sessions. Your memory appears in your system prompt at session start -- it's how you remember things about the user and your environment between conversations. WHEN TO SA… | — |
## `messaging` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `send_message` | Send a message to a connected messaging platform, or list available targets. IMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available tar… | — |
## `moa` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `mixture_of_agents` | Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced alg… | OPENROUTER_API_KEY |
## `rl` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `rl_check_status` | Get status and metrics for a training run. RATE LIMITED: enforces 30-minute minimum between checks for the same run. Returns WandB metrics: step, state, reward_mean, loss, percent_correct. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_edit_config` | Update a configuration field. Use rl_get_current_config() first to see all available fields for the selected environment. Each environment has different configurable options. Infrastructure settings (tokenizer, URLs, lora_rank, learning_ra… | TINKER_API_KEY, WANDB_API_KEY |
| `rl_get_current_config` | Get the current environment configuration. Returns only fields that can be modified: group_size, max_token_length, total_steps, steps_per_eval, use_wandb, wandb_name, max_num_workers. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_get_results` | Get final results and metrics for a completed training run. Returns final metrics and path to trained weights. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_list_environments` | List all available RL environments. Returns environment names, paths, and descriptions. TIP: Read the file_path with file tools to understand how each environment works (verifiers, data loading, rewards). | TINKER_API_KEY, WANDB_API_KEY |
| `rl_list_runs` | List all training runs (active and completed) with their status. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_select_environment` | Select an RL environment for training. Loads the environment's default configuration. After selecting, use rl_get_current_config() to see settings and rl_edit_config() to modify them. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_start_training` | Start a new RL training run with the current environment and config. Most training parameters (lora_rank, learning_rate, etc.) are fixed. Use rl_edit_config() to set group_size, batch_size, wandb_project before starting. WARNING: Training… | TINKER_API_KEY, WANDB_API_KEY |
| `rl_stop_training` | Stop a running training job. Use if metrics look bad, training is stagnant, or you want to try different settings. | TINKER_API_KEY, WANDB_API_KEY |
| `rl_test_inference` | Quick inference test for any environment. Runs a few steps of inference + scoring using OpenRouter. Default: 3 steps x 16 completions = 48 rollouts per model, testing 3 models = 144 total. Tests environment loading, prompt construction, in… | TINKER_API_KEY, WANDB_API_KEY |
## `session_search` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `session_search` | Search your long-term memory of past conversations. This is your recall -- every past session is searchable, and this tool summarizes what happened. USE THIS PROACTIVELY when: - The user says 'we did this before', 'remember when', 'last ti… | — |
## `skills` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `skill_manage` | Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live. Actions: create (full SKILL.m… | — |
| `skill_view` | Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a… | — |
| `skills_list` | List available skills (name + description). Use skill_view(name) to load full content. | — |
## `terminal` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `process` | Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (sen… | — |
| `terminal` | Execute shell commands on a Linux environment. Filesystem persists between calls. Do NOT use cat/head/tail to read files — use read_file instead. Do NOT use grep/rg/find to search — use search_files instead. Do NOT use ls to list directori… | — |
## `todo` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `todo` | Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list. Writing: - Provide 'todos' array to create/update items - merge=… | — |
## `tts` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `text_to_speech` | Convert text to speech audio. Returns a MEDIA: path that the platform delivers as a voice message. On Telegram it plays as a voice bubble, on Discord/WhatsApp as an audio attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider… | — |
## `vision` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `vision_analyze` | Analyze images using AI vision. Provides a comprehensive description and answers a specific question about the image content. | — |
## `web` toolset
| Tool | Description | Requires environment |
|------|-------------|----------------------|
| `web_extract` | Extract content from web page URLs. Returns page content in markdown format. Also works with PDF URLs (arxiv papers, documents, etc.) — pass the PDF link directly and it converts to markdown text. Pages under 5000 chars return full markdow… | FIRECRAWL_API_KEY |
| `web_search` | Search the web for information on any topic. Returns up to 5 relevant results with titles, URLs, and descriptions. | FIRECRAWL_API_KEY |
@@ -0,0 +1,49 @@
---
sidebar_position: 4
title: "Toolsets Reference"
description: "Reference for Hermes core, composite, platform, and dynamic toolsets"
---
# Toolsets Reference
Toolsets are named bundles of tools that you can enable with `hermes chat --toolsets ...`, configure per platform, or resolve inside the agent runtime.
| Toolset | Kind | Resolves to |
|---------|------|-------------|
| `browser` | core | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `web_search` |
| `clarify` | core | `clarify` |
| `code_execution` | core | `execute_code` |
| `cronjob` | core | `list_cronjobs`, `remove_cronjob`, `schedule_cronjob` |
| `debugging` | composite | `patch`, `process`, `read_file`, `search_files`, `terminal`, `web_extract`, `web_search`, `write_file` |
| `delegation` | core | `delegate_task` |
| `file` | core | `patch`, `read_file`, `search_files`, `write_file` |
| `hermes-cli` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-discord` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-email` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-gateway` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-homeassistant` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-signal` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-slack` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-telegram` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `hermes-whatsapp` | platform | `browser_back`, `browser_click`, `browser_close`, `browser_get_images`, `browser_navigate`, `browser_press`, `browser_scroll`, `browser_snapshot`, `browser_type`, `browser_vision`, `clarify`, `delegate_task`, `execute_code`, `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services`, `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search`, `image_generate`, `list_cronjobs`, `memory`, `mixture_of_agents`, `patch`, `process`, `read_file`, `remove_cronjob`, `schedule_cronjob`, `search_files`, `send_message`, `session_search`, `skill_manage`, `skill_view`, `skills_list`, `terminal`, `text_to_speech`, `todo`, `vision_analyze`, `web_extract`, `web_search`, `write_file` |
| `homeassistant` | core | `ha_call_service`, `ha_get_state`, `ha_list_entities`, `ha_list_services` |
| `honcho` | core | `honcho_conclude`, `honcho_context`, `honcho_profile`, `honcho_search` |
| `image_gen` | core | `image_generate` |
| `memory` | core | `memory` |
| `moa` | core | `mixture_of_agents` |
| `rl` | core | `rl_check_status`, `rl_edit_config`, `rl_get_current_config`, `rl_get_results`, `rl_list_environments`, `rl_list_runs`, `rl_select_environment`, `rl_start_training`, `rl_stop_training`, `rl_test_inference` |
| `safe` | composite | `image_generate`, `mixture_of_agents`, `vision_analyze`, `web_extract`, `web_search` |
| `search` | core | `web_search` |
| `session_search` | core | `session_search` |
| `skills` | core | `skill_manage`, `skill_view`, `skills_list` |
| `terminal` | core | `process`, `terminal` |
| `todo` | core | `todo` |
| `tts` | core | `text_to_speech` |
| `vision` | core | `vision_analyze` |
| `web` | core | `web_extract`, `web_search` |
## Dynamic toolsets
- `mcp-<server>` — generated at runtime for each configured MCP server.
- Custom toolsets can be created in configuration and resolved at startup.
- Wildcards: `all` and `*` expand to every registered toolset.

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