parallel-cli is a paid third-party vendor skill that requires
PARALLEL_API_KEY, but it was shipped in the default skills/ directory
with no env-var gate. This caused it to appear in every user's system
prompt even when they have no Parallel account or API key.
Move it to optional-skills/ so it is only visible through the Skills
Hub and must be explicitly installed. Also remove it from the default
skills catalog docs.
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials for token extraction
Users who configured their token via `hermes setup` have it stored in
~/.hermes/.env (GITHUB_TOKEN=...), not in ~/.git-credentials. On macOS
with osxkeychain as the default git credential helper, ~/.git-credentials
may not exist at all, causing silent 401 failures in all GitHub skills.
Add ~/.hermes/.env as the first fallback in the auth detection block and
the inline "Extracting the Token from Git Credentials" example.
Priority order: env var → ~/.hermes/.env → ~/.git-credentials → none
Part of fix for NousResearch/hermes-agent#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
* fix(github-auth): check ~/.hermes/.env before ~/.git-credentials
Fixes#3464
Adds 'hermes webhook' CLI subcommand and a skill — zero new model tools.
CLI commands (require webhook platform to be enabled):
hermes webhook subscribe <name> [--events, --prompt, --deliver, ...]
hermes webhook list
hermes webhook remove <name>
hermes webhook test <name>
All commands gate on webhook platform being enabled in config. If not
configured, prints setup instructions (gateway setup wizard, manual
config.yaml, or env vars).
The agent uses these via terminal tool, guided by the webhook-subscriptions
skill which documents setup, common patterns (GitHub, Stripe, CI/CD,
monitoring), prompt template syntax, security, and troubleshooting.
Adapter enhancement: webhook.py hot-reloads dynamic subscriptions from
~/.hermes/webhook_subscriptions.json on each incoming request (mtime-gated).
Static config.yaml routes always take precedence.
Docs: updated webhooks.md with Dynamic Subscriptions section, added
hermes webhook to cli-commands.md reference.
No new model tools. No toolset changes.
24 new tests for CLI CRUD, persistence, enabled-gate, and adapter
dynamic route loading.
* fix: respect DashScope v1 runtime mode for alibaba
Remove the hardcoded Alibaba branch from resolve_runtime_provider()
that forced api_mode='anthropic_messages' regardless of the base URL.
Alibaba now goes through the generic API-key provider path, which
auto-detects the protocol from the URL:
- /apps/anthropic → anthropic_messages (via endswith check)
- /v1 → chat_completions (default)
This fixes Alibaba setup with OpenAI-compatible DashScope endpoints
(e.g. coding-intl.dashscope.aliyuncs.com/v1) that were broken because
runtime always forced Anthropic mode even when setup saved a /v1 URL.
Based on PR #2024 by @kshitijk4poor.
* docs(skill): add split, merge, search examples to ocr-and-documents skill
Adds pymupdf examples for PDF splitting, merging, and text search
to the existing ocr-and-documents skill. No new dependencies — pymupdf
already covers all three operations natively.
---------
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
The install script creates venv/ but several docs referenced .venv/,
causing agents to fail with 'No such file or directory' when following
AGENTS.md instructions.
Fixes#2066
Adds the Hugging Face CLI (hf) reference as a built-in skill under
mlops/. Covers downloading/uploading models and datasets, repo
management, SQL queries on datasets, inference endpoints, Spaces,
buckets, and more.
Based on the official HF skill from huggingface/skills.
Skill now covers full CLI usage (hermes setup, hermes skills, hermes
tools, hermes config, session management, etc.), config file reference,
and expanded gateway commands.
Agent context notes for STT failure now mention the hermes-agent-setup
skill is available to help users configure Hermes features.
When a user sends a voice message and STT isn't configured, the gateway
now sends a clear message directly to the user explaining how to set up
voice transcription, rather than relying on the agent to relay an
injected context note (which often gets misinterpreted).
Also adds a hermes-agent-setup bundled skill covering STT/TTS setup,
tool configuration, dependency installation, and troubleshooting.
Add inference.sh as a built-in skill that uses the terminal tool to
run infsh CLI commands. No custom tools or tool registration — the
skill teaches the agent how to use the infsh binary via terminal.
Covers 150+ AI apps: image gen (FLUX, Reve, Seedream), video (Veo,
Wan, Seedance), LLMs, search (Tavily, Exa), 3D, avatars, and more.
Includes reference docs for authentication, app discovery, running
apps, and CLI command reference.
Based on PR #1021 by @okaris, reworked as a skill-only integration.
Co-authored-by: okaris <okaris@users.noreply.github.com>
Add inference.sh CLI (infsh) as a tool integration, giving agents
access to 150+ AI apps through a single CLI — image gen (FLUX, Reve,
Seedream), video (Veo, Wan, Seedance), LLMs, search (Tavily, Exa),
3D, avatar/lipsync, and more. One API key manages all services.
Tools:
- infsh: run any infsh CLI command (app list, app run, etc.)
- infsh_install: install the CLI if not present
Registered as an 'inference' toolset (opt-in, not in core tools).
Includes comprehensive skill docs with examples for all app categories.
Changes from original PR:
- NOT added to _HERMES_CORE_TOOLS (available via --toolsets inference)
- Added 12 tests covering tool registration, command execution,
error handling, timeout, JSON parsing, and install flow
Inspired by PR #1021 by @okaris.
Co-authored-by: okaris <okaris@users.noreply.github.com>
Salvaged from PR #1470 by adavyas.
Core fix: Honcho tool calls in a multi-session gateway could route to
the wrong session because honcho_tools.py relied on process-global
state. Now threads session context through the call chain:
AIAgent._invoke_tool() → handle_function_call() → registry.dispatch()
→ handler **kw → _resolve_session_context()
Changes:
- Add _resolve_session_context() to prefer per-call context over globals
- Plumb honcho_manager + honcho_session_key through handle_function_call
- Add sync_honcho=False to run_conversation() for synthetic flush turns
- Pass honcho_session_key through gateway memory flush lifecycle
- Harden gateway PID detection when /proc cmdline is unreadable
- Make interrupt test scripts import-safe for pytest-xdist
- Wrap BibTeX examples in Jekyll raw blocks for docs build
- Fix thread-order-dependent assertion in client lifecycle test
- Expand Honcho docs: session isolation, lifecycle, routing internals
Dropped from original PR:
- Indentation change in _create_request_openai_client that would move
client creation inside the lock (causes unnecessary contention)
Co-authored-by: adavyas <adavyas@users.noreply.github.com>
Store the pending OAuth state and code verifier between --auth-url and --auth-code so the manual headless flow can reuse Flow.fetch_token() without disabling PKCE.
Clarify that Parallel is an optional paid vendor workflow, add headless auth and context-chaining guidance, and align command examples more closely with upstream docs before salvaging PR #985.
* 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.
* 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.
Restore the ACP editor-integration implementation that was present on the
original PR branch but did not actually land in main.
Includes:
- acp_adapter/ server, session manager, event bridge, auth, permissions,
and tool helpers
- hermes acp subcommand and hermes-acp entry point
- hermes-acp curated toolset
- ACP registry manifest, setup guide, and ACP test suite
- jupyter-live-kernel data science skill from the original branch
Also updates the revived ACP code for current main by:
- resolving runtime providers through the modern shared provider router
- binding ACP sessions to per-session cwd task overrides
- tracking duplicate same-name tool calls with FIFO IDs
- restoring terminal approval callbacks after prompts
- normalizing supporting docs/skill metadata
Validated with tests/acp and the full pytest suite (-n0).
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)
- 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)
When a skill declares required_environment_variables in its YAML
frontmatter, missing env vars trigger a secure TUI prompt (identical
to the sudo password widget) when the skill is loaded. Secrets flow
directly to ~/.hermes/.env, never entering LLM context.
Key changes:
- New required_environment_variables frontmatter field for skills
- Secure TUI widget (masked input, 120s timeout)
- Gateway safety: messaging platforms show local setup guidance
- Legacy prerequisites.env_vars normalized into new format
- Remote backend handling: conservative setup_needed=True
- Env var name validation, file permissions hardened to 0o600
- Redact patterns extended for secret-related JSON fields
- 12 existing skills updated with prerequisites declarations
- ~48 new tests covering skip, timeout, gateway, remote backends
- Dynamic panel widget sizing (fixes hardcoded width from original PR)
Cherry-picked from PR #723 by kshitijk4poor, rebased onto current main
with conflict resolution.
Fixes#688
Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
- Tag duckduckgo-search skill with fallback_for_toolsets: [web] so it
auto-hides when Firecrawl is available and auto-shows when it isn't
- Add 'Conditional Activation' section to CONTRIBUTING.md with full
spec, semantics, and examples for all 4 frontmatter fields
- Add 'Conditional Activation (Fallback Skills)' section to the user-
facing skills docs with field reference table and practical example
- Update SKILL.md format examples in both docs to show the new fields
Follow-up to PR #785 (conditional skill activation feature).
Add comprehensive skill for building, testing, and debugging Hermes Agent
RL environments for Atropos training. Includes:
- SKILL.md: Full guide covering HermesAgentBaseEnv interface, required
methods, config class, CLI modes (serve/process/evaluate), reward
function patterns, common pitfalls, and minimum implementation checklist
- New 'Inference Setup' section: instructs the agent to always ask the
user for their inference provider (OpenRouter + model choice, self-hosted
VLLM endpoint, or other OpenAI-compatible API) before running tests
- references/agentresult-fields.md: AgentResult dataclass field reference
- references/atropos-base-env.md: Atropos BaseEnv API reference
- references/usage-patterns.md: Step-by-step patterns for process,
evaluate, serve, and smoke test modes
Will be auto-synced to ~/.hermes/skills/ via skills_sync.
Comprehensive skill for playing Pokemon Red/Blue via the pokemon-agent
package (NousResearch/pokemon-agent). Includes:
- Full startup procedure (uv venv, server, localhost.run dashboard tunnel)
- Save/load lifecycle and naming conventions
- Gameplay loop with emphasis on frequent vision checks
- Hard-learned navigation tips:
- Use vision every 2-4 steps (RAM state is blind to obstacles)
- Wait 2-3 seconds after door/stair warps for map transitions
- Sidestep after exiting buildings to avoid re-entering
- Hold B to speed Gen 1's slow text scrolling
- Ledges are one-way — use vision to find gaps
- Battle strategy, type chart, Gen 1 quirks
- Memory conventions with PKM: prefix
- Progression milestones through all 8 gyms + Elite Four
Adds a 'find-nearby' skill for discovering nearby places using
OpenStreetMap (Overpass + Nominatim). No API keys needed. Works with:
- Coordinates (from Telegram location pins)
- Addresses, cities, zip codes, landmarks (auto-geocoded)
- Multiple place types (restaurant, cafe, bar, pharmacy, etc.)
Returns names, distances, cuisine, hours, addresses, and Google Maps
links (pin + directions). 184-line stdlib-only script.
Also adds Telegram location message handling:
- New MessageType.LOCATION in gateway base
- Telegram adapter handles LOCATION and VENUE messages
- Injects lat/lon coordinates into conversation context
- Prompts agent to ask what the user wants nearby
Inspired by PR #422 (reimplemented with simpler script and broader
skill scope — addresses/cities/zips, not just Telegram coordinates).
Add MCP sampling/createMessage capability via SamplingHandler class.
Text-only sampling + tool use in sampling with governance (rate limits,
model whitelist, token caps, tool loop limits). Per-server audit metrics.
Based on concept from PR #366 by eren-karakus0. Restructured as class-based
design with bug fixes and tests using real MCP SDK types.
50 new tests, 2600 total passing.
Added pitfalls discovered during live abliteration testing:
- Models < 1B have fragmented refusal, respond poorly (0.5B: 60%→20%)
- Models 3B+ work much better (3B: 75%→0% with advanced defaults)
- aggressive method can backfire on small models (made it worse)
- Spectral certification RED is common even when refusal rate is 0%
- Fixed torch property: total_mem → total_memory
