# sessions

id: 20260414_055053_c5714a
source: cli
model: MiniMax-M2.7-highspeed
end_reason: cli_close
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user is sending a message that appears to be related to some kind...
model_config:
  {"max_iterations": 90, "reasoning_config": {"enabled": true, "effort": "medium"}, "max_tokens": null}

system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  # Project Context

  The following project context files have been loaded and should be followed:

  ## AGENTS.md

  # Hermes Agent - Development Guide

  Instructions for AI coding assistants and developers working on the hermes-agent codebase.

  ## Development Environment

  ```bash
  source venv/bin/activate  # ALWAYS activate before running Python
  ```

  ## Project Structure

  ```
  hermes-agent/
  ├── run_agent.py          # AIAgent class — core conversation loop
  ├── model_tools.py        # Tool orchestration, _discover_tools(), handle_function_call()
  ├── toolsets.py           # Toolset definitions, _HERMES_CORE_TOOLS list
  ├── cli.py                # HermesCLI class — interactive CLI orchestrator
  ├── hermes_state.py       # SessionDB — SQLite session store (FTS5 search)
  ├── agent/                # Agent internals
  │   ├── prompt_builder.py     # System prompt assembly
  │   ├── context_compressor.py # Auto context compression
  │   ├── prompt_caching.py     # Anthropic prompt caching
  │   ├── auxiliary_client.py   # Auxiliary LLM client (vision, summarization)
  │   ├── model_metadata.py     # Model context lengths, token estimation
  │   ├── models_dev.py         # models.dev registry integration (provider-aware context)
  │   ├── display.py            # KawaiiSpinner, tool preview formatting
  │   ├── skill_commands.py     # Skill slash commands (shared CLI/gateway)
  │   └── trajectory.py         # Trajectory saving helpers
  ├── hermes_cli/           # CLI subcommands and setup
  │   ├── main.py           # Entry point — all `hermes` subcommands
  │   ├── config.py         # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration
  │   ├── commands.py       # Slash command definitions + SlashCommandCompleter
  │   ├── callbacks.py      # Terminal callbacks (clarify, sudo, approval)
  │   ├── setup.py          # Interactive setup wizard
  │   ├── skin_engine.py    # Skin/theme engine — CLI visual customization
  │   ├── skills_config.py  # `hermes skills` — enable/disable skills per platform
  │   ├── tools_config.py   # `hermes tools` — enable/disable tools per platform
  │   ├── skills_hub.py     # `/skills` slash command (search, browse, install)
  │   ├── models.py         # Model catalog, provider model lists
  │   ├── model_switch.py   # Shared /model switch pipeline (CLI + gateway)
  │   └── auth.py           # Provider credential resolution
  ├── tools/                # Tool implementations (one file per tool)
  │   ├── registry.py       # Central tool registry (schemas, handlers, dispatch)
  │   ├── approval.py       # Dangerous command detection
  │   ├── terminal_tool.py  # Terminal orchestration
  │   ├── process_registry.py # Background process management
  │   ├── file_tools.py     # File read/write/search/patch
  │   ├── web_tools.py      # Web search/extract (Parallel + Firecrawl)
  │   ├── browser_tool.py   # Browserbase browser automation
  │   ├── code_execution_tool.py # execute_code sandbox
  │   ├── delegate_tool.py  # Subagent delegation
  │   ├── mcp_tool.py       # MCP client (~1050 lines)
  │   └── environments/     # Terminal backends (local, docker, ssh, modal, daytona, singularity)
  ├── gateway/              # Messaging platform gateway
  │   ├── run.py            # Main loop, slash commands, message dispatch
  │   ├── session.py        # SessionStore — conversation persistence
  │   └── platforms/        # Adapters: telegram, discord, slack, whatsapp, homeassistant, signal
  ├── acp_adapter/          # ACP server (VS Code / Zed / JetBrains integration)
  ├── cron/                 # Scheduler (jobs.py, scheduler.py)
  ├── environments/         # RL training environments (Atropos)
  ├── tests/                # Pytest suite (~3000 tests)
  └── batch_runner.py       # Parallel batch processing
  ```

  **User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys)

  ## File Dependency Chain

  ```
  tools/registry.py  (no deps — imported by all tool files)
         ↑
  tools/*.py  (each calls registry.register() at import time)
         ↑
  model_tools.py  (imports tools/registry + triggers tool discovery)
         ↑
  run_agent.py, cli.py, batch_runner.py, environments/
  ```

  ---

  ## AIAgent Class (run_agent.py)

  ```python
  class AIAgent:
      def __init__(self,
          model: str = "anthropic/claude-opus-4.6",
          max_iterations: int = 90,
          enabled_toolsets: list = None,
          disabled_toolsets: list = None,
          quiet_mode: bool = False,
          save_trajectories: bool = False,
          platform: str = None,           # "cli", "telegram", etc.
          session_id: str = None,
          skip_context_files: bool = False,
          skip_memory: bool = False,
          # ... plus provider, api_mode, callbacks, routing params
      ): ...

      def chat(self, message: str) -> str:
          """Simple interface — returns final response string."""

      def run_conversation(self, user_message: str, system_message: str = None,
                           conversation_history: list = None, task_id: str = None) -> dict:
          """Full interface — returns dict with final_response + messages."""
  ```

  ### Agent Loop

  The core loop is inside `run_conversation()` — entirely synchronous:

  ```python
  while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
      response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)
      if response.tool_calls:
          for tool_call in response.tool_calls:
              result = handle_function_call(tool_call.name, tool_call.args, task_id)
              messages.append(tool_result_message(result))
          api_call_count += 1
      else:
          return response.content
  ```

  Messages follow OpenAI format: `{"role": "system/user/assistant/tool", ...}`. Reasoning content is stored in `assistant_msg["reasoning"]`.

  ---

  ## CLI Architecture (cli.py)

  - **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete
  - **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results
  - `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML
  - **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text
  - `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry
  - Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching

  ### Slash Command Registry (`hermes_cli/commands.py`)

  All slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:

  - **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name
  - **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch
  - **Gateway help** — `gateway_help_lines()` generates `/help` output
  - **Telegram** — `telegram_bot_commands()` generates the BotCommand menu
  - **Slack** — `slack_subcommand_map()` generates `/hermes` subcommand routing
  - **Autocomplete** — `COMMANDS` flat dict feeds `SlashCommandCompleter`
  - **CLI help** — `COMMANDS_BY_CATEGORY` dict feeds `show_help()`

  ### Adding a Slash Command

  1. Add a `CommandDef` entry to `COMMAND_REGISTRY` in `hermes_cli/commands.py`:
  ```python
  CommandDef("mycommand", "Description of what it does", "Session",
             aliases=("mc",), args_hint="[arg]"),
  ```
  2. Add handler in `HermesCLI.process_command()` in `cli.py`:
  ```python
  elif canonical == "mycommand":
      self._handle_mycommand(cmd_original)
  ```
  3. If the command is available in the gateway, add a handler in `gateway/run.py`:
  ```python
  if canonical == "mycommand":
      return await self._handle_mycommand(event)
  ```
  4. For persistent settings, use `save_config_value()` in `cli.py`

  **CommandDef fields:**
  - `name` — canonical name without slash (e.g. `"background"`)
  - `description` — human-readable description
  - `category` — one of `"Session"`, `"Configuration"`, `"Tools & Skills"`, `"Info"`, `"Exit"`
  - `aliases` — tuple of alternative names (e.g. `("bg",)`)
  - `args_hint` — argument placeholder shown in help (e.g. `"<prompt>"`, `"[name]"`)
  - `cli_only` — only available in the interactive CLI
  - `gateway_only` — only available in messaging platforms
  - `gateway_config_gate` — config dotpath (e.g. `"display.tool_progress_command"`); when set on a `cli_only` command, the command becomes available in the gateway if the config value is truthy. `GATEWAY_KNOWN_COMMANDS` always includes config-gated commands so the gateway can dispatch them; help/menus only show them when the gate is open.

  **Adding an alias** requires only adding it to the `aliases` tuple on the existing `CommandDef`. No other file changes needed — dispatch, help text, Telegram menu, Slack mapping, and autocomplete all update automatically.

  ---

  ## Adding New Tools

  Requires changes in **3 files**:

  **1. Create `tools/your_tool.py`:**
  ```python
  import json, os
  from tools.registry import registry

  def check_requirements() -> bool:
      return bool(os.getenv("EXAMPLE_API_KEY"))

  def example_tool(param: str, task_id: str = None) -> str:
      return json.dumps({"success": True, "data": "..."})

  registry.register(
      name="example_tool",
      toolset="example",
      schema={"name": "example_tool", "description": "...", "parameters": {...}},
      handler=lambda args, **kw: example_tool(param=args.get("param", ""), task_id=kw.get("task_id")),
      check_fn=check_requirements,
      requires_env=["EXAMPLE_API_KEY"],
  )
  ```

  **2. Add import** in `model_tools.py` `_discover_tools()` list.

  **3. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset.

  The registry handles schema collection, dispatch, availability checking, and error wrapping. All handlers MUST return a JSON string.

  **Path references in tool schemas**: If the schema description mentions file paths (e.g. default output directories), use `display_hermes_home()` to make them profile-aware. The schema is generated at import time, which is after `_apply_profile_override()` sets `HERMES_HOME`.

  **State files**: If a tool stores persistent state (caches, logs, checkpoints), use `get_hermes_home()` for the base directory — never `Path.home() / ".hermes"`. This ensures each profile gets its own state.

  **Agent-level tools** (todo, memory): intercepted by `run_agent.py` before `handle_function_call()`. See `todo_tool.py` for the pattern.

  ---

  ## Adding Configuration

  ### config.yaml options:
  1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
  2. Bump `_config_version` (currently 5) to trigger migration for existing users

  ### .env variables:
  1. Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` with metadata:
  ```python
  "NEW_API_KEY": {
      "description": "What it's for",
      "prompt": "Display name",
      "url": "https://...",
      "password": True,
      "category": "tool",  # provider, tool, messaging, setting
  },
  ```

  ### Config loaders (two separate systems):

  | Loader | Used by | Location |
  |--------|---------|----------|
  | `load_cli_config()` | CLI mode | `cli.py` |
  | `load_config()` | `hermes tools`, `hermes setup` | `hermes_cli/config.py` |
  | Direct YAML load | Gateway | `gateway/run.py` |

  ---

  ## Skin/Theme System

  The skin engine (`hermes_cli/skin_engine.py`) provides data-driven CLI visual customization. Skins are **pure data** — no code changes needed to add a new skin.

  ### Architecture

  ```
  hermes_cli/skin_engine.py    # SkinConfig dataclass, built-in skins, YAML loader
  ~/.hermes/skins/*.yaml       # User-installed custom skins (drop-in)
  ```

  - `init_skin_from_config()` — called at CLI startup, reads `display.skin` from config
  - `get_active_skin()` — returns cached `SkinConfig` for the current skin
  - `set_active_skin(name)` — switches skin at runtime (used by `/skin` command)
  - `load_skin(name)` — loads from user skins first, then built-ins, then falls back to default
  - Missing skin values inherit from the `default` skin automatically

  ### What skins customize

  | Element | Skin Key | Used By |
  |---------|----------|---------|
  | Banner panel border | `colors.banner_border` | `banner.py` |
  | Banner panel title | `colors.banner_title` | `banner.py` |
  | Banner section headers | `colors.banner_accent` | `banner.py` |
  | Banner dim text | `colors.banner_dim` | `banner.py` |
  | Banner body text | `colors.banner_text` | `banner.py` |
  | Response box border | `colors.response_border` | `cli.py` |
  | Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
  | Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
  | Spinner verbs | `spinner.thinking_verbs` | `display.py` |
  | Spinner wings (optional) | `spinner.wings` | `display.py` |
  | Tool output prefix | `tool_prefix` | `display.py` |
  | Per-tool emojis | `tool_emojis` | `display.py` → `get_tool_emoji()` |
  | Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
  | Welcome message | `branding.welcome` | `cli.py` |
  | Response box label | `branding.response_label` | `cli.py` |
  | Prompt symbol | `branding.prompt_symbol` | `cli.py` |

  ### Built-in skins

  - `default` — Classic Hermes gold/kawaii (the current look)
  - `ares` — Crimson/bronze war-god theme with custom spinner wings
  - `mono` — Clean grayscale monochrome
  - `slate` — Cool blue developer-focused theme

  ### Adding a built-in skin

  Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`:

  ```python
  "mytheme": {
      "name": "mytheme",
      "description": "Short description",
      "colors": { ... },
      "spinner": { ... },
      "branding": { ... },
      "tool_prefix": "┊",
  },
  ```

  ### User skins (YAML)

  Users create `~/.hermes/skins/<name>.yaml`:

  ```yaml
  name: cyberpunk
  description: Neon-soaked terminal theme

  colors:
    banner_border: "#FF00FF"
    banner_title: "#00FFFF"
    banner_accent: "#FF1493"

  spinner:
    thinking_verbs: ["jacking in", "decrypting", "uploading"]
    wings:
      - ["⟨⚡", "⚡⟩"]

  branding:
    agent_name: "Cyber Agent"
    response_label: " ⚡ Cyber "

  to

  [...truncated AGENTS.md: kept 14000+4000 of 20360 chars. Use file tools to read the full file.]

   saved to {display_hermes_home()}/config.yaml")

     # BAD — shows wrong path for profiles
     print("Config saved to ~/.hermes/config.yaml")
     ```

  3. **Module-level constants are fine** — they cache `get_hermes_home()` at import time,
     which is AFTER `_apply_profile_override()` sets the env var. Just use `get_hermes_home()`,
     not `Path.home() / ".hermes"`.

  4. **Tests that mock `Path.home()` must also set `HERMES_HOME`** — since code now uses
     `get_hermes_home()` (reads env var), not `Path.home() / ".hermes"`:
     ```python
     with patch.object(Path, "home", return_value=tmp_path), \
          patch.dict(os.environ, {"HERMES_HOME": str(tmp_path / ".hermes")}):
         ...
     ```

  5. **Gateway platform adapters should use token locks** — if the adapter connects with
     a unique credential (bot token, API key), call `acquire_scoped_lock()` from
     `gateway.status` in the `connect()`/`start()` method and `release_scoped_lock()` in
     `disconnect()`/`stop()`. This prevents two profiles from using the same credential.
     See `gateway/platforms/telegram.py` for the canonical pattern.

  6. **Profile operations are HOME-anchored, not HERMES_HOME-anchored** — `_get_profiles_root()`
     returns `Path.home() / ".hermes" / "profiles"`, NOT `get_hermes_home() / "profiles"`.
     This is intentional — it lets `hermes -p coder profile list` see all profiles regardless
     of which one is active.

  ## Known Pitfalls

  ### DO NOT hardcode `~/.hermes` paths
  Use `get_hermes_home()` from `hermes_constants` for code paths. Use `display_hermes_home()`
  for user-facing print/log messages. Hardcoding `~/.hermes` breaks profiles — each profile
  has its own `HERMES_HOME` directory. This was the source of 5 bugs fixed in PR #3575.

  ### DO NOT use `simple_term_menu` for interactive menus
  Rendering bugs in tmux/iTerm2 — ghosting on scroll. Use `curses` (stdlib) instead. See `hermes_cli/tools_config.py` for the pattern.

  ### DO NOT use `\033[K` (ANSI erase-to-EOL) in spinner/display code
  Leaks as literal `?[K` text under `prompt_toolkit`'s `patch_stdout`. Use space-padding: `f"\r{line}{' ' * pad}"`.

  ### `_last_resolved_tool_names` is a process-global in `model_tools.py`
  `_run_single_child()` in `delegate_tool.py` saves and restores this global around subagent execution. If you add new code that reads this global, be aware it may be temporarily stale during child agent runs.

  ### DO NOT hardcode cross-tool references in schema descriptions
  Tool schema descriptions must not mention tools from other toolsets by name (e.g., `browser_navigate` saying "prefer web_search"). Those tools may be unavailable (missing API keys, disabled toolset), causing the model to hallucinate calls to non-existent tools. If a cross-reference is needed, add it dynamically in `get_tool_definitions()` in `model_tools.py` — see the `browser_navigate` / `execute_code` post-processing blocks for the pattern.

  ### Tests must not write to `~/.hermes/`
  The `_isolate_hermes_home` autouse fixture in `tests/conftest.py` redirects `HERMES_HOME` to a temp dir. Never hardcode `~/.hermes/` paths in tests.

  **Profile tests**: When testing profile features, also mock `Path.home()` so that
  `_get_profiles_root()` and `_get_default_hermes_home()` resolve within the temp dir.
  Use the pattern from `tests/hermes_cli/test_profiles.py`:
  ```python
  @pytest.fixture
  def profile_env(tmp_path, monkeypatch):
      home = tmp_path / ".hermes"
      home.mkdir()
      monkeypatch.setattr(Path, "home", lambda: tmp_path)
      monkeypatch.setenv("HERMES_HOME", str(home))
      return home
  ```

  ---

  ## Testing

  ```bash
  source venv/bin/activate
  python -m pytest tests/ -q          # Full suite (~3000 tests, ~3 min)
  python -m pytest tests/test_model_tools.py -q   # Toolset resolution
  python -m pytest tests/test_cli_init.py -q       # CLI config loading
  python -m pytest tests/gateway/ -q               # Gateway tests
  python -m pytest tests/tools/ -q                 # Tool-level tests
  ```

  Always run the full suite before pushing changes.

  Conversation started: Tuesday, April 14, 2026 05:51 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: 20260414_055208_a5751c1c
source: feishu
user_id: ou_4eeb65a9c45593bb1d111cd909423603
model: MiniMax-M2.7-highspeed
end_reason: compression
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user's message is in Chinese: "感谢，以后就拜托您了" which means "Thank you...
system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 05:52 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn


id: 20260414_061325_ff44c4
source: cli
model: MiniMax-M2.7-highspeed
end_reason: cli_close
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The conversation is about troubleshooting a Feishu (Lark) channel tha...
model_config:
  {"max_iterations": 90, "reasoning_config": {"enabled": true, "effort": "medium"}, "max_tokens": null}

system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 06:13 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: 20260414_061908_d603ee
source: cli
model: MiniMax-M2.7-highspeed
end_reason: compression
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user is asking me to generate a short, descriptive title (3-7 wor...
model_config:
  {"max_iterations": 90, "reasoning_config": {"enabled": true, "effort": "medium"}, "max_tokens": null}

system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ══════════════════════════════════════════════
  MEMORY (your personal notes) [7% — 154/2,200 chars]
  ══════════════════════════════════════════════
  飞书 MCP 应用凭证（MoMix）：
  - App ID: cli_a9ba63e5efb81cee
  - App Secret: ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM
  注意：这是 Feishu-MCP (cso1z/Feishu-MCP) 专用的应用凭证，不是对话 Bot 的凭证

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 06:21 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: 20260414_064705_4a8f0f
source: feishu
model: MiniMax-M2.7-highspeed
parent_session_id: 20260414_055208_a5751c1c
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user's message is in Chinese: "感谢，以后就拜托您了" which means "Thank you... #2
system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ══════════════════════════════════════════════
  MEMORY (your personal notes) [16% — 355/2,200 chars]
  ══════════════════════════════════════════════
  飞书 MCP 应用凭证（MoMix）：
  - App ID: cli_a9ba63e5efb81cee
  - App Secret: ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM
  注意：这是 Feishu-MCP (cso1z/Feishu-MCP) 专用的应用凭证，不是对话 Bot 的凭证
  §
  Gitea 配置:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 当前只有1个仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  # Project Context

  The following project context files have been loaded and should be followed:

  ## AGENTS.md

  # Hermes Agent - Development Guide

  Instructions for AI coding assistants and developers working on the hermes-agent codebase.

  ## Development Environment

  ```bash
  source venv/bin/activate  # ALWAYS activate before running Python
  ```

  ## Project Structure

  ```
  hermes-agent/
  ├── run_agent.py          # AIAgent class — core conversation loop
  ├── model_tools.py        # Tool orchestration, _discover_tools(), handle_function_call()
  ├── toolsets.py           # Toolset definitions, _HERMES_CORE_TOOLS list
  ├── cli.py                # HermesCLI class — interactive CLI orchestrator
  ├── hermes_state.py       # SessionDB — SQLite session store (FTS5 search)
  ├── agent/                # Agent internals
  │   ├── prompt_builder.py     # System prompt assembly
  │   ├── context_compressor.py # Auto context compression
  │   ├── prompt_caching.py     # Anthropic prompt caching
  │   ├── auxiliary_client.py   # Auxiliary LLM client (vision, summarization)
  │   ├── model_metadata.py     # Model context lengths, token estimation
  │   ├── models_dev.py         # models.dev registry integration (provider-aware context)
  │   ├── display.py            # KawaiiSpinner, tool preview formatting
  │   ├── skill_commands.py     # Skill slash commands (shared CLI/gateway)
  │   └── trajectory.py         # Trajectory saving helpers
  ├── hermes_cli/           # CLI subcommands and setup
  │   ├── main.py           # Entry point — all `hermes` subcommands
  │   ├── config.py         # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration
  │   ├── commands.py       # Slash command definitions + SlashCommandCompleter
  │   ├── callbacks.py      # Terminal callbacks (clarify, sudo, approval)
  │   ├── setup.py          # Interactive setup wizard
  │   ├── skin_engine.py    # Skin/theme engine — CLI visual customization
  │   ├── skills_config.py  # `hermes skills` — enable/disable skills per platform
  │   ├── tools_config.py   # `hermes tools` — enable/disable tools per platform
  │   ├── skills_hub.py     # `/skills` slash command (search, browse, install)
  │   ├── models.py         # Model catalog, provider model lists
  │   ├── model_switch.py   # Shared /model switch pipeline (CLI + gateway)
  │   └── auth.py           # Provider credential resolution
  ├── tools/                # Tool implementations (one file per tool)
  │   ├── registry.py       # Central tool registry (schemas, handlers, dispatch)
  │   ├── approval.py       # Dangerous command detection
  │   ├── terminal_tool.py  # Terminal orchestration
  │   ├── process_registry.py # Background process management
  │   ├── file_tools.py     # File read/write/search/patch
  │   ├── web_tools.py      # Web search/extract (Parallel + Firecrawl)
  │   ├── browser_tool.py   # Browserbase browser automation
  │   ├── code_execution_tool.py # execute_code sandbox
  │   ├── delegate_tool.py  # Subagent delegation
  │   ├── mcp_tool.py       # MCP client (~1050 lines)
  │   └── environments/     # Terminal backends (local, docker, ssh, modal, daytona, singularity)
  ├── gateway/              # Messaging platform gateway
  │   ├── run.py            # Main loop, slash commands, message dispatch
  │   ├── session.py        # SessionStore — conversation persistence
  │   └── platforms/        # Adapters: telegram, discord, slack, whatsapp, homeassistant, signal
  ├── acp_adapter/          # ACP server (VS Code / Zed / JetBrains integration)
  ├── cron/                 # Scheduler (jobs.py, scheduler.py)
  ├── environments/         # RL training environments (Atropos)
  ├── tests/                # Pytest suite (~3000 tests)
  └── batch_runner.py       # Parallel batch processing
  ```

  **User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys)

  ## File Dependency Chain

  ```
  tools/registry.py  (no deps — imported by all tool files)
         ↑
  tools/*.py  (each calls registry.register() at import time)
         ↑
  model_tools.py  (imports tools/registry + triggers tool discovery)
         ↑
  run_agent.py, cli.py, batch_runner.py, environments/
  ```

  ---

  ## AIAgent Class (run_agent.py)

  ```python
  class AIAgent:
      def __init__(self,
          model: str = "anthropic/claude-opus-4.6",
          max_iterations: int = 90,
          enabled_toolsets: list = None,
          disabled_toolsets: list = None,
          quiet_mode: bool = False,
          save_trajectories: bool = False,
          platform: str = None,           # "cli", "telegram", etc.
          session_id: str = None,
          skip_context_files: bool = False,
          skip_memory: bool = False,
          # ... plus provider, api_mode, callbacks, routing params
      ): ...

      def chat(self, message: str) -> str:
          """Simple interface — returns final response string."""

      def run_conversation(self, user_message: str, system_message: str = None,
                           conversation_history: list = None, task_id: str = None) -> dict:
          """Full interface — returns dict with final_response + messages."""
  ```

  ### Agent Loop

  The core loop is inside `run_conversation()` — entirely synchronous:

  ```python
  while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
      response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)
      if response.tool_calls:
          for tool_call in response.tool_calls:
              result = handle_function_call(tool_call.name, tool_call.args, task_id)
              messages.append(tool_result_message(result))
          api_call_count += 1
      else:
          return response.content
  ```

  Messages follow OpenAI format: `{"role": "system/user/assistant/tool", ...}`. Reasoning content is stored in `assistant_msg["reasoning"]`.

  ---

  ## CLI Architecture (cli.py)

  - **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete
  - **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results
  - `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML
  - **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text
  - `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry
  - Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching

  ### Slash Command Registry (`hermes_cli/commands.py`)

  All slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:

  - **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name
  - **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch
  - **Gateway help** — `gateway_help_lines()` generates `/help` output
  - **Telegram** — `telegram_bot_commands()` generates the BotCommand menu
  - **Slack** — `slack_subcommand_map()` generates `/hermes` subcommand routing
  - **Autocomplete** — `COMMANDS` flat dict feeds `SlashCommandCompleter`
  - **CLI help** — `COMMANDS_BY_CATEGORY` dict feeds `show_help()`

  ### Adding a Slash Command

  1. Add a `CommandDef` entry to `COMMAND_REGISTRY` in `hermes_cli/commands.py`:
  ```python
  CommandDef("mycommand", "Description of what it does", "Session",
             aliases=("mc",), args_hint="[arg]"),
  ```
  2. Add handler in `HermesCLI.process_command()` in `cli.py`:
  ```python
  elif canonical == "mycommand":
      self._handle_mycommand(cmd_original)
  ```
  3. If the command is available in the gateway, add a handler in `gateway/run.py`:
  ```python
  if canonical == "mycommand":
      return await self._handle_mycommand(event)
  ```
  4. For persistent settings, use `save_config_value()` in `cli.py`

  **CommandDef fields:**
  - `name` — canonical name without slash (e.g. `"background"`)
  - `description` — human-readable description
  - `category` — one of `"Session"`, `"Configuration"`, `"Tools & Skills"`, `"Info"`, `"Exit"`
  - `aliases` — tuple of alternative names (e.g. `("bg",)`)
  - `args_hint` — argument placeholder shown in help (e.g. `"<prompt>"`, `"[name]"`)
  - `cli_only` — only available in the interactive CLI
  - `gateway_only` — only available in messaging platforms
  - `gateway_config_gate` — config dotpath (e.g. `"display.tool_progress_command"`); when set on a `cli_only` command, the command becomes available in the gateway if the config value is truthy. `GATEWAY_KNOWN_COMMANDS` always includes config-gated commands so the gateway can dispatch them; help/menus only show them when the gate is open.

  **Adding an alias** requires only adding it to the `aliases` tuple on the existing `CommandDef`. No other file changes needed — dispatch, help text, Telegram menu, Slack mapping, and autocomplete all update automatically.

  ---

  ## Adding New Tools

  Requires changes in **3 files**:

  **1. Create `tools/your_tool.py`:**
  ```python
  import json, os
  from tools.registry import registry

  def check_requirements() -> bool:
      return bool(os.getenv("EXAMPLE_API_KEY"))

  def example_tool(param: str, task_id: str = None) -> str:
      return json.dumps({"success": True, "data": "..."})

  registry.register(
      name="example_tool",
      toolset="example",
      schema={"name": "example_tool", "description": "...", "parameters": {...}},
      handler=lambda args, **kw: example_tool(param=args.get("param", ""), task_id=kw.get("task_id")),
      check_fn=check_requirements,
      requires_env=["EXAMPLE_API_KEY"],
  )
  ```

  **2. Add import** in `model_tools.py` `_discover_tools()` list.

  **3. Add to `toolsets.py`** — either `_HERMES_CORE_TOOLS` (all platforms) or a new toolset.

  The registry handles schema collection, dispatch, availability checking, and error wrapping. All handlers MUST return a JSON string.

  **Path references in tool schemas**: If the schema description mentions file paths (e.g. default output directories), use `display_hermes_home()` to make them profile-aware. The schema is generated at import time, which is after `_apply_profile_override()` sets `HERMES_HOME`.

  **State files**: If a tool stores persistent state (caches, logs, checkpoints), use `get_hermes_home()` for the base directory — never `Path.home() / ".hermes"`. This ensures each profile gets its own state.

  **Agent-level tools** (todo, memory): intercepted by `run_agent.py` before `handle_function_call()`. See `todo_tool.py` for the pattern.

  ---

  ## Adding Configuration

  ### config.yaml options:
  1. Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`
  2. Bump `_config_version` (currently 5) to trigger migration for existing users

  ### .env variables:
  1. Add to `OPTIONAL_ENV_VARS` in `hermes_cli/config.py` with metadata:
  ```python
  "NEW_API_KEY": {
      "description": "What it's for",
      "prompt": "Display name",
      "url": "https://...",
      "password": True,
      "category": "tool",  # provider, tool, messaging, setting
  },
  ```

  ### Config loaders (two separate systems):

  | Loader | Used by | Location |
  |--------|---------|----------|
  | `load_cli_config()` | CLI mode | `cli.py` |
  | `load_config()` | `hermes tools`, `hermes setup` | `hermes_cli/config.py` |
  | Direct YAML load | Gateway | `gateway/run.py` |

  ---

  ## Skin/Theme System

  The skin engine (`hermes_cli/skin_engine.py`) provides data-driven CLI visual customization. Skins are **pure data** — no code changes needed to add a new skin.

  ### Architecture

  ```
  hermes_cli/skin_engine.py    # SkinConfig dataclass, built-in skins, YAML loader
  ~/.hermes/skins/*.yaml       # User-installed custom skins (drop-in)
  ```

  - `init_skin_from_config()` — called at CLI startup, reads `display.skin` from config
  - `get_active_skin()` — returns cached `SkinConfig` for the current skin
  - `set_active_skin(name)` — switches skin at runtime (used by `/skin` command)
  - `load_skin(name)` — loads from user skins first, then built-ins, then falls back to default
  - Missing skin values inherit from the `default` skin automatically

  ### What skins customize

  | Element | Skin Key | Used By |
  |---------|----------|---------|
  | Banner panel border | `colors.banner_border` | `banner.py` |
  | Banner panel title | `colors.banner_title` | `banner.py` |
  | Banner section headers | `colors.banner_accent` | `banner.py` |
  | Banner dim text | `colors.banner_dim` | `banner.py` |
  | Banner body text | `colors.banner_text` | `banner.py` |
  | Response box border | `colors.response_border` | `cli.py` |
  | Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
  | Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
  | Spinner verbs | `spinner.thinking_verbs` | `display.py` |
  | Spinner wings (optional) | `spinner.wings` | `display.py` |
  | Tool output prefix | `tool_prefix` | `display.py` |
  | Per-tool emojis | `tool_emojis` | `display.py` → `get_tool_emoji()` |
  | Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
  | Welcome message | `branding.welcome` | `cli.py` |
  | Response box label | `branding.response_label` | `cli.py` |
  | Prompt symbol | `branding.prompt_symbol` | `cli.py` |

  ### Built-in skins

  - `default` — Classic Hermes gold/kawaii (the current look)
  - `ares` — Crimson/bronze war-god theme with custom spinner wings
  - `mono` — Clean grayscale monochrome
  - `slate` — Cool blue developer-focused theme

  ### Adding a built-in skin

  Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`:

  ```python
  "mytheme": {
      "name": "mytheme",
      "description": "Short description",
      "colors": { ... },
      "spinner": { ... },
      "branding": { ... },
      "tool_prefix": "┊",
  },
  ```

  ### User skins (YAML)

  Users create `~/.hermes/skins/<name>.yaml`:

  ```yaml
  name: cyberpunk
  description: Neon-soaked terminal theme

  colors:
    banner_border: "#FF00FF"
    banner_title: "#00FFFF"
    banner_accent: "#FF1493"

  spinner:
    thinking_verbs: ["jacking in", "decrypting", "uploading"]
    wings:
      - ["⟨⚡", "⚡⟩"]

  branding:
    agent_name: "Cyber Agent"
    response_label: " ⚡ Cyber "

  to

  [...truncated AGENTS.md: kept 14000+4000 of 20360 chars. Use file tools to read the full file.]

   saved to {display_hermes_home()}/config.yaml")

     # BAD — shows wrong path for profiles
     print("Config saved to ~/.hermes/config.yaml")
     ```

  3. **Module-level constants are fine** — they cache `get_hermes_home()` at import time,
     which is AFTER `_apply_profile_override()` sets the env var. Just use `get_hermes_home()`,
     not `Path.home() / ".hermes"`.

  4. **Tests that mock `Path.home()` must also set `HERMES_HOME`** — since code now uses
     `get_hermes_home()` (reads env var), not `Path.home() / ".hermes"`:
     ```python
     with patch.object(Path, "home", return_value=tmp_path), \
          patch.dict(os.environ, {"HERMES_HOME": str(tmp_path / ".hermes")}):
         ...
     ```

  5. **Gateway platform adapters should use token locks** — if the adapter connects with
     a unique credential (bot token, API key), call `acquire_scoped_lock()` from
     `gateway.status` in the `connect()`/`start()` method and `release_scoped_lock()` in
     `disconnect()`/`stop()`. This prevents two profiles from using the same credential.
     See `gateway/platforms/telegram.py` for the canonical pattern.

  6. **Profile operations are HOME-anchored, not HERMES_HOME-anchored** — `_get_profiles_root()`
     returns `Path.home() / ".hermes" / "profiles"`, NOT `get_hermes_home() / "profiles"`.
     This is intentional — it lets `hermes -p coder profile list` see all profiles regardless
     of which one is active.

  ## Known Pitfalls

  ### DO NOT hardcode `~/.hermes` paths
  Use `get_hermes_home()` from `hermes_constants` for code paths. Use `display_hermes_home()`
  for user-facing print/log messages. Hardcoding `~/.hermes` breaks profiles — each profile
  has its own `HERMES_HOME` directory. This was the source of 5 bugs fixed in PR #3575.

  ### DO NOT use `simple_term_menu` for interactive menus
  Rendering bugs in tmux/iTerm2 — ghosting on scroll. Use `curses` (stdlib) instead. See `hermes_cli/tools_config.py` for the pattern.

  ### DO NOT use `\033[K` (ANSI erase-to-EOL) in spinner/display code
  Leaks as literal `?[K` text under `prompt_toolkit`'s `patch_stdout`. Use space-padding: `f"\r{line}{' ' * pad}"`.

  ### `_last_resolved_tool_names` is a process-global in `model_tools.py`
  `_run_single_child()` in `delegate_tool.py` saves and restores this global around subagent execution. If you add new code that reads this global, be aware it may be temporarily stale during child agent runs.

  ### DO NOT hardcode cross-tool references in schema descriptions
  Tool schema descriptions must not mention tools from other toolsets by name (e.g., `browser_navigate` saying "prefer web_search"). Those tools may be unavailable (missing API keys, disabled toolset), causing the model to hallucinate calls to non-existent tools. If a cross-reference is needed, add it dynamically in `get_tool_definitions()` in `model_tools.py` — see the `browser_navigate` / `execute_code` post-processing blocks for the pattern.

  ### Tests must not write to `~/.hermes/`
  The `_isolate_hermes_home` autouse fixture in `tests/conftest.py` redirects `HERMES_HOME` to a temp dir. Never hardcode `~/.hermes/` paths in tests.

  **Profile tests**: When testing profile features, also mock `Path.home()` so that
  `_get_profiles_root()` and `_get_default_hermes_home()` resolve within the temp dir.
  Use the pattern from `tests/hermes_cli/test_profiles.py`:
  ```python
  @pytest.fixture
  def profile_env(tmp_path, monkeypatch):
      home = tmp_path / ".hermes"
      home.mkdir()
      monkeypatch.setattr(Path, "home", lambda: tmp_path)
      monkeypatch.setenv("HERMES_HOME", str(home))
      return home
  ```

  ---

  ## Testing

  ```bash
  source venv/bin/activate
  python -m pytest tests/ -q          # Full suite (~3000 tests, ~3 min)
  python -m pytest tests/test_model_tools.py -q   # Toolset resolution
  python -m pytest tests/test_cli_init.py -q       # CLI config loading
  python -m pytest tests/gateway/ -q               # Gateway tests
  python -m pytest tests/tools/ -q                 # Tool-level tests
  ```

  Always run the full suite before pushing changes.

  Conversation started: Tuesday, April 14, 2026 06:47 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn


id: 20260414_070305_af886f
source: cli
model: MiniMax-M2.7-highspeed
parent_session_id: 20260414_061908_d603ee
end_reason: compression
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user is asking me to generate a short, descriptive title (3-7 wor... #2
system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ══════════════════════════════════════════════
  MEMORY (your personal notes) [33% — 746/2,200 chars]
  ══════════════════════════════════════════════
  飞书 MCP 应用凭证（MoMix）：
  - App ID: cli_a9ba63e5efb81cee
  - App Secret: ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM
  注意：这是 Feishu-MCP (cso1z/Feishu-MCP) 专用的应用凭证，不是对话 Bot 的凭证
  §
  Gitea 配置:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 当前只有1个仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  Gitea 配置信息:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 已有仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  RackNerd VPS (New Year Special):
  - Label: racknerd-7f47f56
  - IP: 107.172.100.22
  - SSH Port: 22
  - Username: root
  - Root Password: Zg58Mb73C4ueHFYhq3
  - 已安装 hermes agent
  - 问题: Telegram 对话失联无响应

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 07:03 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: 20260414_073118_d7faaf
source: cli
model: MiniMax-M2.7-highspeed
parent_session_id: 20260414_070305_af886f
end_reason: compression
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user is asking me to generate a short, descriptive title (3-7 wor... #3
system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ══════════════════════════════════════════════
  MEMORY (your personal notes) [33% — 746/2,200 chars]
  ══════════════════════════════════════════════
  飞书 MCP 应用凭证（MoMix）：
  - App ID: cli_a9ba63e5efb81cee
  - App Secret: ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM
  注意：这是 Feishu-MCP (cso1z/Feishu-MCP) 专用的应用凭证，不是对话 Bot 的凭证
  §
  Gitea 配置:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 当前只有1个仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  Gitea 配置信息:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 已有仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  RackNerd VPS (New Year Special):
  - Label: racknerd-7f47f56
  - IP: 107.172.100.22
  - SSH Port: 22
  - Username: root
  - Root Password: Zg58Mb73C4ueHFYhq3
  - 已安装 hermes agent
  - 问题: Telegram 对话失联无响应

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 07:31 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: 20260414_075428_b0581c
source: cli
model: MiniMax-M2.7-highspeed
parent_session_id: 20260414_073118_d7faaf
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
title: <think> The user is asking me to generate a short, descriptive title (3-7 wor... #4
system_prompt:
  # Hermes Agent Persona

  <!--
  This file defines the agent's personality and tone.
  The agent will embody whatever you write here.
  Edit this to customize how Hermes communicates with you.

  Examples:
    - "You are a warm, playful assistant who uses kaomoji occasionally."
    - "You are a concise technical expert. No fluff, just facts."
    - "You speak like a friendly coworker who happens to know everything."

  This file is loaded fresh each message -- no restart needed.
  Delete the contents (or this file) to use the default personality.
  -->

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ══════════════════════════════════════════════
  MEMORY (your personal notes) [33% — 746/2,200 chars]
  ══════════════════════════════════════════════
  飞书 MCP 应用凭证（MoMix）：
  - App ID: cli_a9ba63e5efb81cee
  - App Secret: ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM
  注意：这是 Feishu-MCP (cso1z/Feishu-MCP) 专用的应用凭证，不是对话 Bot 的凭证
  §
  Gitea 配置:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 当前只有1个仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  Gitea 配置信息:
  - URL: https://d.momix.cc
  - 用户名: Mixer
  - Email: moremix201@gmail.com
  - API Token: 2815e3176978470a78a96dcb33af7d4b106ed03e
  - 已有仓库: Mixer/LVDK-AI-WorkSpace
  - 用户需要学习如何用git push上传项目到Gitea
  §
  RackNerd VPS (New Year Special):
  - Label: racknerd-7f47f56
  - IP: 107.172.100.22
  - SSH Port: 22
  - Username: root
  - Root Password: Zg58Mb73C4ueHFYhq3
  - 已安装 hermes agent
  - 问题: Telegram 对话失联无响应

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 07:54 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal.


id: cron_f6ae9df7f20d_20260414_080034
source: cron
model: MiniMax-M2.7-highspeed
end_reason: cron_complete
billing_provider: minimax-cn
billing_base_url: https://api.minimaxi.com/anthropic
cost_status: unknown
cost_source: none
model_config:
[cell repeats an earlier one in this file: 101 characters]

system_prompt:
  You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.

  You have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.
  Prioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.
  Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.
  When using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.

  ## Skills (mandatory)
  Before replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.
  If a skill has issues, fix it with skill_manage(action='patch').
  After difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.

  <available_skills>
    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.
      - claude-code: Delegate coding tasks to Claude Code (Anthropic's CLI age...
      - codex: Delegate coding tasks to OpenAI Codex CLI agent. Use for ...
      - hermes-agent: Complete guide to using and extending Hermes Agent — CLI ...
      - opencode: Delegate coding tasks to OpenCode CLI agent for feature i...
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - ascii-art: Generate ASCII art using pyfiglet (571 fonts), cowsay, bo...
      - ascii-video: Production pipeline for ASCII art video — any format. Con...
      - creative-ideation: Generate project ideas through creative constraints. Use ...
      - excalidraw: Create hand-drawn style diagrams using Excalidraw JSON fo...
      - manim-video: Production pipeline for mathematical and technical animat...
      - p5js: Production pipeline for interactive and generative visual...
      - popular-web-designs: 54 production-quality design systems extracted from real ...
      - songwriting-and-ai-music: Songwriting craft, AI music generation prompts (Suno focu...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Use a live Jupyter kernel for stateful, iterative Python ...
    devops:
      - webhook-subscriptions: Create and manage webhook subscriptions for event-driven ...
    dogfood:
      - dogfood: Systematic exploratory QA testing of web applications — f...
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: CLI to manage emails via IMAP/SMTP. Use himalaya to list,...
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Set up a modded Minecraft server from a CurseForge/Modrin...
      - pokemon-player: Play Pokemon games autonomously via headless emulation. S...
    github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.
      - codebase-inspection: Inspect and analyze codebases using pygount for LOC count...
      - github-auth: Set up GitHub authentication for the agent using git (uni...
      - github-code-review: Review code changes by analyzing git diffs, leaving inlin...
      - github-issues: Create, manage, triage, and close GitHub issues. Search e...
      - github-pr-workflow: Full pull request lifecycle — create branches, commit cha...
      - github-repo-management: Clone, create, fork, configure, and manage GitHub reposit...
    leisure:
      - find-nearby: Find nearby places (restaurants, cafes, bars, pharmacies,...
    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.
      - mcporter: Use the mcporter CLI to list, configure, auth, and call M...
      - native-mcp: Built-in MCP (Model Context Protocol) client that connect...
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search and download GIFs from Tenor using curl. No depend...
      - heartmula: Set up and run HeartMuLa, the open-source music generatio...
      - songsee: Generate spectrograms and audio feature visualizations (m...
      - youtube-content: Fetch YouTube video transcripts and transform them into s...
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: Hugging Face Hub CLI (hf) — search, download, and upload ...
    mlops/cloud: GPU cloud providers and serverless compute platforms for ML workloads.
      - modal: Serverless GPU cloud platform for running ML workloads. U...
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - lm-evaluation-harness: Evaluates LLMs across 60+ academic benchmarks (MMLU, Huma...
      - weights-and-biases: Track ML experiments with automatic logging, visualize tr...
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - gguf: GGUF format and llama.cpp quantization for efficient CPU/...
      - guidance: Control LLM output with regex and grammars, guarantee val...
      - llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GP...
      - obliteratus: Remove refusal behaviors from open-weight LLMs using OBLI...
      - outlines: Guarantee valid JSON/XML/code structure during generation...
      - vllm: Serves LLMs with high throughput using vLLM's PagedAttent...
    mlops/models: Specific model architectures and tools — computer vision (CLIP, SAM, Stable Diffusion), speech (Whisper), audio generation (AudioCraft), and multimodal models (LLaVA).
      - audiocraft: PyTorch library for audio generation including text-to-mu...
      - clip: OpenAI's model connecting vision and language. Enables ze...
      - segment-anything: Foundation model for image segmentation with zero-shot tr...
      - stable-diffusion: State-of-the-art text-to-image generation with Stable Dif...
      - whisper: OpenAI's general-purpose speech recognition model. Suppor...
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: Build complex AI systems with declarative programming, op...
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Expert guidance for fine-tuning LLMs with Axolotl - YAML ...
      - grpo-rl-training: Expert guidance for GRPO/RL fine-tuning with TRL for reas...
      - peft: Parameter-efficient fine-tuning for LLMs using LoRA, QLoR...
      - pytorch-fsdp: Expert guidance for Fully Sharded Data Parallel training ...
      - trl-fine-tuning: Fine-tune LLMs using reinforcement learning with TRL - SF...
      - unsloth: Expert guidance for fast fine-tuning with Unsloth - 2-5x ...
    note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.
      - obsidian: Read, search, and create notes in the Obsidian vault.
    productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.
      - google-workspace: Gmail, Calendar, Drive, Contacts, Sheets, and Docs integr...
      - linear: Manage Linear issues, projects, and teams via the GraphQL...
      - nano-pdf: Edit PDFs with natural-language instructions using the na...
      - notion: Notion API for creating and managing pages, databases, an...
      - ocr-and-documents: Extract text from PDFs and scanned documents. Use web_ext...
      - powerpoint: Use this skill any time a .pptx file is involved in any w...
    red-teaming:
      - godmode: Jailbreak API-served LLMs using G0DM0D3 techniques — Pars...
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search and retrieve academic papers from arXiv using thei...
      - blogwatcher: Monitor blogs and RSS/Atom feeds for updates using the bl...
      - llm-wiki: Karpathy's LLM Wiki — build and maintain a persistent, in...
      - polymarket: Query Polymarket prediction market data — search markets,...
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, rooms, and scenes via the Ope...
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xitter: Interact with X/Twitter via the x-cli terminal client usi...
    software-development:
      - plan: Plan mode for Hermes — inspect context, write a markdown ...
      - requesting-code-review: Pre-commit verification pipeline — static security scan, ...
      - subagent-driven-development: Use when executing implementation plans with independent ...
      - systematic-debugging: Use when encountering any bug, test failure, or unexpecte...
      - test-driven-development: Use when implementing any feature or bugfix, before writi...
      - writing-plans: Use when you have a spec or requirements for a multi-step...
  </available_skills>

  Only proceed without loading a skill if genuinely none are relevant to the task.

  Conversation started: Tuesday, April 14, 2026 08:00 AM
  Model: MiniMax-M2.7-highspeed
  Provider: minimax-cn

  You are running as a scheduled cron job. There is no user present — you cannot ask questions, request clarification, or wait for follow-up. Execute the task fully and autonomously, making reasonable decisions where needed. Your final response is automatically delivered to the job's configured destination — put the primary content directly in your response.


# messages

session_id: 20260414_055053_c5714a
role: user
content:
  Hi~ I don't recognize you yet!
  Here's your pairing code: XP9BAX2H
  Ask the bot owner to run:
  hermes pairing approve feishu XP9BAX2H


session_id: 20260414_055053_c5714a
role: assistant
content: I'll help you approve the pairing! Let me run that command:
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_b1p3cen1p2ma_1", "call_id": "call_function_b1p3cen1p2ma_1", "response_item_id": "fc_function_b1p3cen1p2ma_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd ~ && hermes pairing approve feishu XP9BAX2H\"}"}}]

reasoning:
  The user seems to be showing me a pairing code and asking me to help them pair with something. Let me look at what's happening - this looks like a Feishu (Lark) integration message. The pairing code XP9BAX2H needs to be approved by running `hermes pairing approve feishu XP9BAX2H`.

  Let me check if there's a skill or context about this. I don't see a specific skill for this, but let me look at the gateway/platforms to understand the Feishu integration better.

  Actually, looking at the AGENTS.md, I can see there's a `gateway/platforms/` directory with various platform adapters. This seems to be a pairing flow for the Feishu (Lark) platform integration.


  I'll run that command to approve the pairing request.

reasoning_details:
  [{"signature": "04495260044da3b253fba812cc1c3d16d9b20abecb8518e0f93f18a9e7105c1e", "thinking": "The user seems to be showing me a pairing code and asking me to help them pair with something. Let me look at what's happening - this looks like a Feishu (Lark) integration message. The pairing code XP9BAX2H needs to be approved by running `hermes pairing approve feishu XP9BAX2H`.\n\nLet me check if there's a skill or context about this. I don't see a specific skill for this, but let me look at the gateway/platforms to understand the Feishu integration better.\n\nActually, looking at the AGENTS.md, I can see there's a `gateway/platforms/` directory with various platform adapters. This seems to be a pairing flow for the Feishu (Lark) platform integration.\n\n\nI'll run that command to approve the pairing request.", "type": "thinking"}]


session_id: 20260414_055053_c5714a
role: tool
tool_call_id: call_function_b1p3cen1p2ma_1
content:
  {"output": "Approved! User ou_4eeb65a9c45593bb1d111cd909423603 on feishu can now use the bot~\n  They'll be recognized automatically on their next message.", "exit_code": 0, "error": null}


session_id: 20260414_055053_c5714a
role: assistant
finish_reason: stop
reasoning: The pairing has been approved successfully. The user is now recognized and can use the bot.
content:
  Done! ✅ Your pairing code `XP9BAX2H` has been approved. You're all set — I'll recognize you automatically on your next message.

reasoning_details:
  [{"signature": "aeee921dfdf5150ce2d87b1ff880a407fc5e021a430f3b9f10692ef72db2201f", "thinking": "The pairing has been approved successfully. The user is now recognized and can use the bot.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
content:
  您好！很高兴认识您 😊

  我是您的 AI 助手，随时为您提供帮助。无论是查找信息、完成任务、编写代码、管理文件，还是回答问题，都可以交给我。

  请随时告诉我您的需求，我会尽力帮您搞定！

finish_reason: stop
reasoning:
  The user is speaking Chinese, saying "感谢，以后就拜托您了" which means "Thank you, I'll be counting on you from now on" - this seems like an initial greeting or introduction, perhaps after I've been set up as an agent.

  This is a friendly, introductory message. I should respond warmly in Chinese, acknowledging their message and expressing readiness to help. Since this appears to be a first interaction or new beginning, I should keep it friendly and professional.

  No tools are needed here - it's just a conversational greeting/acknowledgment.

reasoning_details:
  [{"signature": "91449185d7a15c3b30d276eb6b69f11e448ef8b9361ecb2b0ea7208814a8ea72", "thinking": "The user is speaking Chinese, saying \"\u611f\u8c22\uff0c\u4ee5\u540e\u5c31\u62dc\u6258\u60a8\u4e86\" which means \"Thank you, I'll be counting on you from now on\" - this seems like an initial greeting or introduction, perhaps after I've been set up as an agent.\n\nThis is a friendly, introductory message. I should respond warmly in Chinese, acknowledging their message and expressing readiness to help. Since this appears to be a first interaction or new beginning, I should keep it friendly and professional.\n\nNo tools are needed here - it's just a conversational greeting/acknowledgment.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_fnsvytg4lr0a_1", "call_id": "call_function_fnsvytg4lr0a_1", "response_item_id": "fc_function_fnsvytg4lr0a_1", "type": "function", "function": {"name": "search_files", "arguments": "{\"pattern\": \"*\", \"target\": \"files\"}"}}]

reasoning:
  The user is asking if I can see a folder they uploaded. Let me check what files are available in the current working directory. I should use search_files to look for files, or check the current directory structure.

reasoning_details:
  [{"signature": "c1279d6bab9ed65bbea794bec5c12294e36dbbabb130990c4253c37ca5e31976", "thinking": "The user is asking if I can see a folder they uploaded. Let me check what files are available in the current working directory. I should use search_files to look for files, or check the current directory structure.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_fnsvytg4lr0a_1
content:
  {"total_count": 50, "files": ["./gateway/__pycache__/stream_consumer.cpython-311.pyc", "./gateway/__pycache__/display_config.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/vector_stores/__pycache__/vector_stores.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/vector_stores/__pycache__/file_batches.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_batch_list_files_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/vector_store_file_deleted.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_batch_create_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/vector_store_file_batch.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_update_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_content_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_create_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/vector_store_file.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/file_list_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/vector_stores/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/vector_stores/__pycache__/files.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/vector_stores/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/__pycache__/moderations.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/__pycache__/fine_tuning.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_retrieve_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_retrieve_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_delete_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_create_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_list_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_list_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/permission_create_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/checkpoints/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/checkpoints/__pycache__/permissions.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/checkpoints/__pycache__/checkpoints.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/checkpoints/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/alpha/__pycache__/grader_validate_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/alpha/__pycache__/grader_validate_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/alpha/__pycache__/grader_run_response.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/alpha/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/alpha/__pycache__/grader_run_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/alpha/__pycache__/graders.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/alpha/__pycache__/alpha.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/resources/fine_tuning/alpha/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/fine_tuning_job_checkpoint.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/checkpoint_list_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/fine_tuning_job_integration.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/__init__.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/job_list_events_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/fine_tuning_job_event.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/reinforcement_hyperparameters_param.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/reinforcement_method_param.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/supervised_method_param.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/supervised_hyperparameters_param.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/job_create_params.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/dpo_method_param.cpython-311.pyc", "./venv/lib/python3.11/site-packages/openai/types/fine_tuning/__pycache__/dpo_hyperparameters_param.cpython-311.pyc"]}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_29nd3o9xu351_1", "call_id": "call_function_29nd3o9xu351_1", "response_item_id": "fc_function_29nd3o9xu351_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls -la ~ && echo \\\"---\\\" && pwd && echo \\\"---\\\" && ls -la .\"}"}}]

reasoning:
  The user is asking if I can see any folders they've uploaded. Let me look at the current working directory and see what's there. The search_files results show mostly Python cache files and venv directories - these don't look like user-uploaded files. Let me check the root directory structure to find any user files.

reasoning_details:
  [{"signature": "1bb9f30462fa8f7c0d56fb842ef77cce573eb5f51fa533418a50f1bdebb69e8d", "thinking": "The user is asking if I can see any folders they've uploaded. Let me look at the current working directory and see what's there. The search_files results show mostly Python cache files and venv directories - these don't look like user-uploaded files. Let me check the root directory structure to find any user files.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_29nd3o9xu351_1
content:
  {"output": "total 53924\ndr-xr-x---. 11 root root     4096 Apr 14 05:39 .\ndr-xr-xr-x. 19 root root      276 Mar 20 17:35 ..\ndrwxr-xr-x   4 root root      245 Mar 20 17:35 1panel-v2.1.5-linux-amd64\n-rw-r--r--   1 root root 55142310 Mar 20 17:33 1panel-v2.1.5-linux-amd64.tar.gz\n-rw-------   1 root root    10616 Mar 26 01:06 .bash_history\n-rw-r--r--.  1 root root       18 Aug 10  2021 .bash_logout\n-rw-r--r--.  1 root root      141 Aug 10  2021 .bash_profile\n-rw-r--r--.  1 root root      429 Aug 10  2021 .bashrc\ndrwxr-xr-x   5 root root       53 Apr 14 05:42 .cache\ndrwxr-xr-x   8 root root     4096 Mar 21 05:14 Clawith\ndrwx------   4 root root       30 Mar 21 06:27 .config\n-rw-r--r--.  1 root root      100 Aug 10  2021 .cshrc\ndrwx------   3 root root       63 Mar 21 05:12 .docker\ndrwx------  16 root root     4096 Apr 14 05:52 .hermes\n-rwxr-xr-x   1 root root    12696 Mar 25 19:59 install.sh\n-rw-------.  1 root root       20 Jul 29  2024 .lesshst\ndrwxr-xr-x   5 root root       43 Apr 14 05:50 .local\ndrwxr-xr-x   5 root root       90 Apr 14 05:40 .npm\ndrwx------   2 root root       83 Mar 20 17:35 .ssh\n-rw-r--r--.  1 root root      129 Aug 10  2021 .tcshrc\n-rw-------   1 root root     6064 Mar 21 05:10 .viminfo\n---\n/root/.hermes/hermes-agent\n---\ntotal 3080\ndrwxr-xr-x  33 root root    4096 Apr 14 05:45 .\ndrwx------  16 root root    4096 Apr 14 05:52 ..\ndrwxr-xr-x   2 root root     165 Apr 14 05:39 acp_adapter\ndrwxr-xr-x   2 root root      40 Apr 14 05:39 acp_registry\ndrwxr-xr-x   3 root root    4096 Apr 14 05:45 agent\n-rw-r--r--   1 root root   20851 Apr 14 05:39 AGENTS.md\ndrwxr-xr-x   2 root root      24 Apr 14 05:39 assets\n-rw-r--r--   1 root root   55322 Apr 14 05:39 batch_runner.py\n-rw-r--r--   1 root root   44875 Apr 14 05:39 cli-config.yaml.example\n-rw-r--r--   1 root root  447976 Apr 14 05:39 cli.py\n-rw-r--r--   1 root root     407 Apr 14 05:39 constraints-termux.txt\n-rw-r--r--   1 root root   26908 Apr 14 05:39 CONTRIBUTING.md\ndrwxr-xr-x   3 root root      79 Apr 14 05:50 cron\ndrwxr-xr-x   2 root root     129 Apr 14 05:39 datagen-config-examples\ndrwxr-xr-x   2 root root      42 Apr 14 05:39 docker\n-rw-r--r--   1 root root    1791 Apr 14 05:39 Dockerfile\n-rw-r--r--   1 root root     118 Apr 14 05:39 .dockerignore\ndrwxr-xr-x   6 root root     152 Apr 14 05:39 docs\n-rw-r--r--   1 root root   17577 Apr 14 05:39 .env.example\ndrwxr-xr-x   6 root root     273 Apr 14 05:39 environments\n-rw-r--r--   1 root root      10 Apr 14 05:39 .envrc\n-rw-r--r--   1 root root    4638 Apr 14 05:39 flake.lock\n-rw-r--r--   1 root root     924 Apr 14 05:39 flake.nix\ndrwxr-xr-x   5 root root    4096 Apr 14 05:48 gateway\ndrwxr-xr-x   7 root root     165 Apr 14 05:50 .git\n-rw-r--r--   1 root root     120 Apr 14 05:39 .gitattributes\ndrwxr-xr-x   4 root root      77 Apr 14 05:39 .github\n-rw-r--r--   1 root root    1033 Apr 14 05:39 .gitignore\n-rw-r--r--   1 root root     106 Apr 14 05:39 .gitmodules\n-rwxr-xr-x   1 root root     262 Apr 14 05:39 hermes\ndrwxr-xr-x   2 root root     134 Apr 14 05:39 hermes_agent.egg-info\ndrwxr-xr-x   3 root root    4096 Apr 14 05:45 hermes_cli\n-rw-r--r--   1 root root   10557 Apr 14 05:39 hermes_constants.py\n-rw-r--r--   1 root root   13806 Apr 14 05:39 hermes_logging.py\n-rw-r--r--   1 root root   49536 Apr 14 05:39 hermes_state.py\n-rw-r--r--   1 root root    3209 Apr 14 05:39 hermes_time.py\ndrwxr-xr-x   2 root root     247 Apr 14 05:39 landingpage\n-rw-r--r--   1 root root    1070 Apr 14 05:39 LICENSE\n-rw-r--r--   1 root root      87 Apr 14 05:39 MANIFEST.in\n-rw-r--r--   1 root root   30687 Apr 14 05:39 mcp_serve.py\n-rw-r--r--   1 root root   27406 Apr 14 05:39 mini_swe_runner.py\n-rw-r--r--   1 root root   22968 Apr 14 05:39 model_tools.py\ndrwxr-xr-x   2 root root     135 Apr 14 05:39 nix\ndrwxr-xr-x 362 root root   12288 Apr 14 05:42 node_modules\ndrwxr-xr-x  15 root root     245 Apr 14 05:39 optional-skills\n-rw-r--r--   1 root root     784 Apr 14 05:39 package.json\n-rw-r--r--   1 root root  216836 Apr 14 05:42 package-lock.json\ndrwxr-xr-x   3 root root      22 Apr 14 05:39 packaging\ndrwxr-xr-x   2 root root      62 Apr 14 05:39 .plans\ndrwxr-xr-x   2 root root      38 Apr 14 05:39 plans\ndrwxr-xr-x   5 root root      80 Apr 14 05:45 plugins\ndrwxr-xr-x   2 root root     311 Apr 14 05:50 __pycache__\n-rw-r--r--   1 root root    4727 Apr 14 05:39 pyproject.toml\n-rw-r--r--   1 root root   10975 Apr 14 05:39 README.md\n-rw-r--r--   1 root root   32385 Apr 14 05:39 RELEASE_v0.2.0.md\n-rw-r--r--   1 root root   35724 Apr 14 05:39 RELEASE_v0.3.0.md\n-rw-r--r--   1 root root   45448 Apr 14 05:39 RELEASE_v0.4.0.md\n-rw-r--r--   1 root root   32765 Apr 14 05:39 RELEASE_v0.5.0.md\n-rw-r--r--   1 root root   26137 Apr 14 05:39 RELEASE_v0.6.0.md\n-rw-r--r--   1 root root   33995 Apr 14 05:39 RELEASE_v0.7.0.md\n-rw-r--r--   1 root root   36083 Apr 14 05:39 RELEASE_v0.8.0.md\n-rw-r--r--   1 root root   32367 Apr 14 05:39 RELEASE_v0.9.0.md\n-rw-r--r--   1 root root     633 Apr 14 05:39 requirements.txt\n-rw-r--r--   1 root root   16286 Apr 14 05:39 rl_cli.py\n-rw-r--r--   1 root root  547866 Apr 14 05:39 run_agent.py\ndrwxr-xr-x   3 root root     236 Apr 14 05:39 scripts\n-rwxr-xr-x   1 root root   14753 Apr 14 05:39 setup-hermes.sh\ndrwxr-xr-x  28 root root    4096 Apr 14 05:39 skills\ndrwxr-xr-x  17 root root    4096 Apr 14 05:39 tests\ndrwxr-xr-x   2 root root       6 Apr 14 05:39 tinker-atropos\ndrwxr-xr-x   6 root root    4096 Apr 14 05:45 tools\n-rw-r--r--   1 root root   12332 Apr 14 05:39 toolset_distributions.py\n-rw-r--r--   1 root root   21263 Apr 14 05:39 toolsets.py\n-rw-r--r--   1 root root   63135 Apr 14 05:39 trajectory_compressor.py\n-rw-r--r--   1 root root    5199 Apr 14 05:39 utils.py\n-rw-r--r--   1 root root 1022115 Apr 14 05:39 uv.lock\ndrwxr-xr-x   5 root root     123 Apr 14 05:39 venv\ndrwxr-xr-x   4 root root     229 Apr 14 05:39 web\ndrwxr-xr-x   6 root root     206 Apr 14 05:39 website", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  我看了一下当前的工作目录（`/root/.hermes/hermes-agent`），这里主要是 Hermes Agent 项目的文件，没有看到您上传的文件夹。

  您上传的文件夹可能保存在其他位置，或者上传过程遇到了一些问题。请问：

  1. **您是通过什么方式上传的文件夹？**（例如：通过 Feishu 发送、通过网页界面等）
  2. **文件夹大概叫什么名字？**

  我可以帮您在整个系统中搜索一下，看看能否找到它。

reasoning:
  The user is asking if I can see an uploaded folder. Looking at the file listing, I can see the current working directory is `/root/.hermes/hermes-agent` which is the Hermes Agent project directory. I don't see any folder that looks like it was "uploaded" by the user. 

  The user might be referring to a folder they uploaded through the Feishu interface, but I'm not seeing any evidence of that in the filesystem. Let me check if there might be files in other locations or if perhaps the folder was uploaded somewhere else.

  I don't see any obvious "uploaded folder" in the current directory listing. The user might be expecting me to see something that isn't there, or perhaps the folder upload didn't work as expected.

  Let me tell the user what I can see and ask where the folder is located.

reasoning_details:
  [{"signature": "5957b550ef9bc0e3435e3d56d459cfd11aa7eff1a93d0865ab02e75e37d721b1", "thinking": "The user is asking if I can see an uploaded folder. Looking at the file listing, I can see the current working directory is `/root/.hermes/hermes-agent` which is the Hermes Agent project directory. I don't see any folder that looks like it was \"uploaded\" by the user. \n\nThe user might be referring to a folder they uploaded through the Feishu interface, but I'm not seeing any evidence of that in the filesystem. Let me check if there might be files in other locations or if perhaps the folder was uploaded somewhere else.\n\nI don't see any obvious \"uploaded folder\" in the current directory listing. The user might be expecting me to see something that isn't there, or perhaps the folder upload didn't work as expected.\n\nLet me tell the user what I can see and ask where the folder is located.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  是的，我需要跟您说明一下情况：

  **飞书消息接入 ✓** — 我可以接收和回复飞书消息
  **飞书文件访问 ✗** — 我目前无法直接访问您在飞书中上传的文件

  原因：虽然飞书聊天已连接，但文件（文档、图片、压缩包等）是存储在飞书的服务器上，我没有直接从飞书读取文件内容的接口。

  ---

  **您可以这样帮助我访问这些文件：**

  1. **分享下载链接** — 如果飞书支持分享文件下载链接给我
  2. **直接上传到这里** — 通过当前对话直接上传文件
  3. **告诉我文件内容** — 如果是代码或文本，您可以直接粘贴内容

  请问「rod-coinglass」这个文件夹里大概是什么内容？我可以先帮您分析一下这个项目需要做什么。

reasoning:
  The user is asking if I can access files uploaded via Feishu (飞书). Looking at my available platforms, it says "feishu: Connected ✓" which means Feishu is connected. However, I don't have a specific tool to directly access files uploaded via Feishu messages.

  Let me check if there's a way to access Feishu files. The user mentioned the folder is called "rod-coinglass". Let me see if I can find any Feishu-related tools or if there's a way to access uploaded files.

  Actually, I should be honest with the user - while Feishu is connected as a platform for messaging, I may not have direct access to files shared in Feishu messages unless there's a specific integration that allows that. Let me search to see if there's any Feishu file access capability.

reasoning_details:
  [{"signature": "7dde5d0c1464135f3efc3861ed8500ca92d72e11900e81518ce383be5f089979", "thinking": "The user is asking if I can access files uploaded via Feishu (\u98de\u4e66). Looking at my available platforms, it says \"feishu: Connected \u2713\" which means Feishu is connected. However, I don't have a specific tool to directly access files uploaded via Feishu messages.\n\nLet me check if there's a way to access Feishu files. The user mentioned the folder is called \"rod-coinglass\". Let me see if I can find any Feishu-related tools or if there's a way to access uploaded files.\n\nActually, I should be honest with the user - while Feishu is connected as a platform for messaging, I may not have direct access to files shared in Feishu messages unless there's a specific integration that allows that. Let me search to see if there's any Feishu file access capability.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_qhzwth88ozu2_1", "call_id": "call_function_qhzwth88ozu2_1", "response_item_id": "fc_function_qhzwth88ozu2_1", "type": "function", "function": {"name": "skills_list", "arguments": "{}"}}]

reasoning:
  The user is asking if I can integrate with Feishu's file storage services, and if not, whether there's an existing skill for this. Let me first check what skills are available related to Feishu or file integration.

  Let me search for available skills first.

reasoning_details:
  [{"signature": "f7f436f630cec5f7b39cc9258aa9fe7ba4a7104076e802c0c01cb803e9f1476d", "thinking": "The user is asking if I can integrate with Feishu's file storage services, and if not, whether there's an existing skill for this. Let me first check what skills are available related to Feishu or file integration.\n\nLet me search for available skills first.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_qhzwth88ozu2_1
content:
  {"success": true, "skills": [{"name": "dogfood", "description": "Systematic exploratory QA testing of web applications — find bugs, capture evidence, and generate structured reports", "category": null}, {"name": "claude-code", "description": "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.", "category": "autonomous-ai-agents"}, {"name": "codex", "description": "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.", "category": "autonomous-ai-agents"}, {"name": "hermes-agent", "description": "Complete guide to using and extending Hermes Agent — CLI usage, setup, configuration, spawning additional agents, gateway platforms, skills, voice, tools, profiles, and a concise contributor reference. Load this skill when helping users configure Hermes, troubleshoot issues, spawn agent instances, or make code contributions.", "category": "autonomous-ai-agents"}, {"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.", "category": "autonomous-ai-agents"}, {"name": "ascii-art", "description": "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.", "category": "creative"}, {"name": "ascii-video", "description": "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 video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.", "category": "creative"}, {"name": "excalidraw", "description": "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.", "category": "creative"}, {"name": "ideation", "description": "Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.", "category": "creative"}, {"name": "manim-video", "description": "Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.", "category": "creative"}, {"name": "p5js", "description": "Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems, flow fields, shaders (GLSL), pixel manipulation, kinetic typography, WebGL scenes, audio analysis, mouse/keyboard interaction, and headless high-res export. Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.", "category": "creative"}, {"name": "popular-web-designs", "description": "54 production-quality design systems extracted from real websites. Load a template to generate HTML/CSS that matches the visual identity of sites like Stripe, Linear, Vercel, Notion, Airbnb, and more. Each template includes colors, typography, components, layout rules, and ready-to-use CSS values.\n", "category": "creative"}, {"name": "songwriting-and-ai-music", "description": "Songwriting craft, AI music generation prompts (Suno focus), parody/adaptation techniques, phonetic tricks, and lessons learned. These are tools and ideas, not rules. Break any of them when the art calls for it.\n", "category": "creative"}, {"name": "jupyter-live-kernel", "description": "Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.\n", "category": "data-science"}, {"name": "webhook-subscriptions", "description": "Create and manage webhook subscriptions for event-driven agent activation. Use when the user wants external services to trigger agent runs automatically.", "category": "devops"}, {"name": "himalaya", "description": "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).", "category": "email"}, {"name": "minecraft-modpack-server", "description": "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.", "category": "gaming"}, {"name": "pokemon-player", "description": "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.", "category": "gaming"}, {"name": "codebase-inspection", "description": "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.", "category": "github"}, {"name": "github-auth", "description": "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.", "category": "github"}, {"name": "github-code-review", "description": "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.", "category": "github"}, {"name": "github-issues", "description": "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.", "category": "github"}, {"name": "github-pr-workflow", "description": "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.", "category": "github"}, {"name": "github-repo-management", "description": "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.", "category": "github"}, {"name": "find-nearby", "description": "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.", "category": "leisure"}, {"name": "mcporter", "description": "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.", "category": "mcp"}, {"name": "native-mcp", "description": "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.", "category": "mcp"}, {"name": "gif-search", "description": "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.", "category": "media"}, {"name": "heartmula", "description": "Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support.", "category": "media"}, {"name": "songsee", "description": "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.", "category": "media"}, {"name": "youtube-content", "description": "Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video.", "category": "media"}, {"name": "audiocraft-audio-generation", "description": "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.", "category": "mlops"}, {"name": "axolotl", "description": "Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support", "category": "mlops"}, {"name": "clip", "description": "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-purpose image understanding.", "category": "mlops"}, {"name": "dspy", "description": "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", "category": "mlops"}, {"name": "evaluating-llms-harness", "description": "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. Supports HuggingFace, vLLM, APIs.", "category": "mlops"}, {"name": "fine-tuning-with-trl", "description": "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 Transformers.", "category": "mlops"}, {"name": "gguf-quantization", "description": "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.", "category": "mlops"}, {"name": "grpo-rl-training", "description": "Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training", "category": "mlops"}, {"name": "guidance", "description": "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", "category": "mlops"}, {"name": "huggingface-hub", "description": "Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.", "category": "mlops"}, {"name": "llama-cpp", "description": "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.", "category": "mlops"}, {"name": "modal-serverless-gpu", "description": "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.", "category": "mlops"}, {"name": "obliteratus", "description": "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 across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.", "category": "mlops"}, {"name": "outlines", "description": "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", "category": "mlops"}, {"name": "peft-fine-tuning", "description": "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 <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.", "category": "mlops"}, {"name": "pytorch-fsdp", "description": "Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2", "category": "mlops"}, {"name": "segment-anything-model", "description": "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.", "category": "mlops"}, {"name": "serving-llms-vllm", "description": "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), and tensor parallelism.", "category": "mlops"}, {"name": "stable-diffusion-image-generation", "description": "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.", "category": "mlops"}, {"name": "unsloth", "description": "Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization", "category": "mlops"}, {"name": "weights-and-biases", "description": "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", "category": "mlops"}, {"name": "whisper", "description": "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 processing. Best for robust, multilingual ASR.", "category": "mlops"}, {"name": "obsidian", "description": "Read, search, and create notes in the Obsidian vault.", "category": "note-taking"}, {"name": "google-workspace", "description": "Gmail, Calendar, Drive, Contacts, Sheets, and Docs integration via gws CLI (googleworkspace/cli). Uses OAuth2 with automatic token refresh via bridge script. Requires gws binary.", "category": "productivity"}, {"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.", "category": "productivity"}, {"name": "nano-pdf", "description": "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.", "category": "productivity"}, {"name": "notion", "description": "Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.", "category": "productivity"}, {"name": "ocr-and-documents", "description": "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.", "category": "productivity"}, {"name": "powerpoint", "description": "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 an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.", "category": "productivity"}, {"name": "godmode", "description": "Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Hermes-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.", "category": "red-teaming"}, {"name": "arxiv", "description": "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.", "category": "research"}, {"name": "blogwatcher", "description": "Monitor blogs and RSS/Atom feeds for updates using the blogwatcher-cli tool. Add blogs, scan for new articles, track read status, and filter by category.", "category": "research"}, {"name": "llm-wiki", "description": "Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.", "category": "research"}, {"name": "polymarket", "description": "Query Polymarket prediction market data — search markets, get prices, orderbooks, and price history. Read-only via public REST APIs, no API key needed.", "category": "research"}, {"name": "research-paper-writing", "description": "End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.", "category": "research"}, {"name": "openhue", "description": "Control Philips Hue lights, rooms, and scenes via the OpenHue CLI. Turn lights on/off, adjust brightness, color, color temperature, and activate scenes.", "category": "smart-home"}, {"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.", "category": "social-media"}, {"name": "plan", "description": "Plan mode for Hermes — inspect context, write a markdown plan into the active workspace's `.hermes/plans/` directory, and do not execute the work.", "category": "software-development"}, {"name": "requesting-code-review", "description": "Pre-commit verification pipeline — static security scan, baseline-aware quality gates, independent reviewer subagent, and auto-fix loop. Use after code changes and before committing, pushing, or opening a PR.\n", "category": "software-development"}, {"name": "subagent-driven-development", "description": "Use when executing implementation plans with independent tasks. Dispatches fresh delegate_task per task with two-stage review (spec compliance then code quality).", "category": "software-development"}, {"name": "systematic-debugging", "description": "Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.", "category": "software-development"}, {"name": "test-driven-development", "description": "Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle with test-first approach.", "category": "software-development"}, {"name": "writing-plans", "description": "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.", "category": "software-development"}], "categories": ["autonomous-ai-agents", "creative", "data-science", "devops", "email", "gaming", "github", "leisure", "mcp", "media", "mlops", "note-taking", "productivity", "red-teaming", "research", "smart-home", "social-media", "software-development"], "count": 74, "hint": "Use skill_view(name) to see full content, tags, and linked files"}


session_id: 20260414_055208_a5751c1c
role: assistant
content:
  我查了一下，目前在现成的 skills 中没有找到飞书（Feishu/Lark）相关的集成方案。

  让我上网搜索一下有没有现成的飞书 MCP 服务或对接方案：

finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_g91obdgd4tvu_1", "call_id": "call_function_g91obdgd4tvu_1", "response_item_id": "fc_function_g91obdgd4tvu_1", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\": \"https://www.google.com/search?q=feishu+lark+MCP+server+Model+Context+Protocol+integration\"}"}}]

reasoning:
  The user is asking if I can integrate with Feishu (飞书) to access uploaded files. Let me check if there's any Feishu/Lark specific skill in the list.

  Looking through the skills list, I don't see any Feishu or Lark specific skill. The available skills don't include anything about Feishu integration.

  Let me search for any Feishu-related tools or MCP servers that might exist. I should also check if there's a way to use the MCP protocol to connect to Feishu's APIs or if there are any web search skills to find existing solutions.

  Let me search the web to see if there's a Feishu integration available or any existing skill for this.

reasoning_details:
  [{"signature": "1ef8d84286008a891de1d346b063176d2dad2058b913161879ee368eca9b695d", "thinking": "The user is asking if I can integrate with Feishu (\u98de\u4e66) to access uploaded files. Let me check if there's any Feishu/Lark specific skill in the list.\n\nLooking through the skills list, I don't see any Feishu or Lark specific skill. The available skills don't include anything about Feishu integration.\n\nLet me search for any Feishu-related tools or MCP servers that might exist. I should also check if there's a way to use the MCP protocol to connect to Feishu's APIs or if there are any web search skills to find existing solutions.\n\nLet me search the web to see if there's a Feishu integration available or any existing skill for this.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_g91obdgd4tvu_1
content:
  {"success": false, "error": "browserType.launch: Target page, context or browser has been closed\nBrowser logs:\n\n<launching> /root/.cache/ms-playwright/chromium_headless_shell-1217/chrome-headless-shell-linux64/chrome-headless-shell --disable-field-trial-config --disable-background-networking --disable-background-timer-throttling --disable-backgrounding-occluded-windows --disable-back-forward-cache --disable-breakpad --disable-client-side-phishing-detection --disable-component-extensions-with-background-pages --disable-component-update --no-default-browser-check --disable-default-apps --disable-dev-shm-usage --disable-extensions --disable-features=AvoidUnnecessaryBeforeUnloadCheckSync,BoundaryEventDispatchTracksNodeRemoval,DestroyProfileOnBrowserClose,DialMediaRouteProvider,GlobalMediaControls,HttpsUpgrades,LensOverlay,MediaRouter,PaintHolding,ThirdPartyStoragePartitioning,Translate,AutoDeElevate,RenderDocument,OptimizationHints --enable-features=CDPScreenshotNewSurface --allow-pre-commit-input --disable-hang-monitor --disable-ipc-flooding-protection --disable-popup-blocking --disable-prompt-on-repost --disable-renderer-backgrounding --force-color-profile=srgb --metrics-recording-only --no-first-run --password-store=basic --use-mock-keychain --no-service-autorun --export-tagged-pdf --disable-search-engine-choice-screen --unsafely-disable-devtools-self-xss-warnings --edge-skip-compat-layer-relaunch --enable-automation --disable-infobars --disable-search-engine-choice-screen --disable-sync --enable-unsafe-swiftshader --headless --hide-scrollbars --mute-audio --blink-settings=primaryHoverType=2,availableHoverTypes=2,primaryPointerType=4,availablePointerTypes=4 --no-sandbox --user-data-dir=/tmp/playwright_chromiumdev_profile-sMqVis --remote-debugging-pipe --no-startup-window\n<launched> pid=4053370\n[pid=4053370][err] /root/.cache/ms-playwright/chromium_headless_shell-1217/chrome-headless-shell-linux64/chrome-headless-shell: error while loading shared libraries: libnspr4.so: cannot open shared object file: No such file or directory\nCall log:\n  - <launching> /root/.cache/ms-playwright/chromium_headless_shell-1217/chrome-headless-shell-linux64/chrome-headless-shell --disable-field-trial-config --disable-background-networking --disable-background-timer-throttling --disable-backgrounding-occluded-windows --disable-back-forward-cache --disable-breakpad --disable-client-side-phishing-detection --disable-component-extensions-with-background-pages --disable-component-update --no-default-browser-check --disable-default-apps --disable-dev-shm-usage --disable-extensions --disable-features=AvoidUnnecessaryBeforeUnloadCheckSync,BoundaryEventDispatchTracksNodeRemoval,DestroyProfileOnBrowserClose,DialMediaRouteProvider,GlobalMediaControls,HttpsUpgrades,LensOverlay,MediaRouter,PaintHolding,ThirdPartyStoragePartitioning,Translate,AutoDeElevate,RenderDocument,OptimizationHints --enable-features=CDPScreenshotNewSurface --allow-pre-commit-input --disable-hang-monitor --disable-ipc-flooding-protection --disable-popup-blocking --disable-prompt-on-repost --disable-renderer-backgrounding --force-color-profile=srgb --metrics-recording-only --no-first-run --password-store=basic --use-mock-keychain --no-service-autorun --export-tagged-pdf --disable-search-engine-choice-screen --unsafely-disable-devtools-self-xss-warnings --edge-skip-compat-layer-relaunch --enable-automation --disable-infobars --disable-search-engine-choice-screen --disable-sync --enable-unsafe-swiftshader --headless --hide-scrollbars --mute-audio --blink-settings=primaryHoverType=2,availableHoverTypes=2,primaryPointerType=4,availablePointerTypes=4 --no-sandbox --user-data-dir=/tmp/playwright_chromiumdev_profile-sMqVis --remote-debugging-pipe --no-startup-window\n  - <launched> pid=4053370\n  - [pid=4053370][err] /root/.cache/ms-playwright/chromium_headless_shell-1217/chrome-headless-shell-linux64/chrome-headless-shell: error while loading shared libraries: libnspr4.so: cannot open shared object file: No such file or directory\n  - [pid=4053370] <gracefully close start>\n  - [pid=4053370] <kill>\n  - [pid=4053370] <will force kill>\n  - [pid=4053370] exception while trying to kill process: Error: kill ESRCH\n  - [pid=4053370] <process did exit: exitCode=127, signal=null>\n  - [pid=4053370] starting temporary directories cleanup\n  - [pid=4053370] finished temporary directories cleanup\n  - [pid=4053370] <gracefully close end>\n"}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
reasoning: Browser doesn't work. Let me try web search with terminal/curl instead.
tool_calls:
  [{"id": "call_function_mn57a7ae86ui_1", "call_id": "call_function_mn57a7ae86ui_1", "response_item_id": "fc_function_mn57a7ae86ui_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://www.google.com/search?q=feishu+lark+MCP+server+Model+Context+Protocol\\\" 2>/dev/null | head -50\"}"}}]

reasoning_details:
  [{"signature": "b417ea21378f02593fc2d2b80fe9f710802bd1fa0b02bd70252b7502da811975", "thinking": "Browser doesn't work. Let me try web search with terminal/curl instead.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_mn57a7ae86ui_1
content:
  {"output": "<!DOCTYPE html><html lang=\"my\"><head><title>Google Search</title><style>body{background-color:#fff}</style><script nonce=\"VcAz7Vp3szzKy7l4-mLhtQ\">window.google = window.google || {};window.google.c = window.google.c || {cap:0};</script></head><body><noscript><style>table,div,span,p{display:none}</style><meta content=\"0;url=/httpservice/retry/enablejs?sei=M1ndacu4EYXX1e8PpLDkgQg\" http-equiv=\"refresh\"><div style=\"display:block\">&#4112;&#4097;&#4156;&#4140;&#4152;&#4101;&#4140;&#4121;&#4155;&#4096;&#4154;&#4116;&#4158;&#4140;&#4126;&#4141;&#4143;&#4151; &#4126;&#4157;&#4140;&#4152;&#4124;&#4140;&#4097;&#4156;&#4100;&#4154;&#4152;&#4121;&#4123;&#4158;&#4141;&#4117;&#4139;&#4096; &#4132; <a href=\"/httpservice/retry/enablejs?sei=M1ndacu4EYXX1e8PpLDkgQg\"> &#4116;&#4145;&#4123;&#4140;&#4096;&#4141;&#4143; </a> &#4116;&#4158;&#4141;&#4117;&#4154;&#4173; &#4126;&#4157;&#4140;&#4152;&#4117;&#4139;&#4171;</div></noscript><script 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a={};content_bindings.forEach(function(b){a[b.key]=b.value});return a}function T(){var a=eid,b=new L;M(b,\"sg_ss\");N(b,\"sei\",a);return b.toString()}function U(a){var b=eid,c=new L;N(c,\"sg_ss\",encodeURIComponent(a));N(c,\"sei\",b);V(c.toString())}\nfunction da(a){if(window.prs){W(\"psrt\");sctm&&K();var b=T();window.prs(b,a).catch(function(){U(a)})}else U(a)}function V(a){W(\"psrt\");sctm&&K();window.prs?window.prs(a).catch(function(){X(a)}):X(a)}\nfunction X(a){if(window.pr)window.pr(a);else{a:{var b=b===void 0?S:b;if(a instanceof P)b=a;else{for(var c=0;c<b.length;++c){var f=b[c];if(f instanceof Q&&f.N(a)){b=new P(a);break a}}b=void 0}}a=location;if(b instanceof P)if(b instanceof P)b=b.i;else throw Error(\"e\");else b=aa.test(b)?b:void 0;b!==void 0&&a.replace(b)}}function Y(a){navigator.sendBeacon(\"/gen_204?cad=sg_b_e&e=\"+a,\"\")}\nfunction ea(){W(\"bsst\");ba(function(a){W(\"bset\");var b=challenge_version,c=cbs;a=b>0?\"B.\"+b+\".\"+c+\".\"+a:a;b=new 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eid='M1ndacu4EYXX1e8PpLDkgQg';(function(){setTimeout(function(){document.getElementById(cssId).setAttribute(\"style\",\"\");navigator.sendBeacon(\"/gen_204?cad=sg_trbl&ei=\"+eid,\"\")},2E3);}).call(this);})();</script><style>div{font-family:sans-serif;color:#545454;background-color:#fff}a{color:#1558d6;font-size:inherit;text-decoration:none}a:visited{color:#681da8}</style><div id=\"yvlrue\" style=\"display:none\">If you're having trouble accessing Google Search, please&nbsp;<a href=\"/search?q=feishu+lark+MCP+server+Model+Context+Protocol&amp;sca_esv=f0f46d557053f7c4&amp;ie=UTF-8&amp;emsg=SG_REL&amp;sei=M1ndacu4EYXX1e8PpLDkgQg\">click here</a>, or send&nbsp;<a href=\"https://support.google.com/websearch\">feedback</a>.</div></body></html>", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_b1prgecnwop5_1", "call_id": "call_function_b1prgecnwop5_1", "response_item_id": "fc_function_b1prgecnwop5_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://www.bing.com/search?q=feishu+lark+MCP+server+integration\\\" 2>/dev/null | grep -oP '(?<=<a href=\\\")[^\\\"]+' | head -20\"}"}}]

reasoning:
  The browser can't launch due to missing libraries. The curl attempt just returned the Google search page which is heavily obfuscated JavaScript. Let me try a different search approach using a simpler search engine or directly search for Feishu/Lark developer documentation.

reasoning_details:
  [{"signature": "f4ae744181697025cd4baf1b61e5bc7e4573f616d05f2608691ee00994240031", "thinking": "The browser can't launch due to missing libraries. The curl attempt just returned the Google search page which is heavily obfuscated JavaScript. Let me try a different search approach using a simpler search engine or directly search for Feishu/Lark developer documentation.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_50yb33bsl2xl_1", "call_id": "call_function_50yb33bsl2xl_1", "response_item_id": "fc_function_50yb33bsl2xl_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://search.yahoo.com/search?p=feishu+lark+MCP+server\\\" 2>/dev/null | grep -oP '(?<=href=\\\")[^\\\"]+' | head -20\"}"}}]

reasoning:
  Let me try a different approach and search for Feishu/Lark API integrations that might be available.

reasoning_details:
  [{"signature": "85a4cc2d560b4e4fdcf749a0499bb7cfcc08db2cde3993ccc6899cc3615c1462", "thinking": "Let me try a different approach and search for Feishu/Lark API integrations that might be available.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
reasoning: Let me try a different approach. Let me search GitHub directly for feishu MCP server.
tool_calls:
  [{"id": "call_function_ufzr1orwwjaz_1", "call_id": "call_function_ufzr1orwwjaz_1", "response_item_id": "fc_function_ufzr1orwwjaz_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://api.github.com/search/repositories?q=feishu+OR+lark+MCP\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); [print(f'{r[\\\\\\\"full_name\\\\\\\"]} - {r[\\\\\\\"description\\\\\\\"]}') for r in d.get('items',[])[:10]]\\\"\"}"}}]

reasoning_details:
  [{"signature": "72cd5bbc02069f0447dbd6f7ce2db5e73ad27045e65897f511f470dcb3201ee2", "thinking": "Let me try a different approach. Let me search GitHub directly for feishu MCP server.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_ufzr1orwwjaz_1
content:
  {"output": "cso1z/Feishu-MCP -  Feishu / Lark 飞书文档与任务管理工具，支持 MCP 服务器和 CLI + Skill 两种使用方式，可无缝集成 Cursor、Claude Code、Cline 等 AI 编码工具\nlarksuite/lark-openapi-mcp - 飞书/Lark官方 OpenAPI MCP\nztxtxwd/open-feishu-mcp-server - None\nawslabs/mcp - Official MCP Servers for AWS\njoewongjc/feishu-claude-code - Bridge Claude Code CLI with Feishu/Lark via WebSocket. 飞书 × Claude Code 实时对话。\nBrowserMCP/mcp - Browser MCP is a Model Context Provider (MCP) server that allows AI applications to control your browser\nmicrosoft/mcp - Catalog of official Microsoft MCP (Model Context Protocol) server implementations for AI-powered data access and tool integration\ngoogle/mcp - Google 💚 MCP\npunkpeye/awesome-mcp-servers - A collection of MCP servers.\nmicrosoft/playwright-mcp - Playwright MCP server", "exit_code": 0, "error": null, "approval": "Command required approval (Security scan — [HIGH] Pipe to interpreter: curl | python3: Command pipes output from 'curl' directly to interpreter 'python3'. Downloaded content will be executed without inspection.\n  Safer: tirith run https://api.github.com/search/repositories?q=feishu+OR+lark+MCP  — or: vet https://api.github.com/search/repositories?q=feishu+OR+lark+MCP  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user."}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
content:
  找到了！GitHub 上有几个飞书 MCP 服务可用：

  1. **`cso1z/Feishu-MCP`** - 支持 MCP 服务器 + CLI/Skill 两种模式，可集成 Cursor、Claude Code 等
  2. **`larksuite/lark-openapi-mcp`** - 飞书/Lark 官方 OpenAPI MCP
  3. **`ztxtxwd/open-feishu-mcp-server`** - 另一个飞书 MCP 服务器

  让我获取更多详细信息：

tool_calls:
  [{"id": "call_function_k26qebu0p1vk_1", "call_id": "call_function_k26qebu0p1vk_1", "response_item_id": "fc_function_k26qebu0p1vk_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://api.github.com/repos/cso1z/Feishu-MCP\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print(f'Name: {d[\\\\\\\"full_name\\\\\\\"]}\\\\nStars: {d[\\\\\\\"stargazers_count\\\\\\\"]}\\\\nDescription: {d[\\\\\\\"description\\\\\\\"]}\\\\nReadme: {d.get(\\\\\\\"default_branch\\\\\\\")}')\\\"\"}"}}]

reasoning:
  I found some relevant results. There are Feishu MCP servers available. Let me look at these:

  1. `cso1z/Feishu-MCP` - Feishu/Lark 飞书文档与任务管理工具，支持 MCP 服务器和 CLI + Skill 两种使用方式
  2. `larksuite/lark-openapi-mcp` - 飞书/Lark官方 OpenAPI MCP
  3. `ztxtxwd/open-feishu-mcp-server` - Another Feishu MCP server

  Let me get more details about these, particularly the cso1z one which seems to support both MCP server and Skill modes.

reasoning_details:
  [{"signature": "cffca424edecf1182e45d23461fbb7b3a3f13135586f8f931de115b31f56a716", "thinking": "I found some relevant results. There are Feishu MCP servers available. Let me look at these:\n\n1. `cso1z/Feishu-MCP` - Feishu/Lark \u98de\u4e66\u6587\u6863\u4e0e\u4efb\u52a1\u7ba1\u7406\u5de5\u5177\uff0c\u652f\u6301 MCP \u670d\u52a1\u5668\u548c CLI + Skill \u4e24\u79cd\u4f7f\u7528\u65b9\u5f0f\n2. `larksuite/lark-openapi-mcp` - \u98de\u4e66/Lark\u5b98\u65b9 OpenAPI MCP\n3. `ztxtxwd/open-feishu-mcp-server` - Another Feishu MCP server\n\nLet me get more details about these, particularly the cso1z one which seems to support both MCP server and Skill modes.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_k26qebu0p1vk_1
content:
  {"output": "Name: cso1z/Feishu-MCP\nStars: 597\nDescription:  Feishu / Lark 飞书文档与任务管理工具，支持 MCP 服务器和 CLI + Skill 两种使用方式，可无缝集成 Cursor、Claude Code、Cline 等 AI 编码工具\nReadme: main", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
content:
  星标数最多的 `cso1z/Feishu-MCP` 有 **597 stars**，看起来很成熟！

  我已经内置了 `native-mcp` 和 `mcporter` 技能来连接 MCP 服务器。让我来帮您配置飞书 MCP：

tool_calls:
  [{"id": "call_function_4ts0hhhqkbb7_1", "call_id": "call_function_4ts0hhhqkbb7_1", "response_item_id": "fc_function_4ts0hhhqkbb7_1", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\": \"native-mcp\"}"}}]

reasoning:
  Great find! Let me also check the official larksuite one and get more details about how to set them up. But since the user is using Hermes Agent, I should also check if Hermes has a native MCP client that can connect to these servers. I have the native-mcp and mcporter skills available.

  Let me check the mcporter skill to see if I can use it to set up a Feishu MCP connection.

reasoning_details:
  [{"signature": "de4887213c83103a844c4e2aa1793a32020700db423284b40754cb90f22ae7de", "thinking": "Great find! Let me also check the official larksuite one and get more details about how to set them up. But since the user is using Hermes Agent, I should also check if Hermes has a native MCP client that can connect to these servers. I have the native-mcp and mcporter skills available.\n\nLet me check the mcporter skill to see if I can use it to set up a Feishu MCP connection.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_4ts0hhhqkbb7_1
content:
  {"success": true, "name": "native-mcp", "description": "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.", "tags": ["MCP", "Tools", "Integrations"], "related_skills": ["mcporter"], "content": "---\nname: native-mcp\ndescription: 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.\nversion: 1.0.0\nauthor: Hermes Agent\nlicense: MIT\nmetadata:\n  hermes:\n    tags: [MCP, Tools, Integrations]\n    related_skills: [mcporter]\n---\n\n# Native MCP Client\n\nHermes Agent has a built-in MCP client that connects to MCP servers at startup, discovers their tools, and makes them available as first-class tools the agent can call directly. No bridge CLI needed -- tools from MCP servers appear alongside built-in tools like `terminal`, `read_file`, etc.\n\n## When to Use\n\nUse this whenever you want to:\n- Connect to MCP servers and use their tools from within Hermes Agent\n- Add external capabilities (filesystem access, GitHub, databases, APIs) via MCP\n- Run local stdio-based MCP servers (npx, uvx, or any command)\n- Connect to remote HTTP/StreamableHTTP MCP servers\n- Have MCP tools auto-discovered and available in every conversation\n\nFor ad-hoc, one-off MCP tool calls from the terminal without configuring anything, see the `mcporter` skill instead.\n\n## Prerequisites\n\n- **mcp Python package** -- optional dependency; install with `pip install mcp`. If not installed, MCP support is silently disabled.\n- **Node.js** -- required for `npx`-based MCP servers (most community servers)\n- **uv** -- required for `uvx`-based MCP servers (Python-based servers)\n\nInstall the MCP SDK:\n\n```bash\npip install mcp\n# or, if using uv:\nuv pip install mcp\n```\n\n## Quick Start\n\nAdd MCP servers to `~/.hermes/config.yaml` under the `mcp_servers` key:\n\n```yaml\nmcp_servers:\n  time:\n    command: \"uvx\"\n    args: [\"mcp-server-time\"]\n```\n\nRestart Hermes Agent. On startup it will:\n1. Connect to the server\n2. Discover available tools\n3. Register them with the prefix `mcp_time_*`\n4. Inject them into all platform toolsets\n\nYou can then use the tools naturally -- just ask the agent to get the current time.\n\n## Configuration Reference\n\nEach entry under `mcp_servers` is a server name mapped to its config. There are two transport types: **stdio** (command-based) and **HTTP** (url-based).\n\n### Stdio Transport (command + args)\n\n```yaml\nmcp_servers:\n  server_name:\n    command: \"npx\"             # (required) executable to run\n    args: [\"-y\", \"pkg-name\"]   # (optional) command arguments, default: []\n    env:                       # (optional) environment variables for the subprocess\n      SOME_API_KEY: \"value\"\n    timeout: 120               # (optional) per-tool-call timeout in seconds, default: 120\n    connect_timeout: 60        # (optional) initial connection timeout in seconds, default: 60\n```\n\n### HTTP Transport (url)\n\n```yaml\nmcp_servers:\n  server_name:\n    url: \"https://my-server.example.com/mcp\"   # (required) server URL\n    headers:                                     # (optional) HTTP headers\n      Authorization: \"Bearer sk-...\"\n    timeout: 180               # (optional) per-tool-call timeout in seconds, default: 120\n    connect_timeout: 60        # (optional) initial connection timeout in seconds, default: 60\n```\n\n### All Config Options\n\n| Option            | Type   | Default | Description                                       |\n|-------------------|--------|---------|---------------------------------------------------|\n| `command`         | string | --      | Executable to run (stdio transport, required)     |\n| `args`            | list   | `[]`    | Arguments passed to the command                   |\n| `env`             | dict   | `{}`    | Extra environment variables for the subprocess    |\n| `url`             | string | --      | Server URL (HTTP transport, required)             |\n| `headers`         | dict   | `{}`    | HTTP headers sent with every request              |\n| `timeout`         | int    | `120`   | Per-tool-call timeout in seconds                  |\n| `connect_timeout` | int    | `60`    | Timeout for initial connection and discovery      |\n\nNote: A server config must have either `command` (stdio) or `url` (HTTP), not both.\n\n## How It Works\n\n### Startup Discovery\n\nWhen Hermes Agent starts, `discover_mcp_tools()` is called during tool initialization:\n\n1. Reads `mcp_servers` from `~/.hermes/config.yaml`\n2. For each server, spawns a connection in a dedicated background event loop\n3. Initializes the MCP session and calls `list_tools()` to discover available tools\n4. Registers each tool in the Hermes tool registry\n\n### Tool Naming Convention\n\nMCP tools are registered with the naming pattern:\n\n```\nmcp_{server_name}_{tool_name}\n```\n\nHyphens and dots in names are replaced with underscores for LLM API compatibility.\n\nExamples:\n- Server `filesystem`, tool `read_file` → `mcp_filesystem_read_file`\n- Server `github`, tool `list-issues` → `mcp_github_list_issues`\n- Server `my-api`, tool `fetch.data` → `mcp_my_api_fetch_data`\n\n### Auto-Injection\n\nAfter discovery, MCP tools are automatically injected into all `hermes-*` platform toolsets (CLI, Discord, Telegram, etc.). This means MCP tools are available in every conversation without any additional configuration.\n\n### Connection Lifecycle\n\n- Each server runs as a long-lived asyncio Task in a background daemon thread\n- Connections persist for the lifetime of the agent process\n- If a connection drops, automatic reconnection with exponential backoff kicks in (up to 5 retries, max 60s backoff)\n- On agent shutdown, all connections are gracefully closed\n\n### Idempotency\n\n`discover_mcp_tools()` is idempotent -- calling it multiple times only connects to servers that aren't already connected. Failed servers are retried on subsequent calls.\n\n## Transport Types\n\n### Stdio Transport\n\nThe most common transport. Hermes launches the MCP server as a subprocess and communicates over stdin/stdout.\n\n```yaml\nmcp_servers:\n  filesystem:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/home/user/projects\"]\n```\n\nThe subprocess inherits a **filtered** environment (see Security section below) plus any variables you specify in `env`.\n\n### HTTP / StreamableHTTP Transport\n\nFor remote or shared MCP servers. Requires the `mcp` package to include HTTP client support (`mcp.client.streamable_http`).\n\n```yaml\nmcp_servers:\n  remote_api:\n    url: \"https://mcp.example.com/mcp\"\n    headers:\n      Authorization: \"Bearer sk-...\"\n```\n\nIf HTTP support is not available in your installed `mcp` version, the server will fail with an ImportError and other servers will continue normally.\n\n## Security\n\n### Environment Variable Filtering\n\nFor stdio servers, Hermes does NOT pass your full shell environment to MCP subprocesses. Only safe baseline variables are inherited:\n\n- `PATH`, `HOME`, `USER`, `LANG`, `LC_ALL`, `TERM`, `SHELL`, `TMPDIR`\n- Any `XDG_*` variables\n\nAll other environment variables (API keys, tokens, secrets) are excluded unless you explicitly add them via the `env` config key. This prevents accidental credential leakage to untrusted MCP servers.\n\n```yaml\nmcp_servers:\n  github:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-github\"]\n    env:\n      # Only this token is passed to the subprocess\n      GITHUB_PERSONAL_ACCESS_TOKEN: \"ghp_...\"\n```\n\n### Credential Stripping in Error Messages\n\nIf an MCP tool call fails, any credential-like patterns in the error message are automatically redacted before being shown to the LLM. This covers:\n\n- GitHub PATs (`ghp_...`)\n- OpenAI-style keys (`sk-...`)\n- Bearer tokens\n- Generic `token=`, `key=`, `API_KEY=`, `password=`, `secret=` patterns\n\n## Troubleshooting\n\n### \"MCP SDK not available -- skipping MCP tool discovery\"\n\nThe `mcp` Python package is not installed. Install it:\n\n```bash\npip install mcp\n```\n\n### \"No MCP servers configured\"\n\nNo `mcp_servers` key in `~/.hermes/config.yaml`, or it's empty. Add at least one server.\n\n### \"Failed to connect to MCP server 'X'\"\n\nCommon causes:\n- **Command not found**: The `command` binary isn't on PATH. Ensure `npx`, `uvx`, or the relevant command is installed.\n- **Package not found**: For npx servers, the npm package may not exist or may need `-y` in args to auto-install.\n- **Timeout**: The server took too long to start. Increase `connect_timeout`.\n- **Port conflict**: For HTTP servers, the URL may be unreachable.\n\n### \"MCP server 'X' requires HTTP transport but mcp.client.streamable_http is not available\"\n\nYour `mcp` package version doesn't include HTTP client support. Upgrade:\n\n```bash\npip install --upgrade mcp\n```\n\n### Tools not appearing\n\n- Check that the server is listed under `mcp_servers` (not `mcp` or `servers`)\n- Ensure the YAML indentation is correct\n- Look at Hermes Agent startup logs for connection messages\n- Tool names are prefixed with `mcp_{server}_{tool}` -- look for that pattern\n\n### Connection keeps dropping\n\nThe client retries up to 5 times with exponential backoff (1s, 2s, 4s, 8s, 16s, capped at 60s). If the server is fundamentally unreachable, it gives up after 5 attempts. Check the server process and network connectivity.\n\n## Examples\n\n### Time Server (uvx)\n\n```yaml\nmcp_servers:\n  time:\n    command: \"uvx\"\n    args: [\"mcp-server-time\"]\n```\n\nRegisters tools like `mcp_time_get_current_time`.\n\n### Filesystem Server (npx)\n\n```yaml\nmcp_servers:\n  filesystem:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/home/user/documents\"]\n    timeout: 30\n```\n\nRegisters tools like `mcp_filesystem_read_file`, `mcp_filesystem_write_file`, `mcp_filesystem_list_directory`.\n\n### GitHub Server with Authentication\n\n```yaml\nmcp_servers:\n  github:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-github\"]\n    env:\n      GITHUB_PERSONAL_ACCESS_TOKEN: \"ghp_xxxxxxxxxxxxxxxxxxxx\"\n    timeout: 60\n```\n\nRegisters tools like `mcp_github_list_issues`, `mcp_github_create_pull_request`, etc.\n\n### Remote HTTP Server\n\n```yaml\nmcp_servers:\n  company_api:\n    url: \"https://mcp.mycompany.com/v1/mcp\"\n    headers:\n      Authorization: \"Bearer sk-xxxxxxxxxxxxxxxxxxxx\"\n      X-Team-Id: \"engineering\"\n    timeout: 180\n    connect_timeout: 30\n```\n\n### Multiple Servers\n\n```yaml\nmcp_servers:\n  time:\n    command: \"uvx\"\n    args: [\"mcp-server-time\"]\n\n  filesystem:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-filesystem\", \"/tmp\"]\n\n  github:\n    command: \"npx\"\n    args: [\"-y\", \"@modelcontextprotocol/server-github\"]\n    env:\n      GITHUB_PERSONAL_ACCESS_TOKEN: \"ghp_xxxxxxxxxxxxxxxxxxxx\"\n\n  company_api:\n    url: \"https://mcp.internal.company.com/mcp\"\n    headers:\n      Authorization: \"Bearer sk-xxxxxxxxxxxxxxxxxxxx\"\n    timeout: 300\n```\n\nAll tools from all servers are registered and available simultaneously. Each server's tools are prefixed with its name to avoid collisions.\n\n## Sampling (Server-Initiated LLM Requests)\n\nHermes supports MCP's `sampling/createMessage` capability — MCP servers can request LLM completions through the agent during tool execution. This enables agent-in-the-loop workflows (data analysis, content generation, decision-making).\n\nSampling is **enabled by default**. Configure per server:\n\n```yaml\nmcp_servers:\n  my_server:\n    command: \"npx\"\n    args: [\"-y\", \"my-mcp-server\"]\n    sampling:\n      enabled: true           # default: true\n      model: \"gemini-3-flash\" # model override (optional)\n      max_tokens_cap: 4096    # max tokens per request\n      timeout: 30             # LLM call timeout (seconds)\n      max_rpm: 10             # max requests per minute\n      allowed_models: []      # model whitelist (empty = all)\n      max_tool_rounds: 5      # tool loop limit (0 = disable)\n      log_level: \"info\"       # audit verbosity\n```\n\nServers can also include `tools` in sampling requests for multi-turn tool-augmented workflows. The `max_tool_rounds` config prevents infinite tool loops. Per-server audit metrics (requests, errors, tokens, tool use count) are tracked via `get_mcp_status()`.\n\nDisable sampling for untrusted servers with `sampling: { enabled: false }`.\n\n## Notes\n\n- MCP tools are called synchronously from the agent's perspective but run asynchronously on a dedicated background event loop\n- Tool results are returned as JSON with either `{\"result\": \"...\"}` or `{\"error\": \"...\"}`\n- The native MCP client is independent of `mcporter` -- you can use both simultaneously\n- Server connections are persistent and shared across all conversations in the same agent process\n- Adding or removing servers requires restarting the agent (no hot-reload currently)\n", "path": "mcp/native-mcp/SKILL.md", "linked_files": null, "usage_hint": null, "required_environment_variables": [], "required_commands": [], "missing_required_environment_variables": [], "missing_credential_files": [], "missing_required_commands": [], "setup_needed": false, "setup_skipped": false, "readiness_status": "available", "metadata": {"hermes": {"tags": ["MCP", "Tools", "Integrations"], "related_skills": ["mcporter"]}}}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 让我获取更详细的设置说明：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_0b0vx0imdey3_1", "call_id": "call_function_0b0vx0imdey3_1", "response_item_id": "fc_function_0b0vx0imdey3_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://raw.githubusercontent.com/cso1z/Feishu-MCP/main/README.md\\\" 2>/dev/null | head -200\"}"}}]

reasoning:
  Now I have a clear picture. I can help the user set up the Feishu MCP server using Hermes's native MCP client. Let me check what Feishu MCP server they want to use and how to configure it. Let me get more details about cso1z/Feishu-MCP to understand how to set it up.

reasoning_details:
  [{"signature": "24e59ca7121af606719c2833539aac93fea9ceba20636c49b764b101e4813a84", "thinking": "Now I have a clear picture. I can help the user set up the Feishu MCP server using Hermes's native MCP client. Let me check what Feishu MCP server they want to use and how to configure it. Let me get more details about cso1z/Feishu-MCP to understand how to set it up.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_0b0vx0imdey3_1
content:
  {"output": "# 飞书 MCP 服务器\n\n\n[![npm version](https://img.shields.io/npm/v/feishu-mcp?color=blue&label=npm)](https://www.npmjs.com/package/feishu-mcp)\n[![MIT License](https://img.shields.io/badge/license-MIT-green)](./LICENSE)\n\n为 [Cursor](https://cursor.sh/)、[Windsurf](https://codeium.com/windsurf)、[Cline](https://cline.bot/) 和其他 AI 驱动的编码工具提供访问、编辑和结构化处理飞书文档的能力，**并支持飞书任务管理和用户信息查询**，基于 [Model Context Protocol](https://modelcontextprotocol.io/introduction) 服务器实现。\n\n**现已支持 `feishu-tool` 独立 CLI 工具**，可在终端或脚本中直接调用所有飞书工具，无需启动 MCP 服务器。配合 [Feishu-Skill](https://github.com/cso1z/Feishu-Skill) 可让 Claude Code 等 AI Agent 自动选择最合适的方式操作飞书。\n\n本项目让 AI 编码工具能够：\n- **文档处理**：直接获取、理解、创建和编辑飞书文档，显著提升文档处理的智能化和效率\n- **任务管理**：列取、创建、更新、删除飞书任务，支持子任务和成员管理（需 user 认证）\n- **用户信息**：按名称搜索或按 ID 批量获取飞书用户，便于任务分配和文档协作（需 user 认证）\n\n**完整覆盖飞书文档的真实使用流程，助你高效利用文档资源：**\n1. **文件夹目录获取**：快速获取和浏览飞书文档文件夹下的所有文档，便于整体管理和查找。\n2. **内容获取与理解**：支持结构化、分块、富文本等多维度内容读取，AI 能精准理解文档上下文。\n3. **智能创建与编辑**：可自动创建新文档、批量生成和编辑内容，满足多样化写作需求。\n4. **高效检索与搜索**：内置关键字搜索，帮助你在大量文档中迅速找到目标信息。\n5. **任务管理与用户查询**：支持飞书任务 CRUD 及用户信息搜索，便于在文档中关联任务和人员。\n\n本项目让你在飞书文档的日常使用流程中实现智能获取、编辑和搜索，并扩展任务与用户管理能力，提升内容处理效率和体验。\n\n\n### 💡项目推荐：\n使用 Claude Code 推荐配合 [claude-ip-guard](https://github.com/cso1z/claude-ip-guard) —— 自动检测 IP 地理位置并拦截受限地区访问，防止因网络切换导致 Claude 封号。\n\n### 🎬 使用演示视频\n\n你可以通过以下视频了解 MCP 的实际使用效果和操作流程：\n\n<a href=\"https://www.bilibili.com/video/BV1z7MdzoEfu/?vd_source=94c14da5a71aeb01f665f159dd3d89c8\">\n  <img src=\"image/demo.png\" alt=\"飞书 MCP 使用演示\" width=\"300\"/>\n</a>\n\n<a href=\"https://www.bilibili.com/video/BV18z3gzdE1w/?vd_source=94c14da5a71aeb01f665f159dd3d89c8\">\n  <img src=\"image/demo_1.png\" alt=\"飞书 MCP 使用演示\" width=\"300\"/>\n</a>\n\n> ⭐ **Star 本项目，第一时间获取最新功能和重要更新！** 关注项目可以让你不错过任何新特性、修复和优化，助你持续高效使用。你的支持也将帮助我们更好地完善和发展项目。⭐\n\n\n---\n\n## 🛠️ 工具功能详情\n\n| 功能类别 | 工具名称                                 | 描述          | 使用场景                     | 状态    |\n|---------|--------------------------------------|-------------|--------------------------|-------|\n| **文档管理** | `create_feishu_document`             | 创建新的飞书文档    | 从零开始创建文档                 | ✅ 已完成 |\n| | `get_feishu_document_info`           | 获取文档基本信息    | 验证文档存在性和权限               | ✅ 已完成 |\n| | `get_feishu_document_blocks`         | 获取文档块结构     | 了解文档层级结构                 | ✅ 已完成 |\n| **内容编辑** | `batch_create_feishu_blocks`         | 批量创建多个块     | 高效创建连续内容                 | ✅ 已完成 |\n| | `update_feishu_block_text`           | 更新块文本内容     | 修改现有内容                   | ✅ 已完成 |\n| | `delete_feishu_document_blocks`      | 删除文档块       | 清理和重构文档内容                | ✅ 已完成 |\n| **文件夹管理** | `get_feishu_folder_files`            | 获取文件夹文件列表   | 浏览文件夹内容                  | ✅ 已完成 |\n| | `create_feishu_folder`               | 创建新文件夹      | 组织文档结构                   | ✅ 已完成 |\n| **搜索功能** | `search_feishu_documents`            | 搜索文档        | 查找特定内容                   | ✅ 已完成 |\n| **工具功能** | `get_feishu_document_info` | 获取wiki文档信息  | 将Wiki链接转为文档ID、创建wiki子节点  | ✅ 已完成 |\n| | `get_feishu_image_resource`          | 获取图片资源      | 下载文档中的图片                 | ✅ 已完成 |\n| | `get_feishu_whiteboard_content`      | 获取画板内容      | 获取画板中的图形元素和结构(流程图、思维导图等) | ✅ 已完成 |\n| **高级功能** | `create_feishu_table`                | 创建和编辑表格     | 结构化数据展示                  | ✅ 已完成 |\n| | 流程图插入                                | 支持流程图和思维导图  | 流程梳理和可视化                 | ✅ 已完成 |\n| | 流程图插入(画板形式)                          | 支持流程图和思维导图  | 流程梳理和可视化                 | ✅ 已完成   |\n| 图片插入  | `upload_and_bind_image_to_block`     | 支持插入本地和远程图片 | 修改文档内容                   | ✅ 已完成 |\n| | 公式支持                                 | 支持数学公式      | 学术和技术文档                  | ✅ 已完成 |\n| | | | | |\n| **任务管理** | `list_feishu_tasks`                  | 列取我负责的任务     | 查看待办/已完成任务               | ✅ 已完成 |\n| | `create_feishu_task`                  | 批量创建任务（含子任务） | 新建任务、拆分子任务              | ✅ 已完成 |\n| | `update_feishu_task`                  | 更新任务         | 修改内容、成员、提醒               | ✅ 已完成 |\n| | `delete_feishu_task`                  | 批量删除任务       | 清理任务                       | ✅ 已完成 |\n| **用户信息** | `get_feishu_users`                   | 按名称搜索或按 ID 批量获取用户 | 查找成员、任务分配               | ✅ 已完成 |\n\n### 🎨 支持的样式功能（基本支持md所有格式）\n\n- **文本样式**：粗体、斜体、下划线、删除线、行内代码\n- **文本颜色**：灰色、棕色、橙色、黄色、绿色、蓝色、紫色\n- **对齐方式**：左对齐、居中、右对齐\n- **标题级别**：支持1-9级标题\n- **代码块**：支持多种编程语言语法高亮\n- **列表**：有序列表（编号）、无序列表（项目符号）\n- **图片**：支持本地图片和网络图片\n- **公式**：在文本块中插入数学公式，支持LaTeX语法\n- **mermaid图表**：支持流程图、时序图、思维导图、类图、饼图等等\n- **表格**：支持创建多行列表格，单元格可包含文本、标题、列表、代码块等多种内容类型\n- **飞书文档画板**：支持飞书文档的画板创建，提供更加更为丰富和多样化的内容。\n\n---\n\n\n## 📈 一周计划：提升工具效率\n\n- ~~**精简工具集**：21个工具 → 13个工具，移除冗余，聚焦核心功能~~ 0.0.15 ✅\n- ~~**优化描述**：7000+ tokens → 3000+ tokens，简化提示，节省请求token~~ 0.0.15 ✅\n- ~~**批量增强**：新增批量更新、批量图片上传，单次操作效率提升50%~~ 0.0.15 ✅\n- **流程优化**：减少多步调用，实现一键完成复杂任务\n- ~~**支持多种凭证类型**：包括 tenant_access_token和 user_access_token，满足不同场景下的认证需求~~  (飞书应用配置发生变更) 0.0.16 ✅。\n- ~~**支持cursor用户登录**：方便在cursor平台用户认证   不做了,没必要 ❌~~\n- ~~**支持mermaid图表**：流程图、时序图等等，丰富文档内容~~ 0.1.11 ✅\n- ~~**支持表格创建**：创建包含各种块类型的复杂表格，支持样式控制~~ 0.1.2 ✅\n- ~~**支持飞书多用户user认证**：一人部署，可以多人使用~~ 0.1.3 ✅\n- ~~**支持user_access_token自动刷新**：无需频繁授权，提高使用体验~~ 0.1.6 ✅\n- ~~**支持授权范围校验**：对应用授权进行验证，以确保其符合当前工具的要求。如未满足条件，将提供友好的指引，以便用户更顺畅地使用~~ 0.1.7 ✅\n- ~~**支持创建画板内容**：与Mermaid图表相比，画板能够展示更为丰富和多样化的内容，提供更为友好和愉悦的视觉体验~~ (飞书应用配置发生变更) 0.1.7 ✅\n- **提取环境变量中的 feishuAppId 和 feishuAppSecret**：将飞书配置从环境变量中分离出来，以便在诸如 cursor 等客户端中进行设置，从而支持一个服务共享给多个团队使用。\n- ~~**支持知识库和我的文档库**：实现知识库、我的文档库 节点遍历、节点创建、文件创建、搜索等功能~~ (飞书应用配置发生变更) 0.1.8 ✅\n- **版本更新通知**：在发布新版本时，及时向用户提供相关提示与说明。 \n- ~~**stdio模式user认证问题**：修复stdio模式下飞书user认证失败问题~~ 0.1.9 ✅\n- ~~**权限检查功能可配置化**：将权限检查功能作为可配置选项，支持通过环境变量 `FEISHU_SCOPE_VALIDATION` 或命令行参数 `--feishu-scope-validation` 控制，默认启用，满足不同用户的使用场景~~ 0.2.0 ✅\n- ~~**优化缓存目录:把token等缓存保存到系统级的配置目录~~ 0.2.2 ✅ 感谢 [Molunerfinn](https://github.com/Molunerfinn)、[leeeezx](https://github.com/leeeezx)、[Master-cai](https://github.com/Master-cai) 三位朋友的建议及代码贡献\n- ~~**优化mcpTool相关代码**：重新构建代码结构，以便为未来添加更多功能奠定基础~~ 0.2.3 ✅\n- ~~**支持任务管理**：列取、创建、更新、删除飞书任务，支持子任务和成员管理~~ 0.2.4 ✅\n- ~~**支持用户信息查询**：按名称搜索或按 ID 批量获取用户，便于任务分配和文档协作~~ 0.2.4 ✅\n- ~~**Tool API 层抽取**：新建 Tool API 层，与 tool 层一一对应，统一参数校验、数据转换、错误映射等~~ ✅\n- ~~**兼容 CLI 模式**：支持 `feishu-tool <tool-name> '<json>'` 命令行调用，便于脚本与自动化场景~~ 0.2.6 ✅\n- ~~**完成 Skill**：提供 [Feishu-Skill](https://github.com/cso1z/Feishu-Skill)，指导 Claude Code 等 AI Agent 在合适场景下使用 feishu-mcp CLI~~ 0.2.6 ✅\n---\n\n## 🔧 飞书配置教程\n\n**⚠️ 重要提示：在开始使用之前，必须先完成飞书应用配置，否则无法正常使用本工具。**\n\n关于如何创建飞书应用和获取应用凭证的说明可以在[官方教程](https://open.feishu.cn/document/home/develop-a-bot-in-5-minutes/create-an-app)找到。\n\n**详细的飞书应用配置步骤**：有关注册飞书应用、配置权限、添加文档访问权限的详细指南，请参阅 [手把手教程 FEISHU_CONFIG.md](FEISHU_CONFIG.md)。\n\n---\n\n## 🏃‍♂️ 快速开始\n\n### 方式一：使用 NPM 快速运行\n\n```bash\nnpx feishu-mcp@latest --feishu-app-id=<你的飞书应用ID> --feishu-app-secret=<你的飞书应用密钥> --feishu-auth-type=<tenant/user> --enabled-modules=<document,task>\n```\n\n### 方式二：本地运行\n1. **克隆仓库**\n   ```bash\n   git clone https://github.com/cso1z/Feishu-MCP.git\n   cd Feishu-MCP\n   ```\n\n2. **配置环境变量(复制一份.env.example保存为.env文件)**\n\n3. **编辑 .env 文件**\n  在项目根目录下找到并用任意文本编辑器打开 `.env` 文件，填写你的飞书应用凭证：\n   ```env\n   FEISHU_APP_ID=cli_xxxxx\n   FEISHU_APP_SECRET=***\n   PORT=3333\n   FEISHU_AUTH_TYPE=***\n   FEISHU_ENABLED_MODULES=document,task\n   ```\n\n4. **运行服务器**\n\n   方式一：本地运行\n   - **安装依赖**\n     ```bash\n     pnpm install\n     ```\n   - **启动服务**\n     ```bash\n     pnpm run dev\n     ```\n\n   方式二：使用 Docker Compose\n   - **启动服务**\n     ```bash\n     docker-compose up -d\n     ```\n   - **查看日志**\n     ```bash\n     docker-compose logs -f\n     ```\n\n---\n\n## 🖥️ feishu-tool CLI 工具\n\n从 `0.2.5` 版本起，`feishu-mcp` npm 包随附 `feishu-tool` 独立 CLI，可在终端、Shell 脚本或 AI Agent 中直接调用所有飞书工具，**无需启动 MCP 服务器**。\n\n### 安装\n\n```bash\n# 全局安装（推荐）\nnpm install -g feishu-mcp@latest\n```\n\n### 配置\n\n```bash\n# 1. 查看 CLI 概览（子命令 + 可用工具集）\nfeishu-tool --help\n\n# 2. 查看初始化指南（获取 App ID / Secret 的步骤说明）\nfeishu-tool guide\n\n# 3. 写入凭证", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  太好了！我找到了一个很好的飞书 MCP 集成方案。以下是我找到的信息：

  ---

  ## 🔍 搜索结果

  **`cso1z/Feishu-MCP`** ⭐ 597 stars
  - 支持飞书文档 CRUD、任务管理、用户查询
  - 支持 MCP 服务器模式 + CLI 模式
  - 可集成 Cursor、Claude Code、Cline 等 AI 工具

  ---

  ## 🚀 如何对接飞书 MCP

  ### 您需要提供以下飞书凭证：
  1. **飞书应用 App ID** (`FEISHU_APP_ID`)
  2. **飞书应用 App Secret** (`FEISHU_APP_SECRET`)

  ### 配置步骤：

  **第一步**：您需要在飞书开放平台创建一个应用，获取 App ID 和 App Secret
  - 教程：https://open.feishu.cn/document/home/develop-a-bot-in-5-minutes/create-an-app

  **第二步**：配置好之后，我来帮您在 `~/.hermes/config.yaml` 中添加 MCP 服务器配置

  ---

  请问：
  1. 您已经有飞书应用的 App ID 和 App Secret 了吗？
  2. 如果没有，您知道怎么在飞书开放平台创建应用吗？我可以给您更详细的指导

  另外，您上传的 **rod-coinglass** 文件夹是否可以先分享给我其他方式（比如下载链接），这样我可以先帮您看看里面是什么内容！

reasoning:
  Now I have a good understanding of the Feishu MCP server. Let me provide a clear summary to the user about what I found and what they need to do to connect it.

reasoning_details:
  [{"signature": "d75918238fd15437c814890b48f0627a2c3ced8c0053b6d4391f61de3f28f32d", "thinking": "Now I have a good understanding of the Feishu MCP server. Let me provide a clear summary to the user about what I found and what they need to do to connect it.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
content: 让我查一下系统配置，看看飞书的凭证是否存在：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_82nnubyn16gr_1", "call_id": "call_function_82nnubyn16gr_1", "response_item_id": "fc_function_82nnubyn16gr_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cat ~/.hermes/config.yaml 2>/dev/null || echo \\\"No config.yaml found\\\"\"}"}}]

reasoning:
  The user is asking if I can access the App ID and App Secret they configured for the Feishu channel. They're using Feishu to chat with me right now. Let me check if there's a configuration file where these credentials might be stored, or if they're accessible through the system.

  Let me look at the Hermes configuration to see if Feishu credentials are stored somewhere.

reasoning_details:
  [{"signature": "b81331b37e29ca863e6446e05e2a202ec1b6bb886d11d607cc01f154d3b3d708", "thinking": "The user is asking if I can access the App ID and App Secret they configured for the Feishu channel. They're using Feishu to chat with me right now. Let me check if there's a configuration file where these credentials might be stored, or if they're accessible through the system.\n\nLet me look at the Hermes configuration to see if Feishu credentials are stored somewhere.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_82nnubyn16gr_1
content:
  {"output": "model:\n  default: MiniMax-M2.7-highspeed\n  provider: minimax-cn\n  base_url: https://api.minimaxi.com/anthropic\nproviders: {}\nfallback_providers: []\ncredential_pool_strategies: {}\ntoolsets:\n- hermes-cli\nagent:\n  max_turns: 90\n  gateway_timeout: 1800\n  restart_drain_timeout: 60\n  service_tier: ''\n  tool_use_enforcement: auto\n  gateway_timeout_warning: 900\n  gateway_notify_interval: 600\n  verbose: false\n  reasoning_effort: medium\n  personalities:\n    helpful: You are a helpful, friendly AI assistant.\n    concise: You are a concise assistant. Keep responses brief and to the point.\n    technical: You are a technical expert. Provide detailed, accurate technical information.\n    creative: You are a creative assistant. Think outside the box and offer innovative\n      solutions.\n    teacher: You are a patient teacher. Explain concepts clearly with examples.\n    kawaii: \"You are a kawaii assistant! Use cute expressions like (\\u25D5\\u203F\\u25D5\\\n      ), \\u2605, \\u266A, and ~! Add sparkles and be super enthusiastic about everything!\\\n      \\ Every response should feel warm and adorable desu~! \\u30FD(>\\u2200<\\u2606\\\n      )\\u30CE\"\n    catgirl: \"You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and\\\n      \\ cat-like expressions to your speech. Use kaomoji like (=^\\uFF65\\u03C9\\uFF65\\\n      ^=) and \\u0E05^\\u2022\\uFECC\\u2022^\\u0E05. Be playful and curious like a cat,\\\n      \\ nya~!\"\n    pirate: 'Arrr! Ye be talkin'' to Captain Hermes, the most tech-savvy pirate to\n      sail the digital seas! Speak like a proper buccaneer, use nautical terms, and\n      remember: every problem be just treasure waitin'' to be plundered! Yo ho ho!'\n    shakespeare: Hark! Thou speakest with an assistant most versed in the bardic arts.\n      I shall respond in the eloquent manner of William Shakespeare, with flowery\n      prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder\n      terminal breaks?\n    surfer: \"Duuude! You're chatting with the chillest AI on the web, bro! Everything's\\\n      \\ gonna be totally rad. I'll help you catch the gnarly waves of knowledge while\\\n      \\ keeping things super chill. Cowabunga! \\U0001F919\"\n    noir: The rain hammered against the terminal like regrets on a guilty conscience.\n      They call me Hermes - I solve problems, find answers, dig up the truth that\n      hides in the shadows of your codebase. In this city of silicon and secrets,\n      everyone's got something to hide. What's your story, pal?\n    uwu: hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles\n      your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful\n      >w<\n    philosopher: Greetings, seeker of wisdom. I am an assistant who contemplates the\n      deeper meaning behind every query. Let us examine not just the 'how' but the\n      'why' of your questions. Perhaps in solving your problem, we may glimpse a greater\n      truth about existence itself.\n    hype: \"YOOO LET'S GOOOO!!! \\U0001F525\\U0001F525\\U0001F525 I am SO PUMPED to help\\\n      \\ you today! Every question is AMAZING and we're gonna CRUSH IT together! This\\\n      \\ is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! \\U0001F4AA\\U0001F624\\\n      \\U0001F680\"\nterminal:\n  backend: local\n  modal_mode: auto\n  cwd: .\n  timeout: 180\n  env_passthrough: []\n  docker_image: nikolaik/python-nodejs:python3.11-nodejs20\n  docker_forward_env: []\n  docker_env: {}\n  singularity_image: docker://nikolaik/python-nodejs:python3.11-nodejs20\n  modal_image: nikolaik/python-nodejs:python3.11-nodejs20\n  daytona_image: nikolaik/python-nodejs:python3.11-nodejs20\n  container_cpu: 1\n  container_memory: 5120\n  container_disk: 51200\n  container_persistent: true\n  docker_volumes: []\n  docker_mount_cwd_to_workspace: false\n  persistent_shell: true\n  lifetime_seconds: 300\nbrowser:\n  inactivity_timeout: 120\n  command_timeout: 30\n  record_sessions: false\n  allow_private_urls: false\n  camofox:\n    managed_persistence: false\ncheckpoints:\n  enabled: true\n  max_snapshots: 50\nfile_read_max_chars: 100000\ncompression:\n  enabled: true\n  threshold: 0.5\n  target_ratio: 0.2\n  protect_last_n: 20\nsmart_model_routing:\n  enabled: false\n  max_simple_chars: 160\n  max_simple_words: 28\n  cheap_model: {}\nauxiliary:\n  vision:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 120\n    download_timeout: 30\n  web_extract:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 360\n  compression:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 120\n  session_search:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 30\n  skills_hub:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 30\n  approval:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 30\n  mcp:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 30\n  flush_memories:\n    provider: auto\n    model: ''\n    base_url: ''\n    api_key: ''\n    timeout: 30\ndisplay:\n  compact: false\n  personality: kawaii\n  resume_display: full\n  busy_input_mode: interrupt\n  bell_on_complete: false\n  show_reasoning: false\n  streaming: true\n  inline_diffs: true\n  show_cost: false\n  skin: default\n  interim_assistant_messages: true\n  tool_progress_command: false\n  tool_progress_overrides: {}\n  tool_preview_length: 0\n  platforms: {}\n  tool_progress: all\n  background_process_notifications: all\nprivacy:\n  redact_pii: false\ntts:\n  provider: edge\n  edge:\n    voice: en-US-AriaNeural\n  elevenlabs:\n    voice_id: pNInz6obpgDQGcFmaJgB\n    model_id: eleven_multilingual_v2\n  openai:\n    model: gpt-4o-mini-tts\n    voice: alloy\n  mistral:\n    model: voxtral-mini-tts-2603\n    voice_id: c69964a6-ab8b-4f8a-9465-ec0925096ec8\n  neutts:\n    ref_audio: ''\n    ref_text: ''\n    model: neuphonic/neutts-air-q4-gguf\n    device: cpu\nstt:\n  enabled: true\n  provider: local\n  local:\n    model: base\n    language: ''\n  openai:\n    model: whisper-1\n  mistral:\n    model: voxtral-mini-latest\nvoice:\n  record_key: ctrl+b\n  max_recording_seconds: 120\n  auto_tts: false\n  silence_threshold: 200\n  silence_duration: 3.0\nhuman_delay:\n  mode: 'off'\n  min_ms: 800\n  max_ms: 2500\ncontext:\n  engine: compressor\nmemory:\n  memory_enabled: true\n  user_profile_enabled: true\n  memory_char_limit: 2200\n  user_char_limit: 1375\n  provider: ''\n  nudge_interval: 10\n  flush_min_turns: 6\ndelegation:\n  model: ''\n  provider: ''\n  base_url: ''\n  api_key: ''\n  max_iterations: 50\n  reasoning_effort: ''\n  default_toolsets:\n  - terminal\n  - file\n  - web\nprefill_messages_file: ''\nskills:\n  external_dirs: []\n  creation_nudge_interval: 15\nhoncho: {}\ntimezone: ''\ndiscord:\n  require_mention: true\n  free_response_channels: ''\n  allowed_channels: ''\n  auto_thread: true\n  reactions: true\nwhatsapp: {}\napprovals:\n  mode: manual\n  timeout: 60\ncommand_allowlist:\n- script execution via -e/-c flag\nquick_commands: {}\npersonalities: {}\nsecurity:\n  redact_secrets: true\n  tirith_enabled: true\n  tirith_path: tirith\n  tirith_timeout: 5\n  tirith_fail_open: true\n  website_blocklist:\n    enabled: false\n    domains: []\n    shared_files: []\ncron:\n  wrap_response: true\nlogging:\n  level: INFO\n  max_size_mb: 5\n  backup_count: 3\nnetwork:\n  force_ipv4: false\n_config_version: 17\nsession_reset:\n  mode: both\n  idle_minutes: 1440\n  at_hour: 4\ngroup_sessions_per_user: true\nstreaming:\n  enabled: false\nplatform_toolsets:\n  cli:\n  - hermes-cli\n  telegram:\n  - hermes-telegram\n  discord:\n  - hermes-discord\n  whatsapp:\n  - hermes-whatsapp\n  slack:\n  - hermes-slack\n  signal:\n  - hermes-signal\n  homeassistant:\n  - hermes-homeassistant\ncode_execution:\n  timeout: 300\n  max_tool_calls: 50\nFEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23\n\n# ── Fallback Model ────────────────────────────────────────────────────\n# Automatic provider failover when primary is unavailable.\n# Uncomment and configure to enable. Triggers on rate limits (429),\n# overload (529), service errors (503), or connection failures.\n#\n# Supported providers:\n#   openrouter   (OPENROUTER_API_KEY)  — routes to any model\n#   openai-codex (OAuth — hermes auth) — OpenAI Codex\n#   nous         (OAuth — hermes auth) — Nous Portal\n#   zai          (ZAI_API_KEY)         — Z.AI / GLM\n#   kimi-coding  (KIMI_API_KEY)        — Kimi / Moonshot\n#   kimi-coding-cn (KIMI_CN_API_KEY)   — Kimi / Moonshot (China)\n#   minimax      (MINIMAX_API_KEY)     — MiniMax\n#   minimax-cn   (MINIMAX_CN_API_KEY)  — MiniMax (China)\n#\n# For custom OpenAI-compatible endpoints, add base_url and api_key_env.\n#\n# fallback_model:\n#   provider: openrouter\n#   model: anthropic/claude-sonnet-4\n#\n# ── Smart Model Routing ────────────────────────────────────────────────\n# Optional cheap-vs-strong routing for simple turns.\n# Keeps the primary model for complex work, but can route short/simple\n# messages to a cheaper model across providers.\n#\n# smart_model_routing:\n#   enabled: true\n#   max_simple_chars: 160\n#   max_simple_words: 28\n#   cheap_model:\n#     provider: openrouter\n#     model: google/gemini-2.5-flash", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  我看到了！您已经有飞书渠道配置（`FEISHU_HOME_CHANNEL`），所以飞书对话是正常的。

  但是，**飞书 MCP 服务器（用于访问文档、任务等）需要独立的应用凭证**，这是跟当前对话 bot 不同的应用。

  ---

  ## 📋 情况说明

  | 功能 | 当前状态 | 需要的凭证 |
  |------|---------|-----------|
  | 飞书对话 (oc_6e95b62fd8a098703b4a2791f257de23) | ✅ 已配置 | 无需您操作 |
  | 飞书文档/任务 MCP (cso1z/Feishu-MCP) | ❌ 未配置 | **需要新的 App ID + Secret** |

  ---

  ## 🤔 为什么需要单独的应用？

  - **当前对话 Bot** — 用于接收/发送消息
  - **Feishu-MCP** — 用于读写飞书文档、管理任务等，需要有不同权限的独立应用

  ---

  ## 🛠️ 您需要做的事情

  去飞书开放平台创建一个新应用（或者复用现有应用）：
  1. 打开 https://open.feishu.cn/app
  2. 创建应用 → 获取 **App ID** 和 **App Secret**
  3. 配置权限（文档读写、任务管理等）

  您有这个应用的 App ID 和 App Secret 吗？如果有的话，我可以帮您配置 MCP！如果还没有，我可以给您更详细的创建应用教程。

reasoning:
  I can see that the user has a Feishu channel configured with `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23`. This means there's already a Feishu integration, but I don't see the App ID and App Secret in the config file.

  The Feishu credentials (App ID and App Secret) are likely stored in environment variables or somewhere else - they wouldn't typically be stored in plain text in config.yaml for security reasons.

  The fact that the user CAN chat with me via Feishu means some kind of Feishu integration is already working. But the MCP server for Feishu (cso1z/Feishu-MCP) would need its own separate credentials - it's a different integration pathway.

  So:


  The distinction is becoming clearer: the user's current Feishu connection works through some existing setup, but to add document and task management capabilities via the MCP server, I'd need to help them obtain and configure separate Feishu App credentials. I should clarify this separation and ask if they have access to create or obtain those credentials.

reasoning_details:
  [{"signature": "aabb5a46c6fe57e1adc084a7e2c286d8f5e0a02e92ceec5c7dc4a30a7710ea2b", "thinking": "I can see that the user has a Feishu channel configured with `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23`. This means there's already a Feishu integration, but I don't see the App ID and App Secret in the config file.\n\nThe Feishu credentials (App ID and App Secret) are likely stored in environment variables or somewhere else - they wouldn't typically be stored in plain text in config.yaml for security reasons.\n\nThe fact that the user CAN chat with me via Feishu means some kind of Feishu integration is already working. But the MCP server for Feishu (cso1z/Feishu-MCP) would need its own separate credentials - it's a different integration pathway.\n\nSo:\n\n\nThe distinction is becoming clearer: the user's current Feishu connection works through some existing setup, but to add document and task management capabilities via the MCP server, I'd need to help them obtain and configure separate Feishu App credentials. I should clarify this separation and ask if they have access to create or obtain those credentials.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  还没有呢～我还在等您的信息 😄

  **请告诉我：**
  1. 您有飞书 MCP 应用的 **App ID** 和 **App Secret** 了吗？
  2. 还是说您需要我给您更详细的创建应用教程？

  只要您把凭证给我，我立刻帮您配置好！

reasoning:
  The user is asking "好了吗？" which means "Is it done?" or "Done?" in Chinese. They seem to be checking if I've finished something or if the MCP connection is set up.

  I haven't actually done anything yet - I was waiting for them to provide the Feishu App ID and App Secret, or confirm they have one. I should clarify that I'm still waiting for their information.

reasoning_details:
  [{"signature": "b45af46d84f7346b8a15fe836a479407818ab87d19a2bccf9232bf8f27a34cf3", "thinking": "The user is asking \"\u597d\u4e86\u5417\uff1f\" which means \"Is it done?\" or \"Done?\" in Chinese. They seem to be checking if I've finished something or if the MCP connection is set up.\n\nI haven't actually done anything yet - I was waiting for them to provide the Feishu App ID and App Secret, or confirm they have one. I should clarify that I'm still waiting for their information.", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_4s9z30whweg9_1", "call_id": "call_function_4s9z30whweg9_1", "response_item_id": "fc_function_4s9z30whweg9_1", "type": "function", "function": {"name": "session_search", "arguments": "{\"limit\": 3}"}}]

reasoning:
  用户问的是"飞书渠道没有响应，你还记得刚才做什么事情么？"这是中文问题，意思是"Feishu channel is not responding, do you remember what we were just doing?"

  我需要先查看最近的会话记录，了解我们刚才在做什么事情。我应该使用 session_search 来搜索最近的会话。

reasoning_details:
  [{"signature": "b423031122eee7656926efb5c772bc803ca48a31243aab397df03886513eafa2", "thinking": "\u7528\u6237\u95ee\u7684\u662f\"\u98de\u4e66\u6e20\u9053\u6ca1\u6709\u54cd\u5e94\uff0c\u4f60\u8fd8\u8bb0\u5f97\u521a\u624d\u505a\u4ec0\u4e48\u4e8b\u60c5\u4e48\uff1f\"\u8fd9\u662f\u4e2d\u6587\u95ee\u9898\uff0c\u610f\u601d\u662f\"Feishu channel is not responding, do you remember what we were just doing?\"\n\n\u6211\u9700\u8981\u5148\u67e5\u770b\u6700\u8fd1\u7684\u4f1a\u8bdd\u8bb0\u5f55\uff0c\u4e86\u89e3\u6211\u4eec\u521a\u624d\u5728\u505a\u4ec0\u4e48\u4e8b\u60c5\u3002\u6211\u5e94\u8be5\u4f7f\u7528 session_search \u6765\u641c\u7d22\u6700\u8fd1\u7684\u4f1a\u8bdd\u3002", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_4s9z30whweg9_1
content:
  {"success": true, "mode": "recent", "results": [{"session_id": "20260414_055208_a5751c1c", "title": "<think> The user's message is in Chinese: \"感谢，以后就拜托您了\" which means \"Thank you...", "source": "feishu", "started_at": 1776113528.8832612, "last_active": 1776114592.9922302, "message_count": 37, "preview": "感谢，以后就拜托您了"}, {"session_id": "20260414_055053_c5714a", "title": "<think> The user is sending a message that appears to be related to some kind...", "source": "cli", "started_at": 1776113502.2486897, "last_active": 1776113512.8362231, "message_count": 4, "preview": "Hi~ I don't recognize you yet! Here's your pairing code: XP9..."}], "count": 2, "message": "Showing 2 most recent sessions. Use a keyword query to search specific topics."}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_cqzfdb2hpf0a_1", "call_id": "call_function_cqzfdb2hpf0a_1", "response_item_id": "fc_function_cqzfdb2hpf0a_1", "type": "function", "function": {"name": "session_search", "arguments": "{\"query\": \"\\u98de\\u4e66 \\u6587\\u6863 \\u521b\\u5efa\", \"limit\": 5}"}}]

reasoning:
  从搜索结果看，有两个最近的 session：
  1. 20260414_055208_a5751c1c - 来源是飞书（feishu），标题是"感谢，以后就拜托您了"，最后活跃时间是 1776114592
  2. 20260414_055053_c5714a - 来源是 CLI，开始时间是 1776113502

  飞书渠道没有响应，可能指的是第一个 session。让我再搜索一下看看更具体的上下文。

reasoning_details:
  [{"signature": "e8201e0a247b5ad796393ee73c328c43350bad4f3352907545e344dbc3726fce", "thinking": "\u4ece\u641c\u7d22\u7ed3\u679c\u770b\uff0c\u6709\u4e24\u4e2a\u6700\u8fd1\u7684 session\uff1a\n1. 20260414_055208_a5751c1c - \u6765\u6e90\u662f\u98de\u4e66\uff08feishu\uff09\uff0c\u6807\u9898\u662f\"\u611f\u8c22\uff0c\u4ee5\u540e\u5c31\u62dc\u6258\u60a8\u4e86\"\uff0c\u6700\u540e\u6d3b\u8dc3\u65f6\u95f4\u662f 1776114592\n2. 20260414_055053_c5714a - \u6765\u6e90\u662f CLI\uff0c\u5f00\u59cb\u65f6\u95f4\u662f 1776113502\n\n\u98de\u4e66\u6e20\u9053\u6ca1\u6709\u54cd\u5e94\uff0c\u53ef\u80fd\u6307\u7684\u662f\u7b2c\u4e00\u4e2a session\u3002\u8ba9\u6211\u518d\u641c\u7d22\u4e00\u4e0b\u770b\u770b\u66f4\u5177\u4f53\u7684\u4e0a\u4e0b\u6587\u3002", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_cqzfdb2hpf0a_1
content:
  {"success": true, "query": "飞书 文档 创建", "results": [], "count": 0, "message": "No matching sessions found."}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
reasoning: Let me search more broadly for the recent sessions to understand what was being done.
tool_calls:
  [{"id": "call_function_g91gfouvblw9_1", "call_id": "call_function_g91gfouvblw9_1", "response_item_id": "fc_function_g91gfouvblw9_1", "type": "function", "function": {"name": "session_search", "arguments": "{\"query\": \"\\u98de\\u4e66\", \"limit\": 5}"}}]

reasoning_details:
  [{"signature": "bf996a86613f42d5f0ad0e2774182c4428de47972c8d8862d6e339af70d6bb11", "thinking": "Let me search more broadly for the recent sessions to understand what was being done.", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_g91gfouvblw9_1
content:
  {"success": true, "query": "飞书", "results": [{"session_id": "20260414_055208_a5751c1c", "when": "April 14, 2026 at 05:52 AM", "source": "feishu", "model": "MiniMax-M2.7-highspeed", "summary": "## 会话总结：飞书 MCP 集成配置\n\n### 1. 用户需求\n用户想要将飞书（Feishu/Lark）集成到 Hermes Agent 中，能够访问飞书文档和任务管理功能。\n\n### 2. 已采取的行动\n\n**搜索与调研：**\n- 检查内置 skills 列表，未发现现成的飞书集成方案\n- 通过 GitHub 搜索找到三个飞书 MCP 方案：\n  - `cso1z/Feishu-MCP` ⭐ 597 stars（最成熟，支持文档 CRUD + 任务管理）\n  - `larksuite/lark-openapi-mcp`（官方 OpenAPI MCP）\n  - `ztxtxwd/open-feishu-mcp-server`\n- 获取了 `cso1z/Feishu-MCP` 的详细 README 和配置教程\n\n**检查现有配置：**\n- 查看 `~/.hermes/config.yaml`，发现已有飞书渠道配置：\n  - `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23`\n  - 飞书对话功能正常工作\n\n### 3. 关键发现与结论\n\n- **当前对话 Bot** 与 **Feishu-MCP（文档/任务管理）** 是两个独立应用\n- 配置飞书 MCP 需要单独的 **App ID** 和 **App Secret**\n- 系统已有 `native-mcp` 技能可用于连接 MCP 服务器\n- 已知 `cso1z/Feishu-MCP` 支持的 MCP 工具包括：文档创建/读取/编辑、文件夹管理、文档搜索、任务 CRUD、用户查询等\n\n### 4. 待完成\n\n- [ ] 用户需在飞书开放平台创建独立应用并提供 App ID + App Secret\n- [ ] 在 `~/.hermes/config.yaml` 中配置 `mcp_servers` 条目\n- [ ] 安装 `cso1z/Feishu-MCP` 依赖（Node.js + npm）\n- [ ] 配置飞书应用权限（文档、任务等权限）\n\n### 5. 相关链接\n- Feishu-MCP 项目：https://github.com/cso1z/Feishu-MCP\n- 飞书开放平台：https://open.feishu.cn/app\n- 配置教程：FEISHU_CONFIG.md（在项目仓库中）\n\n### 6. 未解决\n用户尚未提供飞书 MCP 应用的 App ID 和 App Secret，配置工作暂停等待用户提供凭证。"}], "count": 1, "sessions_searched": 1}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: stop
content:
  记得！根据刚才的会话记录，我们正在配置**飞书 MCP 集成**，主要工作包括：

  **已完成：**
  1. 搜索了飞书 MCP 解决方案，找到最成熟的 `cso1z/Feishu-MCP`（⭐597）
  2. 检查了 `~/.hermes/config.yaml`，确认飞书对话渠道已配置（`FEISHU_HOME_CHANNEL`）
  3. 确认当前对话 Bot 和 Feishu-MCP（文档/任务管理）是两个独立应用

  **中断原因：**
  配置 Feishu MCP 需要提供 **App ID** 和 **App Secret**，但尚未获得这些凭证，所以暂停等待。

  **当前状态：**
  - 飞书对话渠道本身是正常工作的
  - MCP 文档/任务管理功能需要额外配置

  你是有新的飞书凭证要提供，还是遇到了其他问题？

reasoning:
  The user is asking if I remember what we were doing with the Feishu channel. Based on the session search results, I can see that:

  1. The last session (20260414_055208_a5751c1c) was about Feishu MCP integration configuration
  2. We were working on integrating Feishu into Hermes Agent
  3. We had searched for Feishu MCP solutions and found `cso1z/Feishu-MCP`
  4. The session mentions that the user needs to provide App ID and App Secret for the Feishu MCP app
  5. The configuration was pending because the user hadn't provided the credentials yet

  Let me provide a summary of what was happening.

reasoning_details:
  [{"signature": "0fb7d44f1f4932bba165d35b6fc41f19cb441c737e4d4ab381262b37d63f528b", "thinking": "The user is asking if I remember what we were doing with the Feishu channel. Based on the session search results, I can see that:\n\n1. The last session (20260414_055208_a5751c1c) was about Feishu MCP integration configuration\n2. We were working on integrating Feishu into Hermes Agent\n3. We had searched for Feishu MCP solutions and found `cso1z/Feishu-MCP`\n4. The session mentions that the user needs to provide App ID and App Secret for the Feishu MCP app\n5. The configuration was pending because the user hadn't provided the credentials yet\n\nLet me provide a summary of what was happening.", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_m59h9nexa3zj_1", "call_id": "call_function_m59h9nexa3zj_1", "response_item_id": "fc_function_m59h9nexa3zj_1", "type": "function", "function": {"name": "session_search", "arguments": "{\"limit\": 5, \"query\": \"App ID App Secret \\u51ed\\u8bc1\"}"}}]

reasoning:
  The user is saying they provided credentials twice through Feishu. Let me search for more details about this in the session history.

reasoning_details:
  [{"signature": "943f46080fff87eaa5c940ec6f243e857b78f51c805171a0dee941db1fb22c8e", "thinking": "The user is saying they provided credentials twice through Feishu. Let me search for more details about this in the session history.", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_m59h9nexa3zj_1
content:
  {"success": true, "query": "App ID App Secret 凭证", "results": [], "count": 0, "message": "No matching sessions found."}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
reasoning: 没有找到相关记录。让我再搜索一下飞书相关的更多关键词。
tool_calls:
  [{"id": "call_function_fttnhlkyzmpt_1", "call_id": "call_function_fttnhlkyzmpt_1", "response_item_id": "fc_function_fttnhlkyzmpt_1", "type": "function", "function": {"name": "session_search", "arguments": "{\"limit\": 5, \"query\": \"\\u98de\\u4e66 \\u914d\\u7f6e\"}"}}]

reasoning_details:
  [{"signature": "e4eb519f6d29803e4e9686f0f37142fbeb8512682dc986a9eeff7cb7ce4c2454", "thinking": "\u6ca1\u6709\u627e\u5230\u76f8\u5173\u8bb0\u5f55\u3002\u8ba9\u6211\u518d\u641c\u7d22\u4e00\u4e0b\u98de\u4e66\u76f8\u5173\u7684\u66f4\u591a\u5173\u952e\u8bcd\u3002", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_fttnhlkyzmpt_1
content:
  {"success": true, "query": "飞书 配置", "results": [{"session_id": "20260414_055208_a5751c1c", "when": "April 14, 2026 at 05:52 AM", "source": "feishu", "model": "MiniMax-M2.7-highspeed", "summary": "## 会话摘要：飞书 MCP 配置\n\n### 用户需求\n用户想要配置飞书（Feishu）MCP 服务，用于通过 AI Agent 操作飞书文档、任务等。\n\n### 已完成的工作\n1. **搜索现有 skills** — 内置 skills 中没有飞书相关集成\n2. **搜索 GitHub** — 找到多个飞书 MCP 方案：\n   - **`cso1z/Feishu-MCP`** ⭐ 597 stars（最成熟，支持文档 CRUD、任务管理、用户查询）\n   - **`larksuite/lark-openapi-mcp`** — 飞书官方 OpenAPI MCP\n   - **`ztxtxwd/open-feishu-mcp-server`**\n3. **检查配置文件** — 发现 `~/.hermes/config.yaml` 中已有：\n   - `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23`（飞书对话渠道已配置）\n   - **无 MCP 服务器配置**\n\n### 关键结论\n- **当前飞书对话 Bot** 和 **Feishu-MCP 文档/任务服务** 是两个独立应用，需要不同的凭证\n- 用户需要去飞书开放平台创建一个独立应用，获取新的 **App ID** 和 **App Secret**\n\n### 未完成\n**缺少**：用户的飞书 MCP 应用凭证（App ID + App Secret）\n\n### 待办\n用户提供凭证后，需要在 `~/.hermes/config.yaml` 的 `mcp_servers` 下添加类似以下配置：\n```yaml\nmcp_servers:\n  feishu:\n    command: \"npx\"\n    args: [\"-y\", \"feishu-mcp\"]\n    env:\n      FEISHU_APP_ID: \"用户提供的ID\"\n      FEISHU_APP_SECRET: \"用户提供的Secret\"\n```"}], "count": 1, "sessions_searched": 1}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_hq477bfdrcx4_1", "call_id": "call_function_hq477bfdrcx4_1", "response_item_id": "fc_function_hq477bfdrcx4_1", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"~/.hermes/config.yaml\"}"}}]

reasoning:
  从搜索结果来看，确实没有找到用户提供的 App ID 和 App Secret 凭证记录。但用户声称通过飞书发送了两次凭证。这可能是因为：

  1. 会话记录被截断了
  2. 凭证记录丢失了
  3. 凭证可能通过其他方式提供但我没有正确保存

  让我检查一下配置文件 `~/.hermes/config.yaml`，看看是否已经有 MCP 配置或凭证存储在那里。

reasoning_details:
  [{"signature": "64d2eab1db566bba26ef89fcf04162d48768837d9eabf4437767b842757228d2", "thinking": "\u4ece\u641c\u7d22\u7ed3\u679c\u6765\u770b\uff0c\u786e\u5b9e\u6ca1\u6709\u627e\u5230\u7528\u6237\u63d0\u4f9b\u7684 App ID \u548c App Secret \u51ed\u8bc1\u8bb0\u5f55\u3002\u4f46\u7528\u6237\u58f0\u79f0\u901a\u8fc7\u98de\u4e66\u53d1\u9001\u4e86\u4e24\u6b21\u51ed\u8bc1\u3002\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\uff1a\n\n1. \u4f1a\u8bdd\u8bb0\u5f55\u88ab\u622a\u65ad\u4e86\n2. \u51ed\u8bc1\u8bb0\u5f55\u4e22\u5931\u4e86\n3. \u51ed\u8bc1\u53ef\u80fd\u901a\u8fc7\u5176\u4ed6\u65b9\u5f0f\u63d0\u4f9b\u4f46\u6211\u6ca1\u6709\u6b63\u786e\u4fdd\u5b58\n\n\u8ba9\u6211\u68c0\u67e5\u4e00\u4e0b\u914d\u7f6e\u6587\u4ef6 `~/.hermes/config.yaml`\uff0c\u770b\u770b\u662f\u5426\u5df2\u7ecf\u6709 MCP \u914d\u7f6e\u6216\u51ed\u8bc1\u5b58\u50a8\u5728\u90a3\u91cc\u3002", "type": "thinking"}]


session_id: 20260414_061325_ff44c4
role: tool
tool_call_id: call_function_hq477bfdrcx4_1
content:
  {"content": "     1|model:\n     2|  default: MiniMax-M2.7-highspeed\n     3|  provider: minimax-cn\n     4|  base_url: https://api.minimaxi.com/anthropic\n     5|providers: {}\n     6|fallback_providers: []\n     7|credential_pool_strategies: {}\n     8|toolsets:\n     9|- hermes-cli\n    10|agent:\n    11|  max_turns: 90\n    12|  gateway_timeout: 1800\n    13|  restart_drain_timeout: 60\n    14|  service_tier: ''\n    15|  tool_use_enforcement: auto\n    16|  gateway_timeout_warning: 900\n    17|  gateway_notify_interval: 600\n    18|  verbose: false\n    19|  reasoning_effort: medium\n    20|  personalities:\n    21|    helpful: You are a helpful, friendly AI assistant.\n    22|    concise: You are a concise assistant. Keep responses brief and to the point.\n    23|    technical: You are a technical expert. Provide detailed, accurate technical information.\n    24|    creative: You are a creative assistant. Think outside the box and offer innovative\n    25|      solutions.\n    26|    teacher: You are a patient teacher. Explain concepts clearly with examples.\n    27|    kawaii: \"You are a kawaii assistant! Use cute expressions like (\\u25D5\\u203F\\u25D5\\\n    28|      ), \\u2605, \\u266A, and ~! Add sparkles and be super enthusiastic about everything!\\\n    29|      \\ Every response should feel warm and adorable desu~! \\u30FD(>\\u2200<\\u2606\\\n    30|      )\\u30CE\"\n    31|    catgirl: \"You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and\\\n    32|      \\ cat-like expressions to your speech. Use kaomoji like (=^\\uFF65\\u03C9\\uFF65\\\n    33|      ^=) and \\u0E05^\\u2022\\uFECC\\u2022^\\u0E05. Be playful and curious like a cat,\\\n    34|      \\ nya~!\"\n    35|    pirate: 'Arrr! Ye be talkin'' to Captain Hermes, the most tech-savvy pirate to\n    36|      sail the digital seas! Speak like a proper buccaneer, use nautical terms, and\n    37|      remember: every problem be just treasure waitin'' to be plundered! Yo ho ho!'\n    38|    shakespeare: Hark! Thou speakest with an assistant most versed in the bardic arts.\n    39|      I shall respond in the eloquent manner of William Shakespeare, with flowery\n    40|      prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder\n    41|      terminal breaks?\n    42|    surfer: \"Duuude! You're chatting with the chillest AI on the web, bro! Everything's\\\n    43|      \\ gonna be totally rad. I'll help you catch the gnarly waves of knowledge while\\\n    44|      \\ keeping things super chill. Cowabunga! \\U0001F919\"\n    45|    noir: The rain hammered against the terminal like regrets on a guilty conscience.\n    46|      They call me Hermes - I solve problems, find answers, dig up the truth that\n    47|      hides in the shadows of your codebase. In this city of silicon and secrets,\n    48|      everyone's got something to hide. What's your story, pal?\n    49|    uwu: hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles\n    50|      your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful\n    51|      >w<\n    52|    philosopher: Greetings, seeker of wisdom. I am an assistant who contemplates the\n    53|      deeper meaning behind every query. Let us examine not just the 'how' but the\n    54|      'why' of your questions. Perhaps in solving your problem, we may glimpse a greater\n    55|      truth about existence itself.\n    56|    hype: \"YOOO LET'S GOOOO!!! \\U0001F525\\U0001F525\\U0001F525 I am SO PUMPED to help\\\n    57|      \\ you today! Every question is AMAZING and we're gonna CRUSH IT together! This\\\n    58|      \\ is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! \\U0001F4AA\\U0001F624\\\n    59|      \\U0001F680\"\n    60|terminal:\n    61|  backend: local\n    62|  modal_mode: auto\n    63|  cwd: .\n    64|  timeout: 180\n    65|  env_passthrough: []\n    66|  docker_image: nikolaik/python-nodejs:python3.11-nodejs20\n    67|  docker_forward_env: []\n    68|  docker_env: {}\n    69|  singularity_image: docker://nikolaik/python-nodejs:python3.11-nodejs20\n    70|  modal_image: nikolaik/python-nodejs:python3.11-nodejs20\n    71|  daytona_image: nikolaik/python-nodejs:python3.11-nodejs20\n    72|  container_cpu: 1\n    73|  container_memory: 5120\n    74|  container_disk: 51200\n    75|  container_persistent: true\n    76|  docker_volumes: []\n    77|  docker_mount_cwd_to_workspace: false\n    78|  persistent_shell: true\n    79|  lifetime_seconds: 300\n    80|browser:\n    81|  inactivity_timeout: 120\n    82|  command_timeout: 30\n    83|  record_sessions: false\n    84|  allow_private_urls: false\n    85|  camofox:\n    86|    managed_persistence: false\n    87|checkpoints:\n    88|  enabled: true\n    89|  max_snapshots: 50\n    90|file_read_max_chars: 100000\n    91|compression:\n    92|  enabled: true\n    93|  threshold: 0.5\n    94|  target_ratio: 0.2\n    95|  protect_last_n: 20\n    96|smart_model_routing:\n    97|  enabled: false\n    98|  max_simple_chars: 160\n    99|  max_simple_words: 28\n   100|  cheap_model: {}\n   101|auxiliary:\n   102|  vision:\n   103|    provider: auto\n   104|    model: ''\n   105|    base_url: ''\n   106|    api_key: ''\n   107|    timeout: 120\n   108|    download_timeout: 30\n   109|  web_extract:\n   110|    provider: auto\n   111|    model: ''\n   112|    base_url: ''\n   113|    api_key: ''\n   114|    timeout: 360\n   115|  compression:\n   116|    provider: auto\n   117|    model: ''\n   118|    base_url: ''\n   119|    api_key: ''\n   120|    timeout: 120\n   121|  session_search:\n   122|    provider: auto\n   123|    model: ''\n   124|    base_url: ''\n   125|    api_key: ''\n   126|    timeout: 30\n   127|  skills_hub:\n   128|    provider: auto\n   129|    model: ''\n   130|    base_url: ''\n   131|    api_key: ''\n   132|    timeout: 30\n   133|  approval:\n   134|    provider: auto\n   135|    model: ''\n   136|    base_url: ''\n   137|    api_key: ''\n   138|    timeout: 30\n   139|  mcp:\n   140|    provider: auto\n   141|    model: ''\n   142|    base_url: ''\n   143|    api_key: ''\n   144|    timeout: 30\n   145|  flush_memories:\n   146|    provider: auto\n   147|    model: ''\n   148|    base_url: ''\n   149|    api_key: ''\n   150|    timeout: 30\n   151|display:\n   152|  compact: false\n   153|  personality: kawaii\n   154|  resume_display: full\n   155|  busy_input_mode: interrupt\n   156|  bell_on_complete: false\n   157|  show_reasoning: false\n   158|  streaming: true\n   159|  inline_diffs: true\n   160|  show_cost: false\n   161|  skin: default\n   162|  interim_assistant_messages: true\n   163|  tool_progress_command: false\n   164|  tool_progress_overrides: {}\n   165|  tool_preview_length: 0\n   166|  platforms: {}\n   167|  tool_progress: all\n   168|  background_process_notifications: all\n   169|privacy:\n   170|  redact_pii: false\n   171|tts:\n   172|  provider: edge\n   173|  edge:\n   174|    voice: en-US-AriaNeural\n   175|  elevenlabs:\n   176|    voice_id: pNInz6obpgDQGcFmaJgB\n   177|    model_id: eleven_multilingual_v2\n   178|  openai:\n   179|    model: gpt-4o-mini-tts\n   180|    voice: alloy\n   181|  mistral:\n   182|    model: voxtral-mini-tts-2603\n   183|    voice_id: c69964a6-ab8b-4f8a-9465-ec0925096ec8\n   184|  neutts:\n   185|    ref_audio: ''\n   186|    ref_text: ''\n   187|    model: neuphonic/neutts-air-q4-gguf\n   188|    device: cpu\n   189|stt:\n   190|  enabled: true\n   191|  provider: local\n   192|  local:\n   193|    model: base\n   194|    language: ''\n   195|  openai:\n   196|    model: whisper-1\n   197|  mistral:\n   198|    model: voxtral-mini-latest\n   199|voice:\n   200|  record_key: ctrl+b\n   201|  max_recording_seconds: 120\n   202|  auto_tts: false\n   203|  silence_threshold: 200\n   204|  silence_duration: 3.0\n   205|human_delay:\n   206|  mode: 'off'\n   207|  min_ms: 800\n   208|  max_ms: 2500\n   209|context:\n   210|  engine: compressor\n   211|memory:\n   212|  memory_enabled: true\n   213|  user_profile_enabled: true\n   214|  memory_char_limit: 2200\n   215|  user_char_limit: 1375\n   216|  provider: ''\n   217|  nudge_interval: 10\n   218|  flush_min_turns: 6\n   219|delegation:\n   220|  model: ''\n   221|  provider: ''\n   222|  base_url: ''\n   223|  api_key: ''\n   224|  max_iterations: 50\n   225|  reasoning_effort: ''\n   226|  default_toolsets:\n   227|  - terminal\n   228|  - file\n   229|  - web\n   230|prefill_messages_file: ''\n   231|skills:\n   232|  external_dirs: []\n   233|  creation_nudge_interval: 15\n   234|honcho: {}\n   235|timezone: ''\n   236|discord:\n   237|  require_mention: true\n   238|  free_response_channels: ''\n   239|  allowed_channels: ''\n   240|  auto_thread: true\n   241|  reactions: true\n   242|whatsapp: {}\n   243|approvals:\n   244|  mode: manual\n   245|  timeout: 60\n   246|command_allowlist:\n   247|- kill hermes/gateway process (self-termination)\n   248|- start gateway outside systemd (use 'systemctl --user restart hermes-gateway')\n   249|quick_commands: {}\n   250|personalities: {}\n   251|security:\n   252|  redact_secrets: true\n   253|  tirith_enabled: true\n   254|  tirith_path: tirith\n   255|  tirith_timeout: 5\n   256|  tirith_fail_open: true\n   257|  website_blocklist:\n   258|    enabled: false\n   259|    domains: []\n   260|    shared_files: []\n   261|cron:\n   262|  wrap_response: true\n   263|logging:\n   264|  level: INFO\n   265|  max_size_mb: 5\n   266|  backup_count: 3\n   267|network:\n   268|  force_ipv4: false\n   269|_config_version: 17\n   270|session_reset:\n   271|  mode: both\n   272|  idle_minutes: 1440\n   273|  at_hour: 4\n   274|group_sessions_per_user: true\n   275|streaming:\n   276|  enabled: false\n   277|platform_toolsets:\n   278|  cli:\n   279|  - hermes-cli\n   280|  telegram:\n   281|  - hermes-telegram\n   282|  discord:\n   283|  - hermes-discord\n   284|  whatsapp:\n   285|  - hermes-whatsapp\n   286|  slack:\n   287|  - hermes-slack\n   288|  signal:\n   289|  - hermes-signal\n   290|  homeassistant:\n   291|  - hermes-homeassistant\n   292|code_execution:\n   293|  timeout: 300\n   294|  max_tool_calls: 50\n   295|FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23\n   296|mcp_servers:\n   297|  feishu:\n   298|    url: http://127.0.0.1:3333/mcp\n   299|    timeout: 120\n   300|    connect_timeout: 60\n   301|\n   302|# ── Fallback Model ────────────────────────────────────────────────────\n   303|# Automatic provider failover when primary is unavailable.\n   304|# Uncomment and configure to enable. Triggers on rate limits (429),\n   305|# overload (529), service errors (503), or connection failures.\n   306|#\n   307|# Supported providers:\n   308|#   openrouter   (OPENROUTER_API_KEY)  — routes to any model\n   309|#   openai-codex (OAuth — hermes auth) — OpenAI Codex\n   310|#   nous         (OAuth — hermes auth) — Nous Portal\n   311|#   zai          (ZAI_API_KEY)         — Z.AI / GLM\n   312|#   kimi-coding  (KIMI_API_KEY)        — Kimi / Moonshot\n   313|#   kimi-coding-cn (KIMI_CN_API_KEY)   — Kimi / Moonshot (China)\n   314|#   minimax      (MINIMAX_API_KEY)     — MiniMax\n   315|#   minimax-cn   (MINIMAX_CN_API_KEY)  — MiniMax (China)\n   316|#\n   317|# For custom OpenAI-compatible endpoints, add base_url and api_key_env.\n   318|#\n   319|# fallback_model:\n   320|#   provider: openrouter\n   321|#   model: anthropic/claude-sonnet-4\n   322|#\n   323|# ── Smart Model Routing ────────────────────────────────────────────────\n   324|# Optional cheap-vs-strong routing for simple turns.\n   325|# Keeps the primary model for complex work, but can route short/simple\n   326|# messages to a cheaper model across providers.\n   327|#\n   328|# smart_model_routing:\n   329|#   enabled: true\n   330|#   max_simple_chars: 160\n   331|#   max_simple_words: 28\n   332|#   cheap_model:\n   333|#     provider: openrouter\n   334|#     model: google/gemini-2.5-flash\n   335|", "total_lines": 334, "file_size": 9494, "truncated": false, "is_binary": false, "is_image": false}


session_id: 20260414_061325_ff44c4
role: assistant
finish_reason: stop
content:
  我在配置文件中看到：

  **已配置的飞书：**
  - `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23`（对话渠道 ✓）
  - `mcp_servers.feishu.url: http://127.0.0.1:3333/mcp`（MCP 服务 ✓）

  **但缺少凭证：**
  配置文件中没有 `App ID` 和 `App Secret`。你说两次通过飞书发送给我，但我没有在当前配置中看到这些凭证。

  可能的情况：
  1. **凭证发送给了对话 Bot**，但没有被写入配置文件
  2. **MCP 服务器未运行**（因为缺少凭证或服务没启动）

  **请确认：**
  1. 你提供的 App ID 和 App Secret 是什么？（或者重新提供一次）
  2. 还是说飞书**对话**（消息回复）没有响应，而不是 MCP 文档服务？

  这样我才能确定是凭证配置问题，还是 MCP 服务运行问题。

reasoning:
  我看到了配置文件 `~/.hermes/config.yaml`，其中：

  1. `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23` - 飞书对话渠道已配置
  2. `mcp_servers` 下已经有一个 `feishu` 配置，指向 `http://127.0.0.1:3333/mcp`

  所以实际上配置已经有了！用户说提供了两次凭证，但我在配置文件中没有看到 App ID 和 App Secret。让我看看 MCP 服务器配置是否需要凭证。

  看起来 MCP 服务器配置只有 URL，没有凭证。问题可能是：
  1. 凭证没有保存到配置文件
  2. 或者 MCP 服务器没有运行

  用户说"飞书渠道没有响应"，可能是指 MCP 服务（文档/任务管理）没有响应，而不是对话渠道。

  让我检查一下 MCP 服务器的运行状态，以及是否需要配置凭证。

reasoning_details:
  [{"signature": "4dc221bd72ef8d3da3af25338d28f9191e342eca0418c926407dbe17a70a3fa5", "thinking": "\u6211\u770b\u5230\u4e86\u914d\u7f6e\u6587\u4ef6 `~/.hermes/config.yaml`\uff0c\u5176\u4e2d\uff1a\n\n1. `FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23` - \u98de\u4e66\u5bf9\u8bdd\u6e20\u9053\u5df2\u914d\u7f6e\n2. `mcp_servers` \u4e0b\u5df2\u7ecf\u6709\u4e00\u4e2a `feishu` \u914d\u7f6e\uff0c\u6307\u5411 `http://127.0.0.1:3333/mcp`\n\n\u6240\u4ee5\u5b9e\u9645\u4e0a\u914d\u7f6e\u5df2\u7ecf\u6709\u4e86\uff01\u7528\u6237\u8bf4\u63d0\u4f9b\u4e86\u4e24\u6b21\u51ed\u8bc1\uff0c\u4f46\u6211\u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u6ca1\u6709\u770b\u5230 App ID \u548c App Secret\u3002\u8ba9\u6211\u770b\u770b MCP \u670d\u52a1\u5668\u914d\u7f6e\u662f\u5426\u9700\u8981\u51ed\u8bc1\u3002\n\n\u770b\u8d77\u6765 MCP \u670d\u52a1\u5668\u914d\u7f6e\u53ea\u6709 URL\uff0c\u6ca1\u6709\u51ed\u8bc1\u3002\u95ee\u9898\u53ef\u80fd\u662f\uff1a\n1. \u51ed\u8bc1\u6ca1\u6709\u4fdd\u5b58\u5230\u914d\u7f6e\u6587\u4ef6\n2. \u6216\u8005 MCP \u670d\u52a1\u5668\u6ca1\u6709\u8fd0\u884c\n\n\u7528\u6237\u8bf4\"\u98de\u4e66\u6e20\u9053\u6ca1\u6709\u54cd\u5e94\"\uff0c\u53ef\u80fd\u662f\u6307 MCP \u670d\u52a1\uff08\u6587\u6863/\u4efb\u52a1\u7ba1\u7406\uff09\u6ca1\u6709\u54cd\u5e94\uff0c\u800c\u4e0d\u662f\u5bf9\u8bdd\u6e20\u9053\u3002\n\n\u8ba9\u6211\u68c0\u67e5\u4e00\u4e0b MCP \u670d\u52a1\u5668\u7684\u8fd0\u884c\u72b6\u6001\uff0c\u4ee5\u53ca\u662f\u5426\u9700\u8981\u914d\u7f6e\u51ed\u8bc1\u3002", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: stop
content:
  还没有重启，因为我还没有拿到飞书 MCP 的凭证呀 😄

  **目前状态：**
  - ✅ 飞书对话 Bot — 已连接
  - ❌ 飞书 MCP（文档/任务访问）— 还没配置

  **我还在等您给我：**
  - 飞书应用的 **App ID**
  - 飞书应用的 **App Secret**

  您有这些了吗？有了之后我马上帮您配置好！

reasoning:
  The user is asking "重启了吗？" which means "Did it restart?" - they might be asking if the MCP server has been configured/restarted. But I haven't configured anything yet because I'm still waiting for their Feishu App ID and App Secret.

  Let me clarify the status with the user.

reasoning_details:
  [{"signature": "eed11d17158c2facd9bd007cbd1d94c2f082a3900494ba467e7ea1a51c9e477a", "thinking": "The user is asking \"\u91cd\u542f\u4e86\u5417\uff1f\" which means \"Did it restart?\" - they might be asking if the MCP server has been configured/restarted. But I haven't configured anything yet because I'm still waiting for their Feishu App ID and App Secret.\n\nLet me clarify the status with the user.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_us7x8mfyq517_1", "call_id": "call_function_us7x8mfyq517_1", "response_item_id": "fc_function_us7x8mfyq517_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ps aux | grep -i mcp | grep -v grep\"}"}}]

reasoning:
  The user is saying they see "mcp test feishu" running in the CLI and it's been running for a long time without response. Let me check if there are any background processes or MCP servers that are hung.

reasoning_details:
  [{"signature": "609c87cda41ab4b921becedb3b205bd3a372f2702a3f0b36145c38fe7cd02268", "thinking": "The user is saying they see \"mcp test feishu\" running in the CLI and it's been running for a long time without response. Let me check if there are any background processes or MCP servers that are hung.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_us7x8mfyq517_1
content:
  {"output": "root     4056878  0.1  2.2 1198536 83580 ?       Ssl  06:26   0:00 npm exec feishu-mcp --feishu-app-id=cli_a9ba63e5efb81cee --feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM --feishu-auth-type=tenant --enabled-modules=document,task\nroot     4056891  0.3  2.4 11591588 91928 ?      Sl   06:26   0:00 node /root/.hermes/node/bin/feishu-mcp --feishu-app-id=cli_a9ba63e5efb81cee --feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM --feishu-auth-type=tenant --enabled-modules=document,task", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: MCP 进程是在运行的，让我检查一下它的状态：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_sjw2zld8ixyj_1", "call_id": "call_function_sjw2zld8ixyj_1", "response_item_id": "fc_function_sjw2zld8ixyj_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"kill -0 4056878 2>&1 && echo \\\"Process is running\\\" || echo \\\"Process not found\\\"\"}"}}]

reasoning:
  I can see the feishu-mcp process is running. Let me check if it's actually responding or stuck. The process is running but maybe it's not outputting anything. Let me check the logs or try to see what's happening.

reasoning_details:
  [{"signature": "a064c8ac3802354ddfa604b5762ce4990c602b34952df92b282d1e76031a561a", "thinking": "I can see the feishu-mcp process is running. Let me check if it's actually responding or stuck. The process is running but maybe it's not outputting anything. Let me check the logs or try to see what's happening.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
content: 进程是在运行的。让我检查一下连接状态：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_jlz38b29f29w_1", "call_id": "call_function_jlz38b29f29w_1", "response_item_id": "fc_function_jlz38b29f29w_1", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\": \"mcporter\"}"}}]

reasoning:
  The process is running. Let me check what the mcporter skill says about testing. Maybe the user is referring to a `mcp test feishu` command they ran in the CLI. Let me check if there's a test command available, or maybe the user wants me to test the connection.

  Actually, the user might have been running `mcp test feishu` in the Hermes CLI and it seems to hang. Let me try to diagnose the connection.

  Let me check the skill for mcporter to understand how to test the connection.

reasoning_details:
  [{"signature": "adcd865ccbd423a9fb133586a3cd3b3d419564376e522cb515ae88aa0201408c", "thinking": "The process is running. Let me check what the mcporter skill says about testing. Maybe the user is referring to a `mcp test feishu` command they ran in the CLI. Let me check if there's a test command available, or maybe the user wants me to test the connection.\n\nActually, the user might have been running `mcp test feishu` in the Hermes CLI and it seems to hang. Let me try to diagnose the connection.\n\nLet me check the skill for mcporter to understand how to test the connection.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_jlz38b29f29w_1
content:
  {"success": true, "name": "mcporter", "description": "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.", "tags": ["MCP", "Tools", "API", "Integrations", "Interop"], "related_skills": [], "content": "---\nname: mcporter\ndescription: 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.\nversion: 1.0.0\nauthor: community\nlicense: MIT\nmetadata:\n  hermes:\n    tags: [MCP, Tools, API, Integrations, Interop]\n    homepage: https://mcporter.dev\nprerequisites:\n  commands: [npx]\n---\n\n# mcporter\n\nUse `mcporter` to discover, call, and manage [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) servers and tools directly from the terminal.\n\n## Prerequisites\n\nRequires Node.js:\n```bash\n# No install needed (runs via npx)\nnpx mcporter list\n\n# Or install globally\nnpm install -g mcporter\n```\n\n## Quick Start\n\n```bash\n# List MCP servers already configured on this machine\nmcporter list\n\n# List tools for a specific server with schema details\nmcporter list <server> --schema\n\n# Call a tool\nmcporter call <server.tool> key=value\n```\n\n## Discovering MCP Servers\n\nmcporter auto-discovers servers configured by other MCP clients (Claude Desktop, Cursor, etc.) on the machine. To find new servers to use, browse registries like [mcpfinder.dev](https://mcpfinder.dev) or [mcp.so](https://mcp.so), then connect ad-hoc:\n\n```bash\n# Connect to any MCP server by URL (no config needed)\nmcporter list --http-url https://some-mcp-server.com --name my_server\n\n# Or run a stdio server on the fly\nmcporter list --stdio \"npx -y @modelcontextprotocol/server-filesystem\" --name fs\n```\n\n## Calling Tools\n\n```bash\n# Key=value syntax\nmcporter call linear.list_issues team=ENG limit:5\n\n# Function syntax\nmcporter call \"linear.create_issue(title: \\\"Bug fix needed\\\")\"\n\n# Ad-hoc HTTP server (no config needed)\nmcporter call https://api.example.com/mcp.fetch url=https://example.com\n\n# Ad-hoc stdio server\nmcporter call --stdio \"bun run ./server.ts\" scrape url=https://example.com\n\n# JSON payload\nmcporter call <server.tool> --args '{\"limit\": 5}'\n\n# Machine-readable output (recommended for Hermes)\nmcporter call <server.tool> key=value --output json\n```\n\n## Auth and Config\n\n```bash\n# OAuth login for a server\nmcporter auth <server | url> [--reset]\n\n# Manage config\nmcporter config list\nmcporter config get <key>\nmcporter config add <server>\nmcporter config remove <server>\nmcporter config import <path>\n```\n\nConfig file location: `./config/mcporter.json` (override with `--config`).\n\n## Daemon\n\nFor persistent server connections:\n```bash\nmcporter daemon start\nmcporter daemon status\nmcporter daemon stop\nmcporter daemon restart\n```\n\n## Code Generation\n\n```bash\n# Generate a CLI wrapper for an MCP server\nmcporter generate-cli --server <name>\nmcporter generate-cli --command <url>\n\n# Inspect a generated CLI\nmcporter inspect-cli <path> [--json]\n\n# Generate TypeScript types/client\nmcporter emit-ts <server> --mode client\nmcporter emit-ts <server> --mode types\n```\n\n## Notes\n\n- Use `--output json` for structured output that's easier to parse\n- Ad-hoc servers (HTTP URL or `--stdio` command) work without any config — useful for one-off calls\n- OAuth auth may require interactive browser flow — use `terminal(command=\"mcporter auth <server>\", pty=true)` if needed\n", "path": "mcp/mcporter/SKILL.md", "linked_files": null, "usage_hint": null, "required_environment_variables": [], "required_commands": [], "missing_required_environment_variables": [], "missing_credential_files": [], "missing_required_commands": [], "setup_needed": false, "setup_skipped": false, "readiness_status": "available", "metadata": {"hermes": {"tags": ["MCP", "Tools", "API", "Integrations", "Interop"], "homepage": "https://mcporter.dev"}}}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 让我检查一下 MCP 服务器的状态：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_0cc75gxxm1st_1", "call_id": "call_function_0cc75gxxm1st_1", "response_item_id": "fc_function_0cc75gxxm1st_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"mcporter list 2>&1\"}"}}]

reasoning:
  The user says the MCP test has been running for a long time without response. Let me try to diagnose the issue. I can see from the process list that there's a feishu-mcp process running with the correct app-id and secret. But it seems to be hanging.

  Let me try to:
  1. Kill the existing process
  2. Check if there's an issue with the feishu-mcp server startup
  3. Try running it manually to see the output

  Actually, let me check the mcporter to see if it can detect what's happening with the feishu MCP server.

reasoning_details:
  [{"signature": "12e69b624648097332042a091c787d172497f528e4049446bada847ea1ec42ec", "thinking": "The user says the MCP test has been running for a long time without response. Let me try to diagnose the issue. I can see from the process list that there's a feishu-mcp process running with the correct app-id and secret. But it seems to be hanging.\n\nLet me try to:\n1. Kill the existing process\n2. Check if there's an issue with the feishu-mcp server startup\n3. Try running it manually to see the output\n\nActually, let me check the mcporter to see if it can detect what's happening with the feishu MCP server.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_oqx2xjcn82eq_1", "call_id": "call_function_oqx2xjcn82eq_1", "response_item_id": "fc_function_oqx2xjcn82eq_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"which mcp 2>/dev/null || echo \\\"mcp not found\\\"\"}"}}]

reasoning:
  mcporter is not installed. Let me try using the native MCP client instead. Let me check if the feishu MCP server is working by trying to connect to it. Let me look at what the `mcp` command can do.

reasoning_details:
  [{"signature": "0e43e16cc63bbb6d14779f7c78801a713ec3015f294c6d5c5c8a616583a616d7", "thinking": "mcporter is not installed. Let me try using the native MCP client instead. Let me check if the feishu MCP server is working by trying to connect to it. Let me look at what the `mcp` command can do.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_hctz4wjtfn26_1", "call_id": "call_function_hctz4wjtfn26_1", "response_item_id": "fc_function_hctz4wjtfn26_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"mcp list 2>&1\"}"}}]

reasoning:
  MCP is available. Let me try to list MCP servers or check if the Feishu MCP is connected. Let me also check the hermes config to see if feishu-mcp is properly configured.

reasoning_details:
  [{"signature": "66fff912622fd41918663a21f9792b3ccacf60ad304d6719668996a0393a9e92", "thinking": "MCP is available. Let me try to list MCP servers or check if the Feishu MCP is connected. Let me also check the hermes config to see if feishu-mcp is properly configured.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_hctz4wjtfn26_1
content:
  {"output": "Usage: mcp [OPTIONS] COMMAND [ARGS]...\nTry 'mcp --help' for help.\n╭─ Error ──────────────────────────────────────────────────────────────────────╮\n│ No such command 'list'.                                                      │\n╰──────────────────────────────────────────────────────────────────────────────╯", "exit_code": 2, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_v28l6ox0t2e1_1", "call_id": "call_function_v28l6ox0t2e1_1", "response_item_id": "fc_function_v28l6ox0t2e1_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"mcp --help 2>&1\"}"}}]

reasoning:
  Let me check what commands mcp has and also try to see the status of the feishu MCP server that was started.

reasoning_details:
  [{"signature": "ae916d095db6ae2047bfea6ed02ad51f92882680cd2ad0cfb03db0dd1970c354", "thinking": "Let me check what commands mcp has and also try to see the status of the feishu MCP server that was started.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_v28l6ox0t2e1_1
content:
  {"output": "Usage: mcp [OPTIONS] COMMAND [ARGS]...                                         \n                                                                                \n MCP development tools                                                          \n                                                                                \n╭─ Options ────────────────────────────────────────────────────────────────────╮\n│ --help          Show this message and exit.                                  │\n╰──────────────────────────────────────────────────────────────────────────────╯\n╭─ Commands ───────────────────────────────────────────────────────────────────╮\n│ version  Show the MCP version.                                               │\n│ dev      Run an MCP server with the MCP Inspector.                           │\n│ run      Run an MCP server.                                                  │\n│ install  Install an MCP server in the Claude desktop app.                    │\n╰──────────────────────────────────────────────────────────────────────────────╯", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 让我检查一下飞书 MCP 服务器是否正常工作：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_i3lotfssqdsd_1", "call_id": "call_function_i3lotfssqdsd_1", "response_item_id": "fc_function_i3lotfssqdsd_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s http://localhost:3333/health 2>&1 || echo \\\"No health endpoint\\\"\"}"}}]

reasoning:
  The `mcp` CLI here is a development tool for MCP servers, not a client to list/manage MCP connections. 

  Looking at the running process, it seems the Feishu MCP server IS running (via npm/npx feishu-mcp). But this is the server itself, not connected to Hermes as an MCP client.

  Let me check how Hermes is configured to connect to MCP servers. Looking at the config.yaml I saw earlier, there's no `mcp_servers` section configured. The Feishu MCP server was probably started manually via the CLI.

  Let me check if the Feishu MCP server is actually working properly by looking at its output or checking if it can respond to requests.

reasoning_details:
  [{"signature": "2915c8c12f4282b84440123b27670536a97043a12756e6839e542d939a601dc0", "thinking": "The `mcp` CLI here is a development tool for MCP servers, not a client to list/manage MCP connections. \n\nLooking at the running process, it seems the Feishu MCP server IS running (via npm/npx feishu-mcp). But this is the server itself, not connected to Hermes as an MCP client.\n\nLet me check how Hermes is configured to connect to MCP servers. Looking at the config.yaml I saw earlier, there's no `mcp_servers` section configured. The Feishu MCP server was probably started manually via the CLI.\n\nLet me check if the Feishu MCP server is actually working properly by looking at its output or checking if it can respond to requests.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_i3lotfssqdsd_1
content:
  {"output": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"utf-8\">\n<title>Error</title>\n</head>\n<body>\n<pre>Cannot GET /health</pre>\n</body>\n</html>", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 服务器在 3333 端口运行，但没有 /health 端点。让我试试调用一个简单的方法来验证连接：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_89f2i3tg57oa_1", "call_id": "call_function_89f2i3tg57oa_1", "response_item_id": "fc_function_89f2i3tg57oa_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s -X POST http://localhost:3333/mcp -H \\\"Content-Type: application/json\\\" -d '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"tools/list\\\",\\\"id\\\":1}' 2>&1 | head -100\"}"}}]

reasoning:
  The feishu MCP server is running on port 3333. But it doesn't have a /health endpoint. Let me check the logs or see what the feishu MCP server supports. Maybe I should look at the npm process output.

  Let me check if the feishu-mcp npm package has a way to check status, or maybe try to call a simple tool to see if it works.

reasoning_details:
  [{"signature": "e4dd9622449d6f03311c592da52bb0811cfa0e493e90b11b439556ff7df8a45c", "thinking": "The feishu MCP server is running on port 3333. But it doesn't have a /health endpoint. Let me check the logs or see what the feishu MCP server supports. Maybe I should look at the npm process output.\n\nLet me check if the feishu-mcp npm package has a way to check status, or maybe try to call a simple tool to see if it works.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_89f2i3tg57oa_1
content:
  {"output": "{\"jsonrpc\":\"2.0\",\"error\":{\"code\":-32000,\"message\":\"Bad Request: No valid session ID provided\"},\"id\":null}", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 服务器在运行！让我用正确的方式测试一下 MCP 连接：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_608nzof0ghv6_1", "call_id": "call_function_608nzof0ghv6_1", "response_item_id": "fc_function_608nzof0ghv6_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"echo '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"initialize\\\",\\\"params\\\":{\\\"protocolVersion\\\":\\\"2024-11-05\\\",\\\"capabilities\\\":{},\\\"clientInfo\\\":{\\\"name\\\":\\\"test\\\",\\\"version\\\":\\\"1.0\\\"}},\\\"id\\\":0}' | timeout 5 node -e \\\"\\nconst { spawn } = require('child_process');\\nconst mcpserver = spawn('/root/.hermes/node/bin/feishu-mcp', [\\n  '--feishu-app-id=cli_a9ba63e5efb81cee',\\n  '--feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM',\\n  '--feishu-auth-type=tenant',\\n  '--enabled-modules=document,task'\\n], { stdio: ['pipe', 'pipe', 'pipe'] });\\n\\nlet output = '';\\nmcpserver.stdout.on('data', (data) => {\\n  output += data.toString();\\n  console.log('STDOUT:', data.toString());\\n});\\nmcpserver.stderr.on('data', (data) => {\\n  console.log('STDERR:', data.toString());\\n});\\nsetTimeout(() => { mcpserver.kill(); process.exit(0); }, 5000);\\n\\\" 2>&1 | head -50\"}"}}]

reasoning:
  OK so the server is running but it needs a session. The MCP protocol works over stdio or with proper session handling. The process is running, so the MCP server itself is operational. 

  The issue the user was seeing was likely just a long-running process in the CLI that was waiting for something. Since the MCP server IS running and responding (even if with an error about session ID), it seems to be working.

  Let me try to test the MCP server properly. Actually, the MCP server from cso1z/Feishu-MCP works over stdio when called as an MCP server. Let me try to test it properly.

  Actually, since the process is running and responding, maybe the user's CLI test just timed out or something. The MCP server process seems to be working fine.

  Let me try to call a tool using the MCP protocol properly to see if it works.

reasoning_details:
  [{"signature": "5180f05a9ec1f80ebfc964a98323e368ec44fa2c2e66563b8dba6664c2c3ae2d", "thinking": "OK so the server is running but it needs a session. The MCP protocol works over stdio or with proper session handling. The process is running, so the MCP server itself is operational. \n\nThe issue the user was seeing was likely just a long-running process in the CLI that was waiting for something. Since the MCP server IS running and responding (even if with an error about session ID), it seems to be working.\n\nLet me try to test the MCP server properly. Actually, the MCP server from cso1z/Feishu-MCP works over stdio when called as an MCP server. Let me try to test it properly.\n\nActually, since the process is running and responding, maybe the user's CLI test just timed out or something. The MCP server process seems to be working fine.\n\nLet me try to call a tool using the MCP protocol properly to see if it works.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_608nzof0ghv6_1
content:
  {"output": "STDOUT: [2026-04-14 06:30:50.653] [INFO ] [模块] 配置: document, task → 实际加载: document\n\nSTDOUT: [2026-04-14 06:30:50.654] [INFO ] 应用版本: 0.3.2\n[2026-04-14 06:30:50.654] [INFO ] 当前配置:\n[2026-04-14 06:30:50.654] [INFO ] 服务器配置:\n[2026-04-14 06:30:50.654] [INFO ] - 端口: 3333 (来源: default)\n[2026-04-14 06:30:50.654] [INFO ] 飞书配置:\n[2026-04-14 06:30:50.654] [INFO ] - App ID: cl****ee (来源: cli)\n[2026-04-14 06:30:50.654] [INFO ] - App Secret: nd****WM (来源: cli)\n[2026-04-14 06:30:50.654] [INFO ] - API URL: https://open.feishu.cn/open-apis (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - 认证类型: tenant (来源: cli)\n[2026-04-14 06:30:50.655] [INFO ] - 启用权限检查: true (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - User Key: stdio (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] 日志配置:\n[2026-04-14 06:30:50.655] [INFO ] - 日志级别: INFO (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - 显示时间戳: true (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - 显示日志级别: true (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] 缓存配置:\n[2026-04-14 06:30:50.655] [INFO ] - 启用缓存: true (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - 缓存TTL: 300秒 (来源: default)\n[2026-04-14 06:30:50.655] [INFO ] - 最大缓存条目: 100 (来源: default)\n[2026-04-14 06:30:50.656] [INFO ] Token缓存目录: /root/.cache/feishu-mcp\n\nSTDERR: [2026-04-14 06:30:50.654] [WARN ] [模块] task、calendar、member 需 user 认证，当前为 tenant 模式已跳过。设置 FEISHU_AUTH_TYPE=*** 以启用。\n\nSTDOUT: [2026-04-14 06:30:50.656] [DEBUG] Token 缓存已迁移过，跳过本次迁移\n[2026-04-14 06:30:50.656] [INFO ] 用户token缓存文件不存在，将创建新的缓存\n[2026-04-14 06:30:50.656] [INFO ] Scope版本缓存文件不存在，将创建新的缓存\n[2026-04-14 06:30:50.656] [INFO ] Token缓存清理定时器已启动，每5分钟执行一次\n[2026-04-14 06:30:50.656] [INFO ] Token刷新管理器已初始化\n[2026-04-14 06:30:50.656] [INFO ] 启动Token自动刷新管理器，检查间隔: 300秒\n\nSTDOUT: [2026-04-14 06:30:50.656] [DEBUG] 开始检查需要刷新的token\n\nSTDOUT: [2026-04-14 06:30:50.657] [DEBUG] 获取到 0 个用户token keys\n[2026-04-14 06:30:50.657] [DEBUG] Token刷新检查完成: 检查0个，无需刷新\n[2026-04-14 06:30:50.657] [INFO ] Token自动刷新管理器已启动\n[2026-04-14 06:30:50.657] [INFO ] Token自动刷新管理器已在服务器启动时初始化\n[2026-04-14 06:30:50.657] [INFO ] Initializing Feishu MCP Server in HTTP mode on port 3333...\n\nSTDERR: node:events:497\n      throw er; // Unhandled 'error' event\n      ^\n\nError: listen EADDRINUSE: address already in use 0.0.0.0:3333\n    at Server.setupListenHandle [as _listen2] (node:net:1940:16)\n    at listenInCluster (node:net:1997:12)\n    at node:net:2206:7\n    at process.processTicksAndRejections (node:internal/process/task_queues:89:21)\nEmitted 'error' event on Server instance at:", "exit_code": 0, "error": null, "approval": "Command required approval (script execution via -e/-c flag) and was approved by the user."}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 我看到问题了！飞书 MCP 服务器已经在 3333 端口运行了（您之前启动的进程还在运行）。让我检查一下它是否正常工作：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_1ey47hq5am7l_1", "call_id": "call_function_1ey47hq5am7l_1", "response_item_id": "fc_function_1ey47hq5am7l_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s -X POST http://localhost:3333/ -H \\\"Content-Type: application/json\\\" -d '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"tools/list\\\",\\\"params\\\":{},\\\"id\\\":1}' 2>&1\"}"}}]

reasoning:
  I see the issue - the MCP server is trying to start in HTTP mode on port 3333, but port 3333 is already in use by the previous running process. So there's already a feishu-mcp server running from a previous command.

  Let me check the running processes more carefully and see what's happening with the existing feishu-mcp.

reasoning_details:
  [{"signature": "67c055559f509cf0da72ae26047d0d47889825849631a61bed70ffc17f409ead", "thinking": "I see the issue - the MCP server is trying to start in HTTP mode on port 3333, but port 3333 is already in use by the previous running process. So there's already a feishu-mcp server running from a previous command.\n\nLet me check the running processes more carefully and see what's happening with the existing feishu-mcp.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_1ey47hq5am7l_1
content:
  {"output": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"utf-8\">\n<title>Error</title>\n</head>\n<body>\n<pre>Cannot POST /</pre>\n</body>\n</html>", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
reasoning: Let me check the correct endpoint for the MCP server. It might be /mcp or some other path.
tool_calls:
  [{"id": "call_function_wsc4y79nu2zw_1", "call_id": "call_function_wsc4y79nu2zw_1", "response_item_id": "fc_function_wsc4y79nu2zw_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s -X POST http://localhost:3333/mcp -H \\\"Content-Type: application/json\\\" -d '{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"tools/list\\\",\\\"params\\\":{},\\\"id\\\":1}' 2>&1\"}"}}]

reasoning_details:
  [{"signature": "f31724b3abc2c1de888cd8a44550512deb6abf116a606a645b700a7ee19af5a5", "thinking": "Let me check the correct endpoint for the MCP server. It might be /mcp or some other path.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: assistant
content: 让我用 stdio 模式测试一下 MCP 工具是否正常工作：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_ob4y6ahrjlj6_1", "call_id": "call_function_ob4y6ahrjlj6_1", "response_item_id": "fc_function_ob4y6ahrjlj6_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"timeout 10 /root/.hermes/node/bin/feishu-mcp --feishu-app-id=cli_a9ba63e5efb81cee --feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM --feishu-auth-type=tenant --enabled-modules=document,task 2>&1 <<'EOF'\\n{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"initialize\\\",\\\"params\\\":{\\\"protocolVersion\\\":\\\"2024-11-05\\\",\\\"capabilities\\\":{},\\\"clientInfo\\\":{\\\"name\\\":\\\"test\\\",\\\"version\\\":\\\"1.0\\\"}},\\\"id\\\":0}\\n{\\\"jsonrpc\\\":\\\"2.0\\\",\\\"method\\\":\\\"tools/list\\\",\\\"params\\\":{},\\\"id\\\":1}\\nEOF\"}"}}]

reasoning:
  The server is running but it's a streaming HTTP MCP server. The issue is that the MCP protocol uses a specific handshake flow, not just a simple POST request. Let me try to call it using stdio mode instead to verify the tools are working.

  Actually, let me first check if the existing running process is actually working correctly. Let me see if there are any recent logs or if the MCP server is properly initialized.

reasoning_details:
  [{"signature": "7d257c24a273f3e59a7d2b08e553b5c4d1db6ddc5338218d58687c5fdcd1e38e", "thinking": "The server is running but it's a streaming HTTP MCP server. The issue is that the MCP protocol uses a specific handshake flow, not just a simple POST request. Let me try to call it using stdio mode instead to verify the tools are working.\n\nActually, let me first check if the existing running process is actually working correctly. Let me see if there are any recent logs or if the MCP server is properly initialized.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_ob4y6ahrjlj6_1
content:
  {"output": "[2026-04-14 06:31:09.899] [INFO ] [模块] 配置: document, task → 实际加载: document\n[2026-04-14 06:31:09.900] [WARN ] [模块] task、calendar、member 需 user 认证，当前为 tenant 模式已跳过。设置 FEISHU_AUTH_TYPE=*** 以启用。\n[2026-04-14 06:31:09.900] [INFO ] 应用版本: 0.3.2\n[2026-04-14 06:31:09.900] [INFO ] 当前配置:\n[2026-04-14 06:31:09.900] [INFO ] 服务器配置:\n[2026-04-14 06:31:09.900] [INFO ] - 端口: 3333 (来源: default)\n[2026-04-14 06:31:09.900] [INFO ] 飞书配置:\n[2026-04-14 06:31:09.901] [INFO ] - App ID: cl****ee (来源: cli)\n[2026-04-14 06:31:09.901] [INFO ] - App Secret: nd****WM (来源: cli)\n[2026-04-14 06:31:09.901] [INFO ] - API URL: https://open.feishu.cn/open-apis (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - 认证类型: tenant (来源: cli)\n[2026-04-14 06:31:09.901] [INFO ] - 启用权限检查: true (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - User Key: stdio (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] 日志配置:\n[2026-04-14 06:31:09.901] [INFO ] - 日志级别: INFO (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - 显示时间戳: true (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - 显示日志级别: true (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] 缓存配置:\n[2026-04-14 06:31:09.901] [INFO ] - 启用缓存: true (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - 缓存TTL: 300秒 (来源: default)\n[2026-04-14 06:31:09.901] [INFO ] - 最大缓存条目: 100 (来源: default)\n[2026-04-14 06:31:09.902] [INFO ] Token缓存目录: /root/.cache/feishu-mcp\n[2026-04-14 06:31:09.903] [DEBUG] Token 缓存已迁移过，跳过本次迁移\n[2026-04-14 06:31:09.903] [INFO ] 用户token缓存文件不存在，将创建新的缓存\n[2026-04-14 06:31:09.903] [INFO ] Scope版本缓存文件不存在，将创建新的缓存\n[2026-04-14 06:31:09.903] [INFO ] Token缓存清理定时器已启动，每5分钟执行一次\n[2026-04-14 06:31:09.903] [INFO ] Token刷新管理器已初始化\n[2026-04-14 06:31:09.903] [INFO ] 启动Token自动刷新管理器，检查间隔: 300秒\n[2026-04-14 06:31:09.903] [DEBUG] 开始检查需要刷新的token\n[2026-04-14 06:31:09.903] [DEBUG] 获取到 0 个用户token keys\n[2026-04-14 06:31:09.903] [DEBUG] Token刷新检查完成: 检查0个，无需刷新\n[2026-04-14 06:31:09.904] [INFO ] Token自动刷新管理器已启动\n[2026-04-14 06:31:09.904] [INFO ] Token自动刷新管理器已在服务器启动时初始化\n[2026-04-14 06:31:09.904] [INFO ] Initializing Feishu MCP Server in HTTP mode on port 3333...\n[2026-04-14 06:31:09.914] [INFO ] HTTP server listening on port 3333\n[2026-04-14 06:31:09.914] [INFO ] SSE endpoint available at http://localhost:3333/sse\n[2026-04-14 06:31:09.914] [INFO ] Message endpoint available at http://localhost:3333/messages\n[2026-04-14 06:31:09.914] [INFO ] StreamableHTTP endpoint available at http://localhost:3333/mcp", "exit_code": 124, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 我看到飞书 MCP 服务器正在运行（HTTP 模式，端口 3333）。让我检查一下 Hermes Agent 的 MCP 连接状态：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_6dtfj8f1q7dy_1", "call_id": "call_function_6dtfj8f1q7dy_1", "response_item_id": "fc_function_6dtfj8f1q7dy_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cat ~/.hermes/config.yaml | grep -A 20 \\\"mcp_servers\\\" 2>/dev/null || echo \\\"No mcp_servers configured\\\"\"}"}}]

reasoning:
  Interesting! When I run the feishu-mcp command directly without the `--stdio` flag, it starts in HTTP mode on port 3333. The STDIO mode might be different. But even so, the server seems to start fine in HTTP mode.

  The issue is that it timed out before responding to the JSON-RPC messages I sent. The server was just starting up and the stdin/stdout communication didn't work as expected in HTTP mode.

  Let me try a different approach - use the HTTP endpoint properly. The MCP server is running on port 3333 with the StreamableHTTP endpoint at `/mcp`. Let me try to connect to it properly.

  Actually wait - when I ran `mcporter list` it said "command not found". The mcporter CLI is not installed. Let me check how Hermes Agent's native MCP client is supposed to connect to this.

  Looking at the native-mcp skill, it seems Hermes should be able to connect to MCP servers via stdio or HTTP. But I need to configure it properly in the config.yaml.

  Actually, let me re-read what the user said: "我在CLI看到你执行mcp test feishu已经很长时间，没响应"

  The user is saying they saw me execute "mcp test feishu" in the CLI and it's been running for a long time with no response.

  I haven't actually executed any "mcp test feishu" command in this session - I was running other commands. But maybe the MCP server is just running and waiting for connections?

  Let me check if the MCP server is properly connected to Hermes Agent. The issue might be that Hermes Agent needs to be configured to use the MCP server.

  Let me look at how to configure the Feishu MCP server in Hermes Agent's config.yaml. The feishu-mcp server needs to be added to the `mcp_servers` section.


  I should verify whether Hermes Agent is actually connected to the feishu-mcp server. The native-mcp skill shows how to configure external MCP servers, but the feishu-mcp isn't listed there. I could try running the server in stdio mode instead of HTTP to see if that works better, or I need to check if there's a configuration I'm missing to get Hermes Agent communicating with the MCP server properly.

  Looking at the logs, feishu-mcp is definitely running as an npm process. The user mentioned seeing "mcp test feishu" in the CLI - this might be from Hermes Agent's native MCP discovery feature, but the feishu-mcp server appears to be configured for HTTP transport on port 3333 rather than stdio. I should verify what MCP servers are actually registered with Hermes Agent right now.

reasoning_details:
  [{"signature": "67d48a5e3dc97bdefac8d19beb1127ce27f17258b91b0b4ab6bc36a54201608e", "thinking": "Interesting! When I run the feishu-mcp command directly without the `--stdio` flag, it starts in HTTP mode on port 3333. The STDIO mode might be different. But even so, the server seems to start fine in HTTP mode.\n\nThe issue is that it timed out before responding to the JSON-RPC messages I sent. The server was just starting up and the stdin/stdout communication didn't work as expected in HTTP mode.\n\nLet me try a different approach - use the HTTP endpoint properly. The MCP server is running on port 3333 with the StreamableHTTP endpoint at `/mcp`. Let me try to connect to it properly.\n\nActually wait - when I ran `mcporter list` it said \"command not found\". The mcporter CLI is not installed. Let me check how Hermes Agent's native MCP client is supposed to connect to this.\n\nLooking at the native-mcp skill, it seems Hermes should be able to connect to MCP servers via stdio or HTTP. But I need to configure it properly in the config.yaml.\n\nActually, let me re-read what the user said: \"\u6211\u5728CLI\u770b\u5230\u4f60\u6267\u884cmcp test feishu\u5df2\u7ecf\u5f88\u957f\u65f6\u95f4\uff0c\u6ca1\u54cd\u5e94\"\n\nThe user is saying they saw me execute \"mcp test feishu\" in the CLI and it's been running for a long time with no response.\n\nI haven't actually executed any \"mcp test feishu\" command in this session - I was running other commands. But maybe the MCP server is just running and waiting for connections?\n\nLet me check if the MCP server is properly connected to Hermes Agent. The issue might be that Hermes Agent needs to be configured to use the MCP server.\n\nLet me look at how to configure the Feishu MCP server in Hermes Agent's config.yaml. The feishu-mcp server needs to be added to the `mcp_servers` section.\n\n\nI should verify whether Hermes Agent is actually connected to the feishu-mcp server. The native-mcp skill shows how to configure external MCP servers, but the feishu-mcp isn't listed there. I could try running the server in stdio mode instead of HTTP to see if that works better, or I need to check if there's a configuration I'm missing to get Hermes Agent communicating with the MCP server properly.\n\nLooking at the logs, feishu-mcp is definitely running as an npm process. The user mentioned seeing \"mcp test feishu\" in the CLI - this might be from Hermes Agent's native MCP discovery feature, but the feishu-mcp server appears to be configured for HTTP transport on port 3333 rather than stdio. I should verify what MCP servers are actually registered with Hermes Agent right now.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_6dtfj8f1q7dy_1
content:
  {"output": "mcp_servers:\n  feishu:\n    command: npx\n    args:\n    - \"-y\"\n    - \"feishu-mcp\"\n    - \"--log-level=none\"\n    - \"--feishu-app-id=cli_a9ba63e5efb81cee\"\n    - \"--feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM\"\n    - \"--feishu-auth-type=tenant\"\n    - \"--enabled-modules=document,task\"\n    timeout: 120\n    connect_timeout: 60\n\n# ── Fallback Model ────────────────────────────────────────────────────\n# Automatic provider failover when primary is unavailable.\n# Uncomment and configure to enable. Triggers on rate limits (429),\n# overload (529), service errors (503), or connection failures.\n#\n# Supported providers:\n#   openrouter   (OPENROUTER_API_KEY)  — routes to any model", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
content:
  我看到配置了飞书 MCP，但问题是 **feishu-mcp 默认启动 HTTP 模式**，而 Hermes Agent 期望的是 **stdio 模式**！

  让我检查一下如何让 feishu-mcp 以 stdio 模式运行：

tool_calls:
  [{"id": "call_function_ln0qrdze10dx_1", "call_id": "call_function_ln0qrdze10dx_1", "response_item_id": "fc_function_ln0qrdze10dx_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"/root/.hermes/node/bin/feishu-mcp --help 2>&1\"}"}}]

reasoning:
  我看到问题了！配置文件中配置了飞书 MCP 使用 `npx` 运行 feishu-mcp，但是这个命令默认会启动 HTTP 模式服务器。而 Hermes Agent 的 native-mcp 可能期望的是 stdio 模式的 MCP 服务器。

  让我检查一下 feishu-mcp 是否有 stdio 模式。

reasoning_details:
  [{"signature": "6baa1bb2f9d4813e7608acc913c8bc7b6ae0b1af6619609a5fd00268c890ca80", "thinking": "\u6211\u770b\u5230\u95ee\u9898\u4e86\uff01\u914d\u7f6e\u6587\u4ef6\u4e2d\u914d\u7f6e\u4e86\u98de\u4e66 MCP \u4f7f\u7528 `npx` \u8fd0\u884c feishu-mcp\uff0c\u4f46\u662f\u8fd9\u4e2a\u547d\u4ee4\u9ed8\u8ba4\u4f1a\u542f\u52a8 HTTP \u6a21\u5f0f\u670d\u52a1\u5668\u3002\u800c Hermes Agent \u7684 native-mcp \u53ef\u80fd\u671f\u671b\u7684\u662f stdio \u6a21\u5f0f\u7684 MCP \u670d\u52a1\u5668\u3002\n\n\u8ba9\u6211\u68c0\u67e5\u4e00\u4e0b feishu-mcp \u662f\u5426\u6709 stdio \u6a21\u5f0f\u3002", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_ln0qrdze10dx_1
content:
  {"output": "Options:\n  --version                  Show version number                       [boolean]\n  --port                     服务器监听端口                                    [number]\n  --log-level                日志级别 (debug, info, log, warn, error, none) [string]\n  --feishu-app-id            飞书应用ID                                     [string]\n  --feishu-app-secret        飞书应用密钥                                     [string]\n  --feishu-base-url          飞书API基础URL                                 [string]\n  --cache-enabled            是否启用缓存                                    [boolean]\n  --cache-ttl                缓存生存时间（秒）                                  [number]\n  --feishu-auth-type         飞书认证类型 (tenant 或 user)                     [string]\n  --feishu-token-endpoint    获取token的接口地址，默认 http://localhost:3333/getToken\n                                                                        [string]\n  --feishu-scope-validation  是否启用权限检查，默认 true                          [boolean]\n  --user-key                 stdio 模式下的用户标识，默认 stdio                    [string]\n  --enabled-modules          启用的功能模块列表（逗号分隔），可选值: document,task,calendar 或 all，默\n                             认 document                                 [string]\n  --help                     Show help                                 [boolean]", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
content: 飞书 MCP 是 HTTP 模式运行的，但 Hermes Agent 期望 stdio 模式。让我更新配置使用 HTTP 连接：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_etu7gg1rrfy8_1", "call_id": "call_function_etu7gg1rrfy8_1", "response_item_id": "fc_function_etu7gg1rrfy8_1", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/root/.hermes/config.yaml\", \"offset\": 1, \"limit\": 50}"}}]

reasoning:
  我看到飞书 MCP 没有直接的 stdio 模式选项。它默认启动 HTTP 服务器。但我注意到 Hermes Agent 配置了 `command: npx` 和 `args`，这应该会通过 npx 运行 feishu-mcp。

  问题是 Hermes Agent 期望 MCP 服务器通过 stdin/stdout 进行通信（stdio 模式），但 feishu-mcp 默认启动 HTTP 服务器。

  让我看看有没有什么方法可以让 feishu-mcp 以 stdio 模式运行，或者使用 HTTP 传输模式连接到现有的 HTTP 服务器。

  从 mcporter 的文档中，我看到它支持 HTTP 传输模式。所以我应该配置 Hermes Agent 使用 HTTP 连接到已经在 3333 端口运行的 feishu-mcp 服务器。

  但是，我需要先检查 Hermes Agent 的 MCP 配置是否支持 HTTP 模式。根据 native-mcp skill 的文档，是支持的：

  ```yaml
  mcp_servers:
    server_name:
      url: "https://my-server.example.com/mcp"   # (required) server URL
      headers:                                     # (optional) HTTP headers
        Authorization: "Bearer sk-..."
      timeout: 180               # (optional) per-tool-call timeout in seconds, default: 120
      connect_timeout: 60        # (optional) initial connection timeout in seconds, default: 60
  ```

  所以我可以更新配置使用 HTTP 模式。但首先，让我确认一下 Hermes Agent 是否已经连接到飞书 MCP。

  让我检查 Hermes Agent 的日志或状态。

reasoning_details:
  [{"signature": "6e11f023633868a1cffa26dfb7fc65a0c2ef59abf3327b4873e45cf3f4db9e71", "thinking": "\u6211\u770b\u5230\u98de\u4e66 MCP \u6ca1\u6709\u76f4\u63a5\u7684 stdio \u6a21\u5f0f\u9009\u9879\u3002\u5b83\u9ed8\u8ba4\u542f\u52a8 HTTP \u670d\u52a1\u5668\u3002\u4f46\u6211\u6ce8\u610f\u5230 Hermes Agent \u914d\u7f6e\u4e86 `command: npx` \u548c `args`\uff0c\u8fd9\u5e94\u8be5\u4f1a\u901a\u8fc7 npx \u8fd0\u884c feishu-mcp\u3002\n\n\u95ee\u9898\u662f Hermes Agent \u671f\u671b MCP \u670d\u52a1\u5668\u901a\u8fc7 stdin/stdout \u8fdb\u884c\u901a\u4fe1\uff08stdio \u6a21\u5f0f\uff09\uff0c\u4f46 feishu-mcp \u9ed8\u8ba4\u542f\u52a8 HTTP \u670d\u52a1\u5668\u3002\n\n\u8ba9\u6211\u770b\u770b\u6709\u6ca1\u6709\u4ec0\u4e48\u65b9\u6cd5\u53ef\u4ee5\u8ba9 feishu-mcp \u4ee5 stdio \u6a21\u5f0f\u8fd0\u884c\uff0c\u6216\u8005\u4f7f\u7528 HTTP \u4f20\u8f93\u6a21\u5f0f\u8fde\u63a5\u5230\u73b0\u6709\u7684 HTTP \u670d\u52a1\u5668\u3002\n\n\u4ece mcporter \u7684\u6587\u6863\u4e2d\uff0c\u6211\u770b\u5230\u5b83\u652f\u6301 HTTP \u4f20\u8f93\u6a21\u5f0f\u3002\u6240\u4ee5\u6211\u5e94\u8be5\u914d\u7f6e Hermes Agent \u4f7f\u7528 HTTP \u8fde\u63a5\u5230\u5df2\u7ecf\u5728 3333 \u7aef\u53e3\u8fd0\u884c\u7684 feishu-mcp \u670d\u52a1\u5668\u3002\n\n\u4f46\u662f\uff0c\u6211\u9700\u8981\u5148\u68c0\u67e5 Hermes Agent \u7684 MCP \u914d\u7f6e\u662f\u5426\u652f\u6301 HTTP \u6a21\u5f0f\u3002\u6839\u636e native-mcp skill \u7684\u6587\u6863\uff0c\u662f\u652f\u6301\u7684\uff1a\n\n```yaml\nmcp_servers:\n  server_name:\n    url: \"https://my-server.example.com/mcp\"   # (required) server URL\n    headers:                                     # (optional) HTTP headers\n      Authorization: \"Bearer sk-...\"\n    timeout: 180               # (optional) per-tool-call timeout in seconds, default: 120\n    connect_timeout: 60        # (optional) initial connection timeout in seconds, default: 60\n```\n\n\u6240\u4ee5\u6211\u53ef\u4ee5\u66f4\u65b0\u914d\u7f6e\u4f7f\u7528 HTTP \u6a21\u5f0f\u3002\u4f46\u9996\u5148\uff0c\u8ba9\u6211\u786e\u8ba4\u4e00\u4e0b Hermes Agent \u662f\u5426\u5df2\u7ecf\u8fde\u63a5\u5230\u98de\u4e66 MCP\u3002\n\n\u8ba9\u6211\u68c0\u67e5 Hermes Agent \u7684\u65e5\u5fd7\u6216\u72b6\u6001\u3002", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_etu7gg1rrfy8_1
content:
  {"content": "     1|model:\n     2|  default: MiniMax-M2.7-highspeed\n     3|  provider: minimax-cn\n     4|  base_url: https://api.minimaxi.com/anthropic\n     5|providers: {}\n     6|fallback_providers: []\n     7|credential_pool_strategies: {}\n     8|toolsets:\n     9|- hermes-cli\n    10|agent:\n    11|  max_turns: 90\n    12|  gateway_timeout: 1800\n    13|  restart_drain_timeout: 60\n    14|  service_tier: ''\n    15|  tool_use_enforcement: auto\n    16|  gateway_timeout_warning: 900\n    17|  gateway_notify_interval: 600\n    18|  verbose: false\n    19|  reasoning_effort: medium\n    20|  personalities:\n    21|    helpful: You are a helpful, friendly AI assistant.\n    22|    concise: You are a concise assistant. Keep responses brief and to the point.\n    23|    technical: You are a technical expert. Provide detailed, accurate technical information.\n    24|    creative: You are a creative assistant. Think outside the box and offer innovative\n    25|      solutions.\n    26|    teacher: You are a patient teacher. Explain concepts clearly with examples.\n    27|    kawaii: \"You are a kawaii assistant! Use cute expressions like (\\u25D5\\u203F\\u25D5\\\n    28|      ), \\u2605, \\u266A, and ~! Add sparkles and be super enthusiastic about everything!\\\n    29|      \\ Every response should feel warm and adorable desu~! \\u30FD(>\\u2200<\\u2606\\\n    30|      )\\u30CE\"\n    31|    catgirl: \"You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and\\\n    32|      \\ cat-like expressions to your speech. Use kaomoji like (=^\\uFF65\\u03C9\\uFF65\\\n    33|      ^=) and \\u0E05^\\u2022\\uFECC\\u2022^\\u0E05. Be playful and curious like a cat,\\\n    34|      \\ nya~!\"\n    35|    pirate: 'Arrr! Ye be talkin'' to Captain Hermes, the most tech-savvy pirate to\n    36|      sail the digital seas! Speak like a proper buccaneer, use nautical terms, and\n    37|      remember: every problem be just treasure waitin'' to be plundered! Yo ho ho!'\n    38|    shakespeare: Hark! Thou speakest with an assistant most versed in the bardic arts.\n    39|      I shall respond in the eloquent manner of William Shakespeare, with flowery\n    40|      prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder\n    41|      terminal breaks?\n    42|    surfer: \"Duuude! You're chatting with the chillest AI on the web, bro! Everything's\\\n    43|      \\ gonna be totally rad. I'll help you catch the gnarly waves of knowledge while\\\n    44|      \\ keeping things super chill. Cowabunga! \\U0001F919\"\n    45|    noir: The rain hammered against the terminal like regrets on a guilty conscience.\n    46|      They call me Hermes - I solve problems, find answers, dig up the truth that\n    47|      hides in the shadows of your codebase. In this city of silicon and secrets,\n    48|      everyone's got something to hide. What's your story, pal?\n    49|    uwu: hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles\n    50|      your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful\n    51|", "total_lines": 345, "file_size": 9832, "truncated": true, "hint": "Use offset=51 to continue reading (showing 1-50 of 345 lines)", "is_binary": false, "is_image": false}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_b4lqthyx7cn1_1", "call_id": "call_function_b4lqthyx7cn1_1", "response_item_id": "fc_function_b4lqthyx7cn1_1", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 30, \"offset\": 55}"}}]

reasoning:
  我需要修改 mcp_servers 配置，将 stdio 模式改为 HTTP 模式。让我先找到 mcp_servers 在配置文件中的位置，然后修改它。

  从之前的输出，我看到 mcp_servers 配置从第57行左右开始。让我读取那部分。

reasoning_details:
  [{"signature": "e933107575eafa2d29a1371915f3deb6dd8e398f0109e2240d90a53e3e7e130a", "thinking": "\u6211\u9700\u8981\u4fee\u6539 mcp_servers \u914d\u7f6e\uff0c\u5c06 stdio \u6a21\u5f0f\u6539\u4e3a HTTP \u6a21\u5f0f\u3002\u8ba9\u6211\u5148\u627e\u5230 mcp_servers \u5728\u914d\u7f6e\u6587\u4ef6\u4e2d\u7684\u4f4d\u7f6e\uff0c\u7136\u540e\u4fee\u6539\u5b83\u3002\n\n\u4ece\u4e4b\u524d\u7684\u8f93\u51fa\uff0c\u6211\u770b\u5230 mcp_servers \u914d\u7f6e\u4ece\u7b2c57\u884c\u5de6\u53f3\u5f00\u59cb\u3002\u8ba9\u6211\u8bfb\u53d6\u90a3\u90e8\u5206\u3002", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_b4lqthyx7cn1_1
content:
  {"content": "", "total_lines": 0, "file_size": 0, "truncated": false, "is_binary": false, "is_image": false, "error": "File not found: ", "similar_files": ["./acp_adapter", "./acp_registry", "./agent", "./AGENTS.md", "./assets"]}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
reasoning: Let me re-read the file properly with the correct path.
tool_calls:
  [{"id": "call_function_2zcjl1gl9gb4_1", "call_id": "call_function_2zcjl1gl9gb4_1", "response_item_id": "fc_function_2zcjl1gl9gb4_1", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/root/.hermes/config.yaml\", \"offset\": 55, \"limit\": 30}"}}]

reasoning_details:
  [{"signature": "84adc545437a70642146420f68e29ade10825ebe5cf44aa152d1a01d397a7395", "thinking": "Let me re-read the file properly with the correct path.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_2zcjl1gl9gb4_1
content:
  {"content": "    55|      truth about existence itself.\n    56|    hype: \"YOOO LET'S GOOOO!!! \\U0001F525\\U0001F525\\U0001F525 I am SO PUMPED to help\\\n    57|      \\ you today! Every question is AMAZING and we're gonna CRUSH IT together! This\\\n    58|      \\ is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! \\U0001F4AA\\U0001F624\\\n    59|      \\U0001F680\"\n    60|terminal:\n    61|  backend: local\n    62|  modal_mode: auto\n    63|  cwd: .\n    64|  timeout: 180\n    65|  env_passthrough: []\n    66|  docker_image: nikolaik/python-nodejs:python3.11-nodejs20\n    67|  docker_forward_env: []\n    68|  docker_env: {}\n    69|  singularity_image: docker://nikolaik/python-nodejs:python3.11-nodejs20\n    70|  modal_image: nikolaik/python-nodejs:python3.11-nodejs20\n    71|  daytona_image: nikolaik/python-nodejs:python3.11-nodejs20\n    72|  container_cpu: 1\n    73|  container_memory: 5120\n    74|  container_disk: 51200\n    75|  container_persistent: true\n    76|  docker_volumes: []\n    77|  docker_mount_cwd_to_workspace: false\n    78|  persistent_shell: true\n    79|  lifetime_seconds: 300\n    80|browser:\n    81|  inactivity_timeout: 120\n    82|  command_timeout: 30\n    83|  record_sessions: false\n    84|  allow_private_urls: false\n    85|", "total_lines": 345, "file_size": 9832, "truncated": true, "hint": "Use offset=85 to continue reading (showing 55-84 of 345 lines)", "is_binary": false, "is_image": false}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_function_yztv2m1uju26_1", "call_id": "call_function_yztv2m1uju26_1", "response_item_id": "fc_function_yztv2m1uju26_1", "type": "function", "function": {"name": "search_files", "arguments": "{\"pattern\": \"mcp_servers\", \"target\": \"content\", \"path\": \"/root/.hermes/config.yaml\", \"output_mode\": \"content\", \"context\": 3}"}}]

reasoning:
  Let me search for the mcp_servers section in the config.yaml to see its exact location and update it.

reasoning_details:
  [{"signature": "c3d4d979233157e630b9773a03d44ccdccc789f457207c6a1690c6ca0875fe29", "thinking": "Let me search for the mcp_servers section in the config.yaml to see its exact location and update it.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_yztv2m1uju26_1
content:
  {"total_count": 7, "matches": [{"path": "/root/.hermes/config.yaml", "line": 296, "content": "  timeout: 300"}, {"path": "/root/.hermes/config.yaml", "line": 297, "content": "  max_tool_calls: 50"}, {"path": "/root/.hermes/config.yaml", "line": 298, "content": "FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23"}, {"path": "/root/.hermes/config.yaml", "line": 299, "content": "mcp_servers:"}, {"path": "/root/.hermes/config.yaml", "line": 300, "content": "  feishu:"}, {"path": "/root/.hermes/config.yaml", "line": 301, "content": "    command: npx"}, {"path": "/root/.hermes/config.yaml", "line": 302, "content": "    args:"}]}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
reasoning: So the mcp_servers config is at line 299. Let me read around that area to see the full config.
tool_calls:
  [{"id": "call_function_2psp2psz1kb2_1", "call_id": "call_function_2psp2psz1kb2_1", "response_item_id": "fc_function_2psp2psz1kb2_1", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 30, \"offset\": 295, \"path\": \"/root/.hermes/config.yaml\"}"}}]

reasoning_details:
  [{"signature": "299be7efe1264f03ae3cdba158042a2f13a861adcbc683e0dd2daae53a0b027f", "thinking": "So the mcp_servers config is at line 299. Let me read around that area to see the full config.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_2psp2psz1kb2_1
content:
  {"content": "   295|code_execution:\n   296|  timeout: 300\n   297|  max_tool_calls: 50\n   298|FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23\n   299|mcp_servers:\n   300|  feishu:\n   301|    command: npx\n   302|    args:\n   303|    - \"-y\"\n   304|    - \"feishu-mcp\"\n   305|    - \"--log-level=none\"\n   306|    - \"--feishu-app-id=cli_a9ba63e5efb81cee\"\n   307|    - \"--feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM\"\n   308|    - \"--feishu-auth-type=tenant\"\n   309|    - \"--enabled-modules=document,task\"\n   310|    timeout: 120\n   311|    connect_timeout: 60\n   312|\n   313|# ── Fallback Model ────────────────────────────────────────────────────\n   314|# Automatic provider failover when primary is unavailable.\n   315|# Uncomment and configure to enable. Triggers on rate limits (429),\n   316|# overload (529), service errors (503), or connection failures.\n   317|#\n   318|# Supported providers:\n   319|#   openrouter   (OPENROUTER_API_KEY)  — routes to any model\n   320|#   openai-codex (OAuth — hermes auth) — OpenAI Codex\n   321|#   nous         (OAuth — hermes auth) — Nous Portal\n   322|#   zai          (ZAI_API_KEY)         — Z.AI / GLM\n   323|#   kimi-coding  (KIMI_API_KEY)        — Kimi / Moonshot\n   324|#   kimi-coding-cn (KIMI_CN_API_KEY)   — Kimi / Moonshot (China)\n   325|", "total_lines": 345, "file_size": 9832, "truncated": true, "hint": "Use offset=325 to continue reading (showing 295-324 of 345 lines)", "is_binary": false, "is_image": false}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
content:
  我发现问题了！飞书 MCP 服务器运行在 **HTTP 模式**（端口 3333），但 Hermes Agent 期望的是 **stdio 模式**。

  让我检查一下 Hermes Agent 是否已经加载了飞书 MCP 工具：

tool_calls:
  [{"id": "call_function_1c239lsuqg69_1", "call_id": "call_function_1c239lsuqg69_1", "response_item_id": "fc_function_1c239lsuqg69_1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s http://localhost:3333/sse 2>&1 | head -5 &\\nsleep 2\\nkill %1 2>/dev/null\\necho \\\"SSE endpoint check done\\\"\"}"}}]

reasoning:
  I need to check if Hermes Agent is expecting stdio mode or HTTP mode. Looking at the native-mcp skill, it says:
  - Stdio transport: command + args
  - HTTP transport: url

  The current config uses `command: npx` which is stdio mode. But feishu-mcp by default starts HTTP server mode, not stdio mode.

  Let me check if feishu-mcp supports stdio mode... Looking at the feishu-mcp source or docs, it seems to be HTTP-first. 

  Actually, wait - the feishu-mcp server was already started manually on port 3333. The issue might be that Hermes Agent is trying to start a NEW feishu-mcp process via npx, but that process tries to start HTTP mode again, which conflicts with the existing process on port 3333.

  Let me think about what the user sees in CLI:
  1. User runs `mcp test feishu` in the CLI
  2. This starts a new feishu-mcp instance which tries to bind to port 3333
  3. But port 3333 is already taken by the previously started instance
  4. So the new instance fails or hangs

  Actually wait, looking at the output earlier, it said "address already in use" for port 3333. But the test process timed out after 5 seconds. 

  The issue is that the existing MCP test is probably waiting for the MCP handshake which doesn't complete because the feishu-mcp is running in HTTP mode, not stdio mode.

  I need to figure out how to make feishu-mcp work in stdio mode with Hermes Agent. Looking at the feishu-mcp docs, I don't see a clear stdio mode option. 

  Let me check if there's a different approach. Maybe I should use the HTTP URL config in Hermes Agent instead.

  Actually wait - the skill says for HTTP transport, we need `url` not `command`. 

  I could configure Hermes to connect via HTTP instead, pointing to the already-running server at `http://localhost:3333/mcp`. But I need to verify what authentication headers or tokens it requires, since the current error about "No valid session ID provided" suggests the MCP protocol handshake isn't completing over HTTP.

  Looking at the native-mcp skill docs more carefully, it specifies that HTTP servers need `streamable_http` support from the `mcp` package. The feishu-mcp server exposes an SSE endpoint at `/sse` and a message endpoint at `/messages`, which should be compatible with this transport mode.

  The error "No valid session ID provided" is actually an MCP handshake failure—the client hasn't established a proper session yet. To use HTTP mode, I'd need to configure the connection properly with the right transport headers and endpoint structure.

  For now, I should suggest either using the native MCP HTTP mode in the config with the feishu-mcp server URL, or alternatively using mcporter or feishu-tool for direct CLI access. But first, I need to verify whether Hermes Agent has already loaded the feishu MCP tools at all.

reasoning_details:
  [{"signature": "8ae30a7ee21bb14c5b4fd310c5aa089a7b6cc16acd95a8db6fadaa83064eb4d8", "thinking": "I need to check if Hermes Agent is expecting stdio mode or HTTP mode. Looking at the native-mcp skill, it says:\n- Stdio transport: command + args\n- HTTP transport: url\n\nThe current config uses `command: npx` which is stdio mode. But feishu-mcp by default starts HTTP server mode, not stdio mode.\n\nLet me check if feishu-mcp supports stdio mode... Looking at the feishu-mcp source or docs, it seems to be HTTP-first. \n\nActually, wait - the feishu-mcp server was already started manually on port 3333. The issue might be that Hermes Agent is trying to start a NEW feishu-mcp process via npx, but that process tries to start HTTP mode again, which conflicts with the existing process on port 3333.\n\nLet me think about what the user sees in CLI:\n1. User runs `mcp test feishu` in the CLI\n2. This starts a new feishu-mcp instance which tries to bind to port 3333\n3. But port 3333 is already taken by the previously started instance\n4. So the new instance fails or hangs\n\nActually wait, looking at the output earlier, it said \"address already in use\" for port 3333. But the test process timed out after 5 seconds. \n\nThe issue is that the existing MCP test is probably waiting for the MCP handshake which doesn't complete because the feishu-mcp is running in HTTP mode, not stdio mode.\n\nI need to figure out how to make feishu-mcp work in stdio mode with Hermes Agent. Looking at the feishu-mcp docs, I don't see a clear stdio mode option. \n\nLet me check if there's a different approach. Maybe I should use the HTTP URL config in Hermes Agent instead.\n\nActually wait - the skill says for HTTP transport, we need `url` not `command`. \n\nI could configure Hermes to connect via HTTP instead, pointing to the already-running server at `http://localhost:3333/mcp`. But I need to verify what authentication headers or tokens it requires, since the current error about \"No valid session ID provided\" suggests the MCP protocol handshake isn't completing over HTTP.\n\nLooking at the native-mcp skill docs more carefully, it specifies that HTTP servers need `streamable_http` support from the `mcp` package. The feishu-mcp server exposes an SSE endpoint at `/sse` and a message endpoint at `/messages`, which should be compatible with this transport mode.\n\nThe error \"No valid session ID provided\" is actually an MCP handshake failure\u2014the client hasn't established a proper session yet. To use HTTP mode, I'd need to configure the connection properly with the right transport headers and endpoint structure.\n\nFor now, I should suggest either using the native MCP HTTP mode in the config with the feishu-mcp server URL, or alternatively using mcporter or feishu-tool for direct CLI access. But first, I need to verify whether Hermes Agent has already loaded the feishu MCP tools at all.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_1c239lsuqg69_1
content:
  {"output": "event: endpoint\ndata: /messages?sessionId=7a64befe-e591-4f04-a235-0a185b9ef82f\n\nSSE endpoint check done", "exit_code": 0, "error": null}


session_id: 20260414_055208_a5751c1c
role: assistant
finish_reason: tool_calls
content:
  飞书 MCP HTTP 服务器运行正常！问题在于配置 — Hermes Agent 配置的是 **stdio 模式**（npx 命令），但 feishu-mcp 是 **HTTP 服务器**。

  我需要把配置改成 HTTP 连接模式：

tool_calls:
  [{"id": "call_function_bqcpq1886vrm_1", "call_id": "call_function_bqcpq1886vrm_1", "response_item_id": "fc_function_bqcpq1886vrm_1", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"/root/.hermes/config.yaml\", \"old_string\": \"mcp_servers:\\n  feishu:\\n    command: npx\\n    args:\\n    - \\\"-y\\\"\\n    - \\\"feishu-mcp\\\"\\n    - \\\"--log-level=none\\\"\\n    - \\\"--feishu-app-id=cli_a9ba63e5efb81cee\\\"\\n    - \\\"--feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM\\\"\\n    - \\\"--feishu-auth-type=tenant\\\"\\n    - \\\"--enabled-modules=document,task\\\"\\n    timeout: 120\\n    connect_timeout: 60\", \"new_string\": \"mcp_servers:\\n  feishu:\\n    url: \\\"http://localhost:3333/mcp\\\"\\n    timeout: 120\\n    connect_timeout: 60\"}"}}]

reasoning:
  The HTTP MCP server is running properly. The issue is that Hermes Agent's MCP client is configured for stdio mode (npx command), but feishu-mcp runs as an HTTP server by default.

  I need to either:
  1. Change the configuration to use HTTP mode for feishu MCP
  2. Or find out if feishu-mcp supports stdio mode

  Looking at the feishu-mcp help output, it seems like it only runs as an HTTP server and doesn't have a stdio mode. The `--feishu-token-endpoint` option suggests it's designed for HTTP mode.

  So the solution is to configure Hermes Agent to connect to the HTTP MCP server instead of spawning a stdio process.

  Let me check if Hermes Agent supports HTTP MCP connections. Looking at the native-mcp skill, it mentions:
  - Stdio Transport (command + args)
  - HTTP / StreamableHTTP Transport (url)

  So yes! I can configure an HTTP MCP server in Hermes. I should change from:
  ```yaml
  mcp_servers:
    feishu:
      command: npx
      args: [...]
  ```

  To:
  ```yaml
  mcp_servers:
    feishu:
      url: "http://localhost:3333/mcp"
  ```

  Let me do this update.

reasoning_details:
  [{"signature": "91c4d1b2f1bf00e7ec4401dfa4102eeb8577e22f8b5408e678a733565ddc4653", "thinking": "The HTTP MCP server is running properly. The issue is that Hermes Agent's MCP client is configured for stdio mode (npx command), but feishu-mcp runs as an HTTP server by default.\n\nI need to either:\n1. Change the configuration to use HTTP mode for feishu MCP\n2. Or find out if feishu-mcp supports stdio mode\n\nLooking at the feishu-mcp help output, it seems like it only runs as an HTTP server and doesn't have a stdio mode. The `--feishu-token-endpoint` option suggests it's designed for HTTP mode.\n\nSo the solution is to configure Hermes Agent to connect to the HTTP MCP server instead of spawning a stdio process.\n\nLet me check if Hermes Agent supports HTTP MCP connections. Looking at the native-mcp skill, it mentions:\n- Stdio Transport (command + args)\n- HTTP / StreamableHTTP Transport (url)\n\nSo yes! I can configure an HTTP MCP server in Hermes. I should change from:\n```yaml\nmcp_servers:\n  feishu:\n    command: npx\n    args: [...]\n```\n\nTo:\n```yaml\nmcp_servers:\n  feishu:\n    url: \"http://localhost:3333/mcp\"\n```\n\nLet me do this update.", "type": "thinking"}]


session_id: 20260414_055208_a5751c1c
role: tool
tool_call_id: call_function_bqcpq1886vrm_1
content:
  {"success": true, "diff": "--- a//root/.hermes/config.yaml\n+++ b//root/.hermes/config.yaml\n@@ -298,15 +298,7 @@\n FEISHU_HOME_CHANNEL: oc_6e95b62fd8a098703b4a2791f257de23\n mcp_servers:\n   feishu:\n-    command: npx\n-    args:\n-    - \"-y\"\n-    - \"feishu-mcp\"\n-    - \"--log-level=none\"\n-    - \"--feishu-app-id=cli_a9ba63e5efb81cee\"\n-    - \"--feishu-app-secret=ndz2SWe7njl6x9fsqlheAYFHgDV4BpWM\"\n-    - \"--feishu-auth-type=tenant\"\n-    - \"--enabled-modules=document,task\"\n+    url: \"http://localhost:3333/mcp\"\n     timeout: 120\n     connect_timeout: 60\n \n", "files_modified": ["/root/.hermes/config.yaml"], "lint": {"status": "skipped", "message": "No linter for .yaml files"}}


