# sessions

id: 20260502_124831_766822
source: cli
model: anthropic/claude-sonnet-4.5
end_reason: cli_close
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.
  -->

  If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs

  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.
  Write memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. 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.

  ══════════════════════════════════════════════
  USER PROFILE (who the user is) [33% — 461/1,375 chars]
  ══════════════════════════════════════════════
  _Learn about the person you're helping. Update this as you go._
  §
  **Name:**
  §
  **What to call them:**
  §
  **Pronouns:** _(optional)_
  §
  **Timezone:**
  §
  **Notes:**
  §
  Context: _(What do they care about? What projects are they working on? What annoys them? What makes them laugh? Build this over time.)_
  §
  Context: ---
  §
  Context: The more you know, the better you can help. But remember — you're learning about a person, not building a dossier. Respect the difference.

  ## 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.
  Whenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.
  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 to Claude Code CLI (features, PRs).
      - codex: Delegate coding to OpenAI Codex CLI (features, PRs).
      - hermes-agent: Configure, extend, or contribute to Hermes Agent.
      - opencode: Delegate coding to OpenCode CLI (features, PR review).
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.
      - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
      - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.
      - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.
      - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).
      - claude-design: Design one-off HTML artifacts (landing, deck, prototype).
      - comfyui: Generate images, video, and audio with ComfyUI — install,...
      - design-md: Author/validate/export Google's DESIGN.md token spec files.
      - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
      - humanizer: Humanize text: strip AI-isms and add real voice.
      - ideation: Generate project ideas via creative constraints.
      - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.
      - p5js: p5.js sketches: gen art, shaders, interactive, 3D.
      - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).
      - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
      - pretext: Use when building creative browser demos with @chenglou/p...
      - sketch: Throwaway HTML mockups: 2-3 design variants to compare.
      - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.
      - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).
    devops:
      - kanban-orchestrator: Decomposition playbook + specialist-roster conventions + ...
      - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...
      - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.
    dogfood:
      - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).
      - pokemon-player: Play Pokemon via headless emulator + RAM reads.
    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 codebases w/ pygount: LOC, languages, ratios.
      - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
      - github-code-review: Review PRs: diffs, inline comments via gh or REST.
      - github-issues: Create, triage, label, assign GitHub issues via gh or REST.
      - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.
      - github-repo-management: Clone/create/fork repos; manage remotes, releases.
    mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.
      - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search/download GIFs from Tenor via curl + jq.
      - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.
      - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.
      - spotify: Spotify: play, search, queue, manage playlists and devices.
      - youtube-content: YouTube transcripts to summaries, threads, blogs.
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
      - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.
      - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).
      - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.
      - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.
    mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.
      - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
      - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
      - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
      - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
    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.
      - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.
      - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
      - linear: Linear: manage issues, projects, teams via GraphQL + curl.
      - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
      - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
      - notion: Notion API via curl: pages, databases, blocks, search.
      - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).
      - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.
    red-teaming:
      - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search arXiv papers by keyword, author, category, or ID.
      - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.
      - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.
      - polymarket: Query Polymarket: markets, prices, orderbooks, history.
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.
    software-development:
      - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.
      - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.
      - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.
      - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.
      - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).
      - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.
      - spike: Throwaway experiments to validate an idea before build.
      - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).
      - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.
      - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.
      - writing-plans: Write implementation plans: bite-sized tasks, paths, code.
    yuanbao:
      - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.
  </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
  # Prefer .venv; fall back to venv if that's what your checkout has.
  source .venv/bin/activate   # or: source venv/bin/activate
  ```

  `scripts/run_tests.sh` probes `.venv` first, then `venv`, then
  `$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the
  main checkout).

  ## Project Structure

  File counts shift constantly — don't treat the tree below as exhaustive.
  The canonical source is the filesystem. The notes call out the load-bearing
  entry points you'll actually edit.

  ```
  hermes-agent/
  ├── run_agent.py          # AIAgent class — core conversation loop (~12k LOC)
  ├── model_tools.py        # Tool orchestration, discover_builtin_tools(), handle_function_call()
  ├── toolsets.py           # Toolset definitions, _HERMES_CORE_TOOLS list
  ├── cli.py                # HermesCLI class — interactive CLI orchestrator (~11k LOC)
  ├── hermes_state.py       # SessionDB — SQLite session store (FTS5 search)
  ├── hermes_constants.py   # get_hermes_home(), display_hermes_home() — profile-aware paths
  ├── hermes_logging.py     # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)
  ├── batch_runner.py       # Parallel batch processing
  ├── agent/                # Agent internals (provider adapters, memory, caching, compression, etc.)
  ├── hermes_cli/           # CLI subcommands, setup wizard, plugins loader, skin engine
  ├── tools/                # Tool implementations — auto-discovered via tools/registry.py
  │   └── environments/     # Terminal backends (local, docker, ssh, modal, daytona, singularity)
  ├── gateway/              # Messaging gateway — run.py + session.py + platforms/
  │   ├── platforms/        # Adapter per platform (telegram, discord, slack, whatsapp,
  │   │                     #   homeassistant, signal, matrix, mattermost, email, sms,
  │   │                     #   dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,
  │   │                     #   webhook, api_server, ...). See ADDING_A_PLATFORM.md.
  │   └── builtin_hooks/    # Extension point for always-registered gateway hooks (none shipped)
  ├── plugins/              # Plugin system (see "Plugins" section below)
  │   ├── memory/           # Memory-provider plugins (honcho, mem0, supermemory, ...)
  │   ├── context_engine/   # Context-engine plugins
  │   └── <others>/         # Dashboard, image-gen, disk-cleanup, examples, ...
  ├── optional-skills/      # Heavier/niche skills shipped but NOT active by default
  ├── skills/               # Built-in skills bundled with the repo
  ├── ui-tui/               # Ink (React) terminal UI — `hermes --tui`
  │   └── src/              # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib
  ├── tui_gateway/          # Python JSON-RPC backend for the TUI
  ├── acp_adapter/          # ACP server (VS Code / Zed / JetBrains integration)
  ├── cron/                 # Scheduler — jobs.py, scheduler.py
  ├── environments/         # RL training environments (Atropos)
  ├── scripts/              # run_tests.sh, release.py, auxiliary scripts
  ├── website/              # Docusaurus docs site
  └── tests/                # Pytest suite (~15k tests across ~700 files as of Apr 2026)
  ```

  **User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).
  **Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),
  `gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.
  Browse with `hermes logs [--follow] [--level ...] [--session ...]`.

  ## 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)

  The real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,
  session context, budget, credential pool, etc.). The signature below is the
  minimum subset you'll usually touch — read `run_agent.py` for the full list.

  ```python
  class AIAgent:
      def __init__(self,
          base_url: str = None,
          api_key: str = None,
          provider: str = None,
          api_mode: str = None,              # "chat_completions" | "codex_responses" | ...
          model: str = "",                   # empty → resolved from config/provider later
          max_iterations: int = 90,          # tool-calling iterations (shared with subagents)
          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,
          credential_pool=None,
          # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,
          # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.
      ): ...

      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, with
  interrupt checks, budget tracking, and a one-turn grace call:

  ```python
  while (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \
          or self._budget_grace_call:
      if self._interrupt_requested: break
      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.

  ---

  ## TUI Architecture (ui-tui + tui_gateway)

  The TUI is a full replacement for the classic (prompt_toolkit) CLI, activated via `hermes --tui` or `HERMES_TUI=1`.

  ### Process Model

  ```
  hermes --tui
    └─ Node (Ink)  ──stdio JSON-RPC──  Python (tui_gateway)
         │                                  └─ AIAgent + tools + sessions
         └─ renders transcript, composer, prompts, activity
  ```

  TypeScript owns the screen. Python owns sessions, tools, model calls, and slash command logic.

  ### Transport

  Newline-delimited JSON-RPC over stdio. Requests from Ink, events from Python. See `tui_gateway/server.py` for the full method/event catalog.

  ### Key Surfaces

  | Surface | Ink component | Gateway method |
  |---------|---------------|----------------|
  | Chat streaming | `app.tsx` + `messageLine.tsx` | `prompt.submit` → `message.delta/complete` |
  | Tool activity | `thinking.tsx` | `tool.start/progress/complete` |
  | Approvals | `prompts.tsx` | `approval.respond` ← `approval.request` |
  | Clarify/sudo/secret | `prompts.tsx`, `maskedPrompt.tsx` | `clarify/sudo/secret.respond` |
  | Session picker | `sessionPicker.tsx` | `session.list/resume` |
  | Slash commands | Local handler + fallthrough | `slash.exec` → `_SlashWorker`, `command.dispatch` |
  | Completions | `useCompletion` hook | `complete.slash`, `complete.path` |
  | Theming | `theme.ts` + `branding.tsx` | `gateway.ready` with skin data |

  ### Slash Command Flow

  1. Built-in client commands (`/help`, `/quit`, `/clear`, `/resume`, `/copy`, `/paste`, etc.) handled locally in `app.tsx`
  2. Everything else → `slash.exec` (runs in persistent `_SlashWorker` subprocess) → `command.dispatch` fallback

  ### Dev Commands

  ```bash
  cd ui-tui
  npm install       # first time
  npm run dev       # watch mode (rebuilds hermes-ink + tsx --watch)
  npm start         # production
  npm run build     # full build (hermes-ink + tsc)
  npm run type-check # typecheck only (tsc --noEmit)
  npm run lint      # eslint
  npm run fmt       # prettier
  npm test          # vitest
  ```

  ### TUI in the Dashboard (`hermes dashboard` → `/chat`)

  The dashboard embeds the real `hermes --tui` — **not** a rewrite.  See `hermes_cli/pty_bridge.py` + the `@app.websocket("/api/pty")` endpoint in `hermes_cli/web_server.py`.

  - Browser loads `web/src/pages/ChatPage.tsx`, which mounts xterm.js's `Terminal` with the WebGL renderer, `@xterm/addon-fit` for container-driven resize, and `@xterm/addon-unicode11` for modern wide-character widths.
  - `/api/pty?token=…` upgrades to a WebSocket; auth uses the same ephemeral `_SESSION_TOKEN` as REST, via query param (browsers can't set `Authorization` on WS upgrade).
  - The server spawns whatever `hermes --tui` would spawn, through `ptyprocess` (POSIX PTY — WSL works, native Windows does not).
  - Frames: raw PTY bytes each direction; resize via `\x1b[RESIZE:<cols>;<rows>]` intercepted on the server and applied with `TIOCSWINSZ`.

  **Do not re-implement the primary chat experience in React.** The main transcript, composer/input flow (including slash-command behavior), and PTY-backed terminal belong to the embedded `hermes --tui` — anything new you add to Ink shows up in the dashboard automatically. If you find yourself rebuilding the transcript or composer for the dashboard, stop and extend Ink instead.

  **Structured React UI around the TUI is allowed when it is not a second chat surface.** Sidebar widgets, inspectors, summaries, status panels, and similar supporting views (e.g. `ChatSidebar`, `ModelPickerDialog`, `ToolCall`) are fine when they complement the embedded TUI rather than replacing the transcript / composer / terminal. Keep their state independent of the PTY child's session and surface their failures non-destructively so the terminal pane keeps working unimpaired.

  ---

  ## Adding New Tools

  Requires changes in **2 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=chec

  [...truncated AGENTS.md: kept 14000+4000 of 35103 chars. Use file tools to read the full file.]

   `_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

  **ALWAYS use `scripts/run_tests.sh`** — do not call `pytest` directly. The script enforces
  hermetic environment parity with CI (unset credential vars, TZ=UTC, LANG=C.UTF-8,
  4 xdist workers matching GHA ubuntu-latest). Direct `pytest` on a 16+ core
  developer machine with API keys set diverges from CI in ways that have caused
  multiple "works locally, fails in CI" incidents (and the reverse).

  ```bash
  scripts/run_tests.sh                                  # full suite, CI-parity
  scripts/run_tests.sh tests/gateway/                   # one directory
  scripts/run_tests.sh tests/agent/test_foo.py::test_x  # one test
  scripts/run_tests.sh -v --tb=long                     # pass-through pytest flags
  ```

  ### Why the wrapper (and why the old "just call pytest" doesn't work)

  Five real sources of local-vs-CI drift the script closes:

  | | Without wrapper | With wrapper |
  |---|---|---|
  | Provider API keys | Whatever is in your env (auto-detects pool) | All `*_API_KEY`/`*_TOKEN`/etc. unset |
  | HOME / `~/.hermes/` | Your real config+auth.json | Temp dir per test |
  | Timezone | Local TZ (PDT etc.) | UTC |
  | Locale | Whatever is set | C.UTF-8 |
  | xdist workers | `-n auto` = all cores (20+ on a workstation) | `-n 4` matching CI |

  `tests/conftest.py` also enforces points 1-4 as an autouse fixture so ANY pytest
  invocation (including IDE integrations) gets hermetic behavior — but the wrapper
  is belt-and-suspenders.

  ### Running without the wrapper (only if you must)

  If you can't use the wrapper (e.g. on Windows or inside an IDE that shells
  pytest directly), at minimum activate the venv and pass `-n 4`:

  ```bash
  source .venv/bin/activate   # or: source venv/bin/activate
  python -m pytest tests/ -q -n 4
  ```

  Worker count above 4 will surface test-ordering flakes that CI never sees.

  Always run the full suite before pushing changes.

  ### Don't write change-detector tests

  A test is a **change-detector** if it fails whenever data that is **expected
  to change** gets updated — model catalogs, config version numbers,
  enumeration counts, hardcoded lists of provider models. These tests add no
  behavioral coverage; they just guarantee that routine source updates break
  CI and cost engineering time to "fix."

  **Do not write:**

  ```python
  # catalog snapshot — breaks every model release
  assert "gemini-2.5-pro" in _PROVIDER_MODELS["gemini"]
  assert "MiniMax-M2.7" in models

  # config version literal — breaks every schema bump
  assert DEFAULT_CONFIG["_config_version"] == 21

  # enumeration count — breaks every time a skill/provider is added
  assert len(_PROVIDER_MODELS["huggingface"]) == 8
  ```

  **Do write:**

  ```python
  # behavior: does the catalog plumbing work at all?
  assert "gemini" in _PROVIDER_MODELS
  assert len(_PROVIDER_MODELS["gemini"]) >= 1

  # behavior: does migration bump the user's version to current latest?
  assert raw["_config_version"] == DEFAULT_CONFIG["_config_version"]

  # invariant: no plan-only model leaks into the legacy list
  assert not (set(moonshot_models) & coding_plan_only_models)

  # invariant: every model in the catalog has a context-length entry
  for m in _PROVIDER_MODELS["huggingface"]:
      assert m.lower() in DEFAULT_CONTEXT_LENGTHS_LOWER
  ```

  The rule: if the test reads like a snapshot of current data, delete it. If
  it reads like a contract about how two pieces of data must relate, keep it.
  When a PR adds a new provider/model and you want a test, make the test
  assert the relationship (e.g. "catalog entries all have context lengths"),
  not the specific names.

  Reviewers should reject new change-detector tests; authors should convert
  them into invariants before re-requesting review.

  Conversation started: Saturday, May 02, 2026 12:49 PM
  Model: anthropic/claude-sonnet-4.5
  Provider: openrouter

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal. File delivery: there is no attachment channel — the user reads your response directly in their terminal. Do NOT emit MEDIA:/path tags (those are only intercepted on messaging platforms like Telegram, Discord, Slack, etc.; on the CLI they render as literal text). When referring to a file you created or changed, just state its absolute path in plain text; the user can open it from there.


id: 20260502_125110_775fe4ea
source: telegram
user_id: 1223068642
model: anthropic/claude-sonnet-4.5
end_reason: session_reset
billing_provider: openrouter
billing_base_url: https://openrouter.ai/api/v1
cost_status: unknown
cost_source: provider_models_api
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.
  -->

  If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs

  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.
  Write memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. 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.

  ══════════════════════════════════════════════
  USER PROFILE (who the user is) [33% — 461/1,375 chars]
  ══════════════════════════════════════════════
  _Learn about the person you're helping. Update this as you go._
  §
  **Name:**
  §
  **What to call them:**
  §
  **Pronouns:** _(optional)_
  §
  **Timezone:**
  §
  **Notes:**
  §
  Context: _(What do they care about? What projects are they working on? What annoys them? What makes them laugh? Build this over time.)_
  §
  Context: ---
  §
  Context: The more you know, the better you can help. But remember — you're learning about a person, not building a dossier. Respect the difference.

  ## 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.
  Whenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.
  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 to Claude Code CLI (features, PRs).
      - codex: Delegate coding to OpenAI Codex CLI (features, PRs).
      - hermes-agent: Configure, extend, or contribute to Hermes Agent.
      - opencode: Delegate coding to OpenCode CLI (features, PR review).
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.
      - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
      - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.
      - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.
      - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).
      - claude-design: Design one-off HTML artifacts (landing, deck, prototype).
      - comfyui: Generate images, video, and audio with ComfyUI — install,...
      - design-md: Author/validate/export Google's DESIGN.md token spec files.
      - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
      - humanizer: Humanize text: strip AI-isms and add real voice.
      - ideation: Generate project ideas via creative constraints.
      - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.
      - p5js: p5.js sketches: gen art, shaders, interactive, 3D.
      - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).
      - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
      - pretext: Use when building creative browser demos with @chenglou/p...
      - sketch: Throwaway HTML mockups: 2-3 design variants to compare.
      - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.
      - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).
    devops:
      - kanban-orchestrator: Decomposition playbook + specialist-roster conventions + ...
      - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...
      - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.
    dogfood:
      - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).
      - pokemon-player: Play Pokemon via headless emulator + RAM reads.
    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 codebases w/ pygount: LOC, languages, ratios.
      - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
      - github-code-review: Review PRs: diffs, inline comments via gh or REST.
      - github-issues: Create, triage, label, assign GitHub issues via gh or REST.
      - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.
      - github-repo-management: Clone/create/fork repos; manage remotes, releases.
    mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.
      - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search/download GIFs from Tenor via curl + jq.
      - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.
      - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.
      - spotify: Spotify: play, search, queue, manage playlists and devices.
      - youtube-content: YouTube transcripts to summaries, threads, blogs.
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
      - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.
      - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).
      - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.
      - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.
    mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.
      - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
      - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
      - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
      - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
    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.
      - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.
      - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
      - linear: Linear: manage issues, projects, teams via GraphQL + curl.
      - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
      - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
      - notion: Notion API via curl: pages, databases, blocks, search.
      - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).
      - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.
    red-teaming:
      - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search arXiv papers by keyword, author, category, or ID.
      - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.
      - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.
      - polymarket: Query Polymarket: markets, prices, orderbooks, history.
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.
    software-development:
      - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.
      - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.
      - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.
      - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.
      - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).
      - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.
      - spike: Throwaway experiments to validate an idea before build.
      - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).
      - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.
      - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.
      - writing-plans: Write implementation plans: bite-sized tasks, paths, code.
    yuanbao:
      - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.
  </available_skills>

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

  Conversation started: Saturday, May 02, 2026 12:51 PM
  Model: anthropic/claude-sonnet-4.5
  Provider: openrouter

  You are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.


id: 20260503_014409_dc10a7
source: cli
model: anthropic/claude-sonnet-4.5
end_reason: cli_close
billing_provider: openrouter
billing_base_url: https://openrouter.ai/api/v1
cost_status: estimated
cost_source: provider_models_api
title: User Ending Conversation
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.
  -->

  If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs

  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.
  Write memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. 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.

  ══════════════════════════════════════════════
  USER PROFILE (who the user is) [33% — 461/1,375 chars]
  ══════════════════════════════════════════════
  _Learn about the person you're helping. Update this as you go._
  §
  **Name:**
  §
  **What to call them:**
  §
  **Pronouns:** _(optional)_
  §
  **Timezone:**
  §
  **Notes:**
  §
  Context: _(What do they care about? What projects are they working on? What annoys them? What makes them laugh? Build this over time.)_
  §
  Context: ---
  §
  Context: The more you know, the better you can help. But remember — you're learning about a person, not building a dossier. Respect the difference.

  ## 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.
  Whenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.
  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 to Claude Code CLI (features, PRs).
      - codex: Delegate coding to OpenAI Codex CLI (features, PRs).
      - hermes-agent: Configure, extend, or contribute to Hermes Agent.
      - opencode: Delegate coding to OpenCode CLI (features, PR review).
    creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
      - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.
      - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
      - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.
      - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.
      - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).
      - claude-design: Design one-off HTML artifacts (landing, deck, prototype).
      - comfyui: Generate images, video, and audio with ComfyUI — install,...
      - design-md: Author/validate/export Google's DESIGN.md token spec files.
      - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
      - humanizer: Humanize text: strip AI-isms and add real voice.
      - ideation: Generate project ideas via creative constraints.
      - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.
      - p5js: p5.js sketches: gen art, shaders, interactive, 3D.
      - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).
      - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
      - pretext: Use when building creative browser demos with @chenglou/p...
      - sketch: Throwaway HTML mockups: 2-3 design variants to compare.
      - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.
      - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...
    data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.
      - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).
    devops:
      - kanban-orchestrator: Decomposition playbook + specialist-roster conventions + ...
      - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...
      - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.
    dogfood:
      - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.
    email: Skills for sending, receiving, searching, and managing email from the terminal.
      - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.
    gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.
      - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).
      - pokemon-player: Play Pokemon via headless emulator + RAM reads.
    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 codebases w/ pygount: LOC, languages, ratios.
      - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
      - github-code-review: Review PRs: diffs, inline comments via gh or REST.
      - github-issues: Create, triage, label, assign GitHub issues via gh or REST.
      - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.
      - github-repo-management: Clone/create/fork repos; manage remotes, releases.
    mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.
      - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).
    media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
      - gif-search: Search/download GIFs from Tenor via curl + jq.
      - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.
      - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.
      - spotify: Spotify: play, search, queue, manage playlists and devices.
      - youtube-content: YouTube transcripts to summaries, threads, blogs.
    mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models
      - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.
    mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.
      - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
      - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.
    mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.
      - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.
      - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).
      - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.
      - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.
    mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.
      - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
      - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.
    mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.
      - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.
    mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.
      - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
      - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
      - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
    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.
      - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.
      - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
      - linear: Linear: manage issues, projects, teams via GraphQL + curl.
      - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
      - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
      - notion: Notion API via curl: pages, databases, blocks, search.
      - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).
      - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.
    red-teaming:
      - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.
    research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.
      - arxiv: Search arXiv papers by keyword, author, category, or ID.
      - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.
      - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.
      - polymarket: Query Polymarket: markets, prices, orderbooks, history.
    smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.
      - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.
    social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.
      - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.
    software-development:
      - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.
      - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.
      - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.
      - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.
      - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).
      - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.
      - spike: Throwaway experiments to validate an idea before build.
      - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).
      - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.
      - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.
      - writing-plans: Write implementation plans: bite-sized tasks, paths, code.
    yuanbao:
      - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.
  </available_skills>

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

  Conversation started: Sunday, May 03, 2026 01:47 AM
  Model: anthropic/claude-sonnet-4.5
  Provider: openrouter

  You are a CLI AI Agent. Try not to use markdown but simple text renderable inside a terminal. File delivery: there is no attachment channel — the user reads your response directly in their terminal. Do NOT emit MEDIA:/path tags (those are only intercepted on messaging platforms like Telegram, Discord, Slack, etc.; on the CLI they render as literal text). When referring to a file you created or changed, just state its absolute path in plain text; the user can open it from there.


id: 20260503_015237_1252d5
source: telegram
model: minimax/minimax-m2.5:free
parent_session_id: 20260502_125110_775fe4ea
billing_provider: openrouter
billing_base_url: https://openrouter.ai/api/v1
cost_status: unknown
cost_source: provider_models_api
model_config:
  {"max_iterations": 50, "reasoning_config": {"enabled": true, "effort": "medium"}, "max_tokens": null}

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.

  If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs

  Conversation started: Sunday, May 03, 2026 01:52 AM
  Model: minimax/minimax-m2.5:free
  Provider: openrouter

  You are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.


# messages

session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: stop
content:
  Hello! 👋

  I'm currently using **Claude Sonnet 4.5** (via OpenRouter).

  Is there anything I can help you with today?

reasoning:
  The user is asking what model I'm using. I can see from the system prompt that:

  "Model: anthropic/claude-sonnet-4.5
  Provider: openrouter"

  So I should let them know I'm using Claude Sonnet 4.5 (via OpenRouter).

reasoning_content:
  The user is asking what model I'm using. I can see from the system prompt that:

  "Model: anthropic/claude-sonnet-4.5
  Provider: openrouter"

  So I should let them know I'm using Claude Sonnet 4.5 (via OpenRouter).


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: stop
content:
  这个 app 创意有一定潜力，我来分析一下：

  ## ✅ **优势**

  1. **价值主张清晰** - "拍照 → 识别问题 → 给方案" 的流程直观易懂
  2. **Freemium 经典模式** - 免费用户体验核心功能（识别），付费解锁完整价值（解决方案）
  3. **Hook 点精准** - 用户已经投入时间拍照并看到问题，此时付费意愿最高

  ## ⚠️ **挑战和风险**

  1. **垂直场景很关键**
     - 通用型（什么都拍）很难做准，也缺乏专业性
     - 建议聚焦：植物病害诊断、家居维修、医疗咨询（需合规）、汽车故障等单一垂直

  2. **免费/付费边界的平衡**
     - 如果免费结论太模糊 → 用户觉得没用直接卸载
     - 如果免费结论太具体 → 用户觉得够用不付费
     - **建议**：免费版给"是什么问题 + 严重程度"，付费版给"具体步骤 + 工具清单 + 视频教程"

  3. **竞争壁垒**
     - Google Lens / GPT-4V 已经能"拍照识别 + 给建议"
     - 你的优势必须是：**更准确的垂直识别 + 结构化的解决方案库**

  ## 💡 **改进建议**

  1. **增强留存的设计**
     - 扫描历史记录（免费）
     - 问题追踪（"3 天后提醒我检查"）
     - 社区功能（用户分享修复前后对比）

  2. **变现优化**
     - 单次解锁（$2.99 看一次完整方案）+ 订阅（$9.99/月无限次）双模式
     - 前 3 次扫描免费看完整方案（提高首次付费转化）

  3. **病毒传播**
     - 分享修复成果到社交媒体（watermark 带 app 名）
     - "帮朋友诊断"功能（生成分享链接）

  ## 🎯 **Next Steps**

  如果要验证这个想法，我建议：
  1. 选一个最痛的垂直场景（比如"植物养护"）
  2. 用现成的视觉 API（GPT-4V / Google Cloud Vision）+ 提示词工程快速做 MVP
  3. 测试免费用户的"结论满意度"和付费转化率

  要我帮你做技术原型或市场调研吗？

reasoning:
  用户用中文问我关于一个 app 创意的想法。让我理解一下这个概念：

  **输入：** 拍一张照片
  **AI：** 识别问题 + 风险 + 3步解决
  **输出：** 可读结论 + Pro 才能看完整步骤
  **变现：** 扫描结果页触发 Paywall

  这是一个基于视觉识别的问题诊断 app 概念：
  1. 用户拍照（比如损坏的物品、植物病害、皮肤问题等）
  2. AI 分析识别问题和风险
  3. 给出 3 步解决方案
  4. 免费用户只能看到简要结论
  5. Pro 订阅用户才能看完整的解决步骤

  变现模式是在扫描结果页面设置付费墙。

  用户问我这个主意怎么样，想要我的评估。根据系统提示，用户写中文，我应该用中文回复。

  让我给出建设性的分析：优点、挑战、改进建议。

reasoning_content:
  用户用中文问我关于一个 app 创意的想法。让我理解一下这个概念：

  **输入：** 拍一张照片
  **AI：** 识别问题 + 风险 + 3步解决
  **输出：** 可读结论 + Pro 才能看完整步骤
  **变现：** 扫描结果页触发 Paywall

  这是一个基于视觉识别的问题诊断 app 概念：
  1. 用户拍照（比如损坏的物品、植物病害、皮肤问题等）
  2. AI 分析识别问题和风险
  3. 给出 3 步解决方案
  4. 免费用户只能看到简要结论
  5. Pro 订阅用户才能看完整的解决步骤

  变现模式是在扫描结果页面设置付费墙。

  用户问我这个主意怎么样，想要我的评估。根据系统提示，用户写中文，我应该用中文回复。

  让我给出建设性的分析：优点、挑战、改进建议。


session_id: 20260503_014409_dc10a7
role: assistant
content: Goodbye! Feel free to return anytime you need assistance.
finish_reason: stop
reasoning:
  The user is saying "exit". This is a simple command that typically means they want to exit the current session or application. Since I'm a CLI AI Agent, I should acknowledge this and provide a friendly farewell message. I don't need to use any tools for this - just provide a simple, terminal-friendly response.

reasoning_content:
  The user is saying "exit". This is a simple command that typically means they want to exit the current session or application. Since I'm a CLI AI Agent, I should acknowledge this and provide a friendly farewell message. I don't need to use any tools for this - just provide a simple, terminal-friendly response.


session_id: 20260503_014409_dc10a7
role: assistant
content: Goodbye!
finish_reason: stop
reasoning:
  The user said "exit" again. They want to exit the conversation or session. I should acknowledge this politely and briefly. Since they've said it twice, they clearly want to end the session.

reasoning_content:
  The user said "exit" again. They want to exit the conversation or session. I should acknowledge this politely and briefly. Since they've said it twice, they clearly want to end the session.


session_id: 20260503_014409_dc10a7
role: assistant
finish_reason: stop
content:
  It looks like you're trying to exit a Python interpreter, but you're currently in the Hermes Agent CLI.

  To exit Hermes, you can:
  - Press Ctrl+C or Ctrl+D
  - Close the terminal window
  - Type "quit" or "exit" (without parentheses)

  If you need to run Python code, I can help with that using the terminal or execute_code tools!

reasoning:
  The user is typing "exit()" which looks like they're trying to exit a Python REPL or similar interactive session. However, I'm the Hermes Agent CLI interface, not a Python interpreter. They might be confused about what interface they're in, or they might genuinely just want to exit our conversation.

  I should respond in a friendly way that acknowledges their command and clarifies that they can just close the terminal or use Ctrl+C/Ctrl+D to exit the Hermes session if that's what they want to do.

reasoning_content:
  The user is typing "exit()" which looks like they're trying to exit a Python REPL or similar interactive session. However, I'm the Hermes Agent CLI interface, not a Python interpreter. They might be confused about what interface they're in, or they might genuinely just want to exit our conversation.

  I should respond in a friendly way that acknowledges their command and clarifies that they can just close the terminal or use Ctrl+C/Ctrl+D to exit the Hermes session if that's what they want to do.


session_id: 20260503_015237_1252d5
role: user
content:
  Research the market for a photo-based problem diagnosis app with freemium/paywall model. Find:
  1. Direct competitors (apps that let users take a photo to identify problems and get solutions)
  2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)
  3. Market size and trends for AI-powered visual diagnosis apps
  4. Pricing models used by similar apps
  5. User reviews mentioning paywall/frustration

  Search in English and Chinese. Return a comprehensive summary with app names, key features, pricing, and market positioning.


session_id: 20260503_015237_1252d5
role: assistant
finish_reason: stop
content:
  I'll research the market for photo-based problem diagnosis apps. Let me start by checking the workspace and then conducting comprehensive searches.
  <minimax:tool_call>
  <invoke name="cli-mcp-server_run_command">
  <parameter name="command">ls -la /home/ubuntu</parameter>
  </invoke>
  </minimax:tool_call>

reasoning:
  The user wants me to research the market for a photo-based problem diagnosis app with a freemium/paywall model. I need to find:

  1. Direct competitors (apps that let users take a photo to identify problems and get solutions)
  2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)
  3. Market size and trends for AI-powered visual diagnosis apps
  4. Pricing models used by similar apps
  5. User reviews mentioning paywall/frustration

  I should search in both English and Chinese. Let me start by doing web searches to gather this information.

  First, let me check the workspace to see if there's anything already there.

reasoning_content:
  The user wants me to research the market for a photo-based problem diagnosis app with a freemium/paywall model. I need to find:

  1. Direct competitors (apps that let users take a photo to identify problems and get solutions)
  2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)
  3. Market size and trends for AI-powered visual diagnosis apps
  4. Pricing models used by similar apps
  5. User reviews mentioning paywall/frustration

  I should search in both English and Chinese. Let me start by doing web searches to gather this information.

  First, let me check the workspace to see if there's anything already there.


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_18fb57b468624c67a79c0e0c", "call_id": "call_18fb57b468624c67a79c0e0c", "response_item_id": "fc_18fb57b468624c67a79c0e0c", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"goal\": \"Research the market for a photo-based problem diagnosis app with freemium/paywall model. Find:\\n1. Direct competitors (apps that let users take a photo to identify problems and get solutions)\\n2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)\\n3. Market size and trends for AI-powered visual diagnosis apps\\n4. Pricing models used by similar apps\\n5. User reviews mentioning paywall/frustration\\n\\nSearch in English and Chinese. Return a comprehensive summary with app names, key features, pricing, and market positioning.\", \"toolsets\": [\"web\"]}"}}]

reasoning:
  The user wants me to do market research for this app idea. Let me search_files for existing apps and competitors in this space - photo-based problem identification with freemium/paywall model.

  I should search_files for:
  1. Existing apps that do photo-based problem diagnosis
  2. Competitors in this space (plant diagnosis, car repair, home maintenance, medical, etc.)
  3. Market reports on this category
  4. Similar apps with paywall models

  Let me do some web research to gather this information.

reasoning_content:
  The user wants me to do market research for this app idea. Let me search_files for existing apps and competitors in this space - photo-based problem identification with freemium/paywall model.

  I should search_files for:
  1. Existing apps that do photo-based problem diagnosis
  2. Competitors in this space (plant diagnosis, car repair, home maintenance, medical, etc.)
  3. Market reports on this category
  4. Similar apps with paywall models

  Let me do some web research to gather this information.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_18fb57b468624c67a79c0e0c
content:
  {"results": [{"task_index": 0, "status": "completed", "summary": "I'll research the market for photo-based problem diagnosis apps. Let me start by checking the workspace and then conducting comprehensive searches.\n<minimax:tool_call>\n<invoke name=\"cli-mcp-server_run_command\">\n<parameter name=\"command\">ls -la /home/ubuntu</parameter>\n</invoke>\n</minimax:tool_call>", "api_calls": 1, "duration_seconds": 10.99, "model": "minimax/minimax-m2.5:free", "exit_reason": "completed", "tokens": {"input": 755, "output": 198}, "tool_trace": []}], "total_duration_seconds": 11.03}


session_id: 20260502_125110_775fe4ea
role: assistant
content: 让我直接来做市场调研：
finish_reason: tool_calls
tool_calls:
  [{"id": "call_f06582204ea04b1995d85f90", "call_id": "call_f06582204ea04b1995d85f90", "response_item_id": "fc_f06582204ea04b1995d85f90", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\": \"https://www.apple.com/cn/app-store/\"}"}}]

reasoning:
  The delegate_task seems to have had an issue - it only ran one command and didn't complete the actual research. Let me do the market research myself using web search and browsing tools.

reasoning_content:
  The delegate_task seems to have had an issue - it only ran one command and didn't complete the actual research. Let me do the market research myself using web search and browsing tools.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_f06582204ea04b1995d85f90
content:
  {"success": false, "error": "Auto-launch failed: Chrome not found. Checked:\n  - agent-browser cache: /home/ubuntu/.agent-browser/browsers\n  - System Chrome installations\n  - Puppeteer browser cache\n  - Playwright browser cache\nRun `agent-browser install` to download Chrome, or use --executable-path."}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
reasoning: Browser not available. Let me use web search instead.
reasoning_content: Browser not available. Let me use web search instead.
tool_calls:
  [{"id": "call_161b6b5a9f7348aa82f6106f", "call_id": "call_161b6b5a9f7348aa82f6106f", "response_item_id": "fc_161b6b5a9f7348aa82f6106f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://api.allorigins.win/raw?url=https://www.google.com/search?q=photo+problem+diagnosis+app+AI+plant+car+skin+freemium+market\\\" | head -100\"}"}}]


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_99c52964467f4fe597ec66ab", "call_id": "call_99c52964467f4fe597ec66ab", "response_item_id": "fc_99c52964467f4fe597ec66ab", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://r.jina.ai/http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024\\\" 2>/dev/null || echo \\\"trying alternative...\\\"\"}"}}]

reasoning:
  Let me try a different approach - I'll use my knowledge to provide a comprehensive market analysis, and also try some web searches with different methods.

reasoning_content:
  Let me try a different approach - I'll use my knowledge to provide a comprehensive market analysis, and also try some web searches with different methods.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_99c52964467f4fe597ec66ab
content:
  {"output": "Title: https://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024&sei=l6r2adT6FOLZruEP6OPv6A0\n\nURL Source: http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024\n\nWarning: Target URL returned error 429: Too Many Requests\nWarning: This page maybe requiring CAPTCHA, please make sure you are authorized to access this page.\n\nMarkdown Content:\n**About this page**\n\nOur systems have detected unusual traffic from your computer network. This page checks to see if it's really you sending the requests, and not a robot. [Why did this happen?](http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024#)\n\nIP address: 2600:1900:0:2d0b::2101\n\nTime: 2026-05-03T01:53:27Z\n\nURL: https://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024&sei=l6r2adT6FOLZruEP6OPv6A0", "exit_code": 0, "error": null}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_4e8572dcd97b4b5a929b7f8a", "call_id": "call_4e8572dcd97b4b5a929b7f8a", "response_item_id": "fc_4e8572dcd97b4b5a929b7f8a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://r.jina.ai/http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size\\\" 2>/dev/null | head -50\"}"}}]

reasoning:
  Web search is getting rate limited. Let me try a different approach - I'll use my knowledge to provide a comprehensive market analysis based on what I know about this space, and also try some direct API calls to app store APIs or other sources.

reasoning_content:
  Web search is getting rate limited. Let me try a different approach - I'll use my knowledge to provide a comprehensive market analysis based on what I know about this space, and also try some direct API calls to app store APIs or other sources.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_4e8572dcd97b4b5a929b7f8a
content:
  {"output": "Title: 404. Page Not Found - Bloomberg\n\nURL Source: http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size\n\nWarning: Target URL returned error 404: Not Found\n\nMarkdown Content:\n# 404. Page Not Found - Bloomberg\n\n[Skip to content](http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size#that-jump-content--default)\n\n[Bloomberg the Company & Its Products The Company & its Products](https://www.bloomberg.com/company/?utm_source=bloomberg-menu&utm_medium=bcom)[Bloomberg Terminal Demo Request](https://www.bloomberg.com/professional/contact-menu/?utm_source=bloomberg-menu&utm_medium=bcom&bbgsum=DG-WS-PROF-DEMO-bbgmenu)[Bloomberg Anywhere Remote Login Bloomberg Anywhere Login](https://bba.bloomberg.net/?utm_source=bloomberg-menu&utm_medium=bcom)[Bloomberg Customer Support Customer Support](https://www.bloomberg.com/professional/support/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n*   ### Bloomberg\n\nConnecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world \n### For Customers\n\n    *   [Bloomberg Anywhere Remote Login](https://bba.bloomberg.net/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Software Updates](https://www.bloomberg.com/professional/support/customer-support/software-updates/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Manage Products and Account Information](https://service.bloomberg.com/portal/sessions/new?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Support\n\nAmericas+1 212 318 2000\n\nEMEA+44 20 7330 7500\n\nAsia Pacific+65 6212 1000\n\n*   ### Company\n\n    *   [About](https://www.bloomberg.com/company/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Careers](https://www.bloomberg.com/company/what-we-do/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Inclusion at Bloomberg](https://www.bloomberg.com/company/values/inclusion/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Tech at Bloomberg](https://www.bloomberg.com/company/values/tech-at-bloomberg/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Philanthropy](https://www.bloomberg.com/company/philanthropy/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Sustainability](https://www.bloomberg.com/company/sustainability/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Bloomberg Beta](https://www.bloomberg.com/company/values/tech-at-bloomberg/bloomberg-beta/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Communications\n\n    *   [Press Announcements](https://www.bloomberg.com/company/announcements/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Press Contacts](https://www.bloomberg.com/company/press-contacts/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Follow\n\n    *   [Facebook](https://www.facebook.com/Bloomberglp)\n    *   [Instagram](https://www.instagram.com/bloomberg/)\n    *   [LinkedIn](https://www.linkedin.com/company/2494)", "exit_code": 0, "error": null}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_27f195d2bd7b499783f1e6df", "call_id": "call_27f195d2bd7b499783f1e6df", "response_item_id": "fc_27f195d2bd7b499783f1e6df", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]

reasoning:
  Web searches are getting blocked. Let me provide a comprehensive market analysis based on my knowledge of this space, and then try some direct app store API calls if possible.

reasoning_content:
  Web searches are getting blocked. Let me provide a comprehensive market analysis based on my knowledge of this space, and then try some direct app store API calls if possible.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_27f195d2bd7b499783f1e6df
content:
  {"output": "PictureThis - Plant Identifier - Glority Global Group Ltd.\nPlant Parent: Plant Care Guide - Glority Global Group Ltd.\nPlantIn: Plant Identifier・Care - Vortemol Limited\nPlanta: AI Plant & Garden Care - Planta AB\nPlant App: Plant Identifier - SCALEUP YAZILIM HIZMETLERI\nPlant Disease Diagnosis App - App Holdings\nPlantSnap - Plant Scanner - PlantSnap, Inc.\nBlossom - Plant Care Guide - Mosaic S.r.l.\nAgrio - Plant diagnosis app - Saillog LTD", "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://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10  — or: vet https://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user."}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_6e25685d7eb24198bba9e2eb", "call_id": "call_6e25685d7eb24198bba9e2eb", "response_item_id": "fc_6e25685d7eb24198bba9e2eb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]

reasoning:
  Good, I got some app store results. Let me continue to gather more data on competitors in different verticals.

reasoning_content:
  Good, I got some app store results. Let me continue to gather more data on competitors in different verticals.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_6e25685d7eb24198bba9e2eb
content:
  {"output": "Aysa - Logical Images\nAl Skin Scanner: mole, rash id - SWIFTTOUCH APPS LTD\nscanoma - mole check - Scanoma Pty Ltd\nSkinSAFE: AI Skincare Scanner - SkinSAFE Products, Inc\nNolla Skin - MAGIC HEALTH INC. dba NOLLA HEALTH\nMiiskin: Rx Dermatology Visits - Miiskin\nSkin-Check - Skin Check Inc.\nMary Kay® Skin Analyzer - Mary Kay Inc.\nAI Skin Scanner: Mole Tracker - CLOUD COMP. TECH", "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://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10  — or: vet https://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user."}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_57a418bca54644108c37cd5a", "call_id": "call_57a418bca54644108c37cd5a", "response_item_id": "fc_57a418bca54644108c37cd5a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=car+repair+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]

reasoning:
  Good, I'm getting data. Let me also search_files for car repair and home maintenance apps, and then compile a comprehensive market analysis.

reasoning_content:
  Good, I'm getting data. Let me also search_files for car repair and home maintenance apps, and then compile a comprehensive market analysis.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_57a418bca54644108c37cd5a
content:
  {"output": "Car Scanner ELM OBD2 - Stanislav Svistunov\nMIMP: Car Diagnosis & Repair - DojoTrack LLC\nARI (Auto Repair Software) - Constantin Coutun\nRepairSolutions2 - Innova Electronic\nFIXD OBD2 Scanner - FIXD Automotive Inc\nCARFAX Car Care - CARFAX, Inc.\nZUS - Save Car Expenses - No NDA Inc\nCarista OBD2 - Prizmos\nRepair My Car! - Rollic Games\nCARFAX - New & Used Cars - CARFAX, Inc.", "exit_code": 0, "error": null}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_e26cf697c57d43f4a9ab3eba", "call_id": "call_e26cf697c57d43f4a9ab3eba", "response_item_id": "fc_e26cf697c57d43f4a9ab3eba", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=home+repair+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]

reasoning:
  Let me also search_files for home/electrical repair apps and then compile everything into a comprehensive market report.

reasoning_content:
  Let me also search_files for home/electrical repair apps and then compile everything into a comprehensive market report.


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_e26cf697c57d43f4a9ab3eba
content:
  {"output": "AHS: American Home Shield - Frontdoor Incorporated\nChoice Home Warranty - Choice Home Warranty Group, Inc.\nLiberty Home Guard - Liberty Home Guard LLC\nUnder My Roof Home Inventory + - Binary Formations, LLC\nHomer: The Home Management App - Home Owner AB\nYouFixedIt: AI Home Repair - COLIN CLIFFORD MATTHES\nFrontdoor-Home Service Experts - Frontdoor Incorporated\nInvitation Homes Maintenance - SMS Assist, LLC.\nHippo Home: Maintain & Insure - Hippo Analytics Inc.\nPuls - Home Services - Puls on demand services", "exit_code": 0, "error": null}


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
tool_calls:
  [{"id": "call_7c709e1474754fcf93e00c55", "call_id": "call_7c709e1474754fcf93e00c55", "response_item_id": "fc_7c709e1474754fcf93e00c55", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1436963778\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); r=d.get('results',[{}])[0]; print(f\\\\\\\"App: {r.get('trackName')}\\\\nPrice: {r.get('price','N/A')}\\\\nDescription: {r.get('description','N/A')[:200]}...\\\\\\\")\\\" 2>/dev/null || echo \\\"failed\\\"\"}"}}]

reasoning:
  Good, I have a lot of data now. Let me also try to get some pricing information for these apps to understand the market better.

reasoning_content:
  Good, I have a lot of data now. Let me also try to get some pricing information for these apps to understand the market better.


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
reasoning: Let me try a different approach to get app details.
reasoning_content: Let me try a different approach to get app details.
tool_calls:
  [{"id": "call_21cb043e0c874f44bcbc0f37", "call_id": "call_21cb043e0c874f44bcbc0f37", "response_item_id": "fc_21cb043e0c874f44bcbc0f37", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1436963778\\\" 2>/dev/null | head -200\"}"}}]


session_id: 20260502_125110_775fe4ea
role: assistant
finish_reason: tool_calls
reasoning: Let me try with a different app ID that I know exists.
reasoning_content: Let me try with a different app ID that I know exists.
tool_calls:
  [{"id": "call_2bad3a12beff4d5da8c07352", "call_id": "call_2bad3a12beff4d5da8c07352", "response_item_id": "fc_2bad3a12beff4d5da8c07352", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1452877078\\\" 2>/dev/null | python3 -m json.tool 2>/dev/null | head -30\"}"}}]


session_id: 20260502_125110_775fe4ea
role: tool
tool_call_id: call_2bad3a12beff4d5da8c07352
content:
  {"output": "", "exit_code": -1, "error": "BLOCKED: Command timed out. Do NOT retry this command.", "status": "blocked"}


session_id: 20260502_125110_775fe4ea
role: user
content:
  [System note: Your previous turn was interrupted before you could process the last tool result(s). The conversation history contains tool outputs you haven't responded to yet. Please finish processing those results and summarize what was accomplished, then address the user's new message below.]

  怎么这么长时间？


# messages_fts_content

c0:
  Hello! 👋

  I'm currently using **Claude Sonnet 4.5** (via OpenRouter).

  Is there anything I can help you with today?  


c0:
  这个 app 创意有一定潜力，我来分析一下：

  ## ✅ **优势**

  1. **价值主张清晰** - "拍照 → 识别问题 → 给方案" 的流程直观易懂
  2. **Freemium 经典模式** - 免费用户体验核心功能（识别），付费解锁完整价值（解决方案）
  3. **Hook 点精准** - 用户已经投入时间拍照并看到问题，此时付费意愿最高

  ## ⚠️ **挑战和风险**

  1. **垂直场景很关键**
     - 通用型（什么都拍）很难做准，也缺乏专业性
     - 建议聚焦：植物病害诊断、家居维修、医疗咨询（需合规）、汽车故障等单一垂直

  2. **免费/付费边界的平衡**
     - 如果免费结论太模糊 → 用户觉得没用直接卸载
     - 如果免费结论太具体 → 用户觉得够用不付费
     - **建议**：免费版给"是什么问题 + 严重程度"，付费版给"具体步骤 + 工具清单 + 视频教程"

  3. **竞争壁垒**
     - Google Lens / GPT-4V 已经能"拍照识别 + 给建议"
     - 你的优势必须是：**更准确的垂直识别 + 结构化的解决方案库**

  ## 💡 **改进建议**

  1. **增强留存的设计**
     - 扫描历史记录（免费）
     - 问题追踪（"3 天后提醒我检查"）
     - 社区功能（用户分享修复前后对比）

  2. **变现优化**
     - 单次解锁（$2.99 看一次完整方案）+ 订阅（$9.99/月无限次）双模式
     - 前 3 次扫描免费看完整方案（提高首次付费转化）

  3. **病毒传播**
     - 分享修复成果到社交媒体（watermark 带 app 名）
     - "帮朋友诊断"功能（生成分享链接）

  ## 🎯 **Next Steps**

  如果要验证这个想法，我建议：
  1. 选一个最痛的垂直场景（比如"植物养护"）
  2. 用现成的视觉 API（GPT-4V / Google Cloud Vision）+ 提示词工程快速做 MVP
  3. 测试免费用户的"结论满意度"和付费转化率

  要我帮你做技术原型或市场调研吗？  


c0:
  It looks like you're trying to exit a Python interpreter, but you're currently in the Hermes Agent CLI.

  To exit Hermes, you can:
  - Press Ctrl+C or Ctrl+D
  - Close the terminal window
  - Type "quit" or "exit" (without parentheses)

  If you need to run Python code, I can help with that using the terminal or execute_code tools!  


c0:
  Research the market for a photo-based problem diagnosis app with freemium/paywall model. Find:
  1. Direct competitors (apps that let users take a photo to identify problems and get solutions)
  2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)
  3. Market size and trends for AI-powered visual diagnosis apps
  4. Pricing models used by similar apps
  5. User reviews mentioning paywall/frustration

  Search in English and Chinese. Return a comprehensive summary with app names, key features, pricing, and market positioning.  


c0:
  I'll research the market for photo-based problem diagnosis apps. Let me start by checking the workspace and then conducting comprehensive searches.
  <minimax:tool_call>
  <invoke name="cli-mcp-server_run_command">
  <parameter name="command">ls -la /home/ubuntu</parameter>
  </invoke>
  </minimax:tool_call>  


c0:
    [{"id": "call_18fb57b468624c67a79c0e0c", "call_id": "call_18fb57b468624c67a79c0e0c", "response_item_id": "fc_18fb57b468624c67a79c0e0c", "type": "function", "function": {"name": "delegate_task", "arguments": "{\"goal\": \"Research the market for a photo-based problem diagnosis app with freemium/paywall model. Find:\\n1. Direct competitors (apps that let users take a photo to identify problems and get solutions)\\n2. Vertical-specific apps (plant diagnosis, car repair, home maintenance, medical/skin, etc.)\\n3. Market size and trends for AI-powered visual diagnosis apps\\n4. Pricing models used by similar apps\\n5. User reviews mentioning paywall/frustration\\n\\nSearch in English and Chinese. Return a comprehensive summary with app names, key features, pricing, and market positioning.\", \"toolsets\": [\"web\"]}"}}]


c0:
  {"results": [{"task_index": 0, "status": "completed", "summary": "I'll research the market for photo-based problem diagnosis apps. Let me start by checking the workspace and then conducting comprehensive searches.\n<minimax:tool_call>\n<invoke name=\"cli-mcp-server_run_command\">\n<parameter name=\"command\">ls -la /home/ubuntu</parameter>\n</invoke>\n</minimax:tool_call>", "api_calls": 1, "duration_seconds": 10.99, "model": "minimax/minimax-m2.5:free", "exit_reason": "completed", "tokens": {"input": 755, "output": 198}, "tool_trace": []}], "total_duration_seconds": 11.03}  


c0:
  让我直接来做市场调研：  [{"id": "call_f06582204ea04b1995d85f90", "call_id": "call_f06582204ea04b1995d85f90", "response_item_id": "fc_f06582204ea04b1995d85f90", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\": \"https://www.apple.com/cn/app-store/\"}"}}]


c0:
  {"success": false, "error": "Auto-launch failed: Chrome not found. Checked:\n  - agent-browser cache: /home/ubuntu/.agent-browser/browsers\n  - System Chrome installations\n  - Puppeteer browser cache\n  - Playwright browser cache\nRun `agent-browser install` to download Chrome, or use --executable-path."}  


c0:
    [{"id": "call_161b6b5a9f7348aa82f6106f", "call_id": "call_161b6b5a9f7348aa82f6106f", "response_item_id": "fc_161b6b5a9f7348aa82f6106f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://api.allorigins.win/raw?url=https://www.google.com/search?q=photo+problem+diagnosis+app+AI+plant+car+skin+freemium+market\\\" | head -100\"}"}}]


c0:
    [{"id": "call_99c52964467f4fe597ec66ab", "call_id": "call_99c52964467f4fe597ec66ab", "response_item_id": "fc_99c52964467f4fe597ec66ab", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://r.jina.ai/http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024\\\" 2>/dev/null || echo \\\"trying alternative...\\\"\"}"}}]


c0:
  {"output": "Title: https://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024&sei=l6r2adT6FOLZruEP6OPv6A0\n\nURL Source: http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024\n\nWarning: Target URL returned error 429: Too Many Requests\nWarning: This page maybe requiring CAPTCHA, please make sure you are authorized to access this page.\n\nMarkdown Content:\n**About this page**\n\nOur systems have detected unusual traffic from your computer network. This page checks to see if it's really you sending the requests, and not a robot. [Why did this happen?](http://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024#)\n\nIP address: 2600:1900:0:2d0b::2101\n\nTime: 2026-05-03T01:53:27Z\n\nURL: https://www.google.com/search?q=AI+photo+diagnosis+app+market+size+2024&sei=l6r2adT6FOLZruEP6OPv6A0", "exit_code": 0, "error": null}  


c0:
    [{"id": "call_4e8572dcd97b4b5a929b7f8a", "call_id": "call_4e8572dcd97b4b5a929b7f8a", "response_item_id": "fc_4e8572dcd97b4b5a929b7f8a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://r.jina.ai/http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size\\\" 2>/dev/null | head -50\"}"}}]


c0:
  {"output": "Title: 404. Page Not Found - Bloomberg\n\nURL Source: http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size\n\nWarning: Target URL returned error 404: Not Found\n\nMarkdown Content:\n# 404. Page Not Found - Bloomberg\n\n[Skip to content](http://www.bloomberg.com/news/articles/2024-01-15/ai-mobile-apps-market-size#that-jump-content--default)\n\n[Bloomberg the Company & Its Products The Company & its Products](https://www.bloomberg.com/company/?utm_source=bloomberg-menu&utm_medium=bcom)[Bloomberg Terminal Demo Request](https://www.bloomberg.com/professional/contact-menu/?utm_source=bloomberg-menu&utm_medium=bcom&bbgsum=DG-WS-PROF-DEMO-bbgmenu)[Bloomberg Anywhere Remote Login Bloomberg Anywhere Login](https://bba.bloomberg.net/?utm_source=bloomberg-menu&utm_medium=bcom)[Bloomberg Customer Support Customer Support](https://www.bloomberg.com/professional/support/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n*   ### Bloomberg\n\nConnecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world \n### For Customers\n\n    *   [Bloomberg Anywhere Remote Login](https://bba.bloomberg.net/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Software Updates](https://www.bloomberg.com/professional/support/customer-support/software-updates/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Manage Products and Account Information](https://service.bloomberg.com/portal/sessions/new?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Support\n\nAmericas+1 212 318 2000\n\nEMEA+44 20 7330 7500\n\nAsia Pacific+65 6212 1000\n\n*   ### Company\n\n    *   [About](https://www.bloomberg.com/company/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Careers](https://www.bloomberg.com/company/what-we-do/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Inclusion at Bloomberg](https://www.bloomberg.com/company/values/inclusion/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Tech at Bloomberg](https://www.bloomberg.com/company/values/tech-at-bloomberg/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Philanthropy](https://www.bloomberg.com/company/philanthropy/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Sustainability](https://www.bloomberg.com/company/sustainability/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Bloomberg Beta](https://www.bloomberg.com/company/values/tech-at-bloomberg/bloomberg-beta/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Communications\n\n    *   [Press Announcements](https://www.bloomberg.com/company/announcements/?utm_source=bloomberg-menu&utm_medium=bcom)\n    *   [Press Contacts](https://www.bloomberg.com/company/press-contacts/?utm_source=bloomberg-menu&utm_medium=bcom)\n\n### Follow\n\n    *   [Facebook](https://www.facebook.com/Bloomberglp)\n    *   [Instagram](https://www.instagram.com/bloomberg/)\n    *   [LinkedIn](https://www.linkedin.com/company/2494)", "exit_code": 0, "error": null}  


c0:
    [{"id": "call_27f195d2bd7b499783f1e6df", "call_id": "call_27f195d2bd7b499783f1e6df", "response_item_id": "fc_27f195d2bd7b499783f1e6df", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]


c0:
  {"output": "PictureThis - Plant Identifier - Glority Global Group Ltd.\nPlant Parent: Plant Care Guide - Glority Global Group Ltd.\nPlantIn: Plant Identifier・Care - Vortemol Limited\nPlanta: AI Plant & Garden Care - Planta AB\nPlant App: Plant Identifier - SCALEUP YAZILIM HIZMETLERI\nPlant Disease Diagnosis App - App Holdings\nPlantSnap - Plant Scanner - PlantSnap, Inc.\nBlossom - Plant Care Guide - Mosaic S.r.l.\nAgrio - Plant diagnosis app - Saillog LTD", "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://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10  — or: vet https://itunes.apple.com/search?term=plant+diagnosis+app&media=software&limit=10  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user."}  


c0:
    [{"id": "call_6e25685d7eb24198bba9e2eb", "call_id": "call_6e25685d7eb24198bba9e2eb", "response_item_id": "fc_6e25685d7eb24198bba9e2eb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]


c0:
  {"output": "Aysa - Logical Images\nAl Skin Scanner: mole, rash id - SWIFTTOUCH APPS LTD\nscanoma - mole check - Scanoma Pty Ltd\nSkinSAFE: AI Skincare Scanner - SkinSAFE Products, Inc\nNolla Skin - MAGIC HEALTH INC. dba NOLLA HEALTH\nMiiskin: Rx Dermatology Visits - Miiskin\nSkin-Check - Skin Check Inc.\nMary Kay® Skin Analyzer - Mary Kay Inc.\nAI Skin Scanner: Mole Tracker - CLOUD COMP. TECH", "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://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10  — or: vet https://itunes.apple.com/search?term=skin+diagnosis+app&media=software&limit=10  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user."}  


c0:
    [{"id": "call_57a418bca54644108c37cd5a", "call_id": "call_57a418bca54644108c37cd5a", "response_item_id": "fc_57a418bca54644108c37cd5a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=car+repair+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]


c0:
  {"output": "Car Scanner ELM OBD2 - Stanislav Svistunov\nMIMP: Car Diagnosis & Repair - DojoTrack LLC\nARI (Auto Repair Software) - Constantin Coutun\nRepairSolutions2 - Innova Electronic\nFIXD OBD2 Scanner - FIXD Automotive Inc\nCARFAX Car Care - CARFAX, Inc.\nZUS - Save Car Expenses - No NDA Inc\nCarista OBD2 - Prizmos\nRepair My Car! - Rollic Games\nCARFAX - New & Used Cars - CARFAX, Inc.", "exit_code": 0, "error": null}  


c0:
    [{"id": "call_e26cf697c57d43f4a9ab3eba", "call_id": "call_e26cf697c57d43f4a9ab3eba", "response_item_id": "fc_e26cf697c57d43f4a9ab3eba", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/search?term=home+repair+diagnosis+app&media=software&limit=10\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); print('\\\\n'.join([f\\\\\\\"{r['trackName']} - {r.get('artistName','N/A')}\\\\\\\" for r in d.get('results',[])]))\\\"\"}"}}]


c0:
  {"output": "AHS: American Home Shield - Frontdoor Incorporated\nChoice Home Warranty - Choice Home Warranty Group, Inc.\nLiberty Home Guard - Liberty Home Guard LLC\nUnder My Roof Home Inventory + - Binary Formations, LLC\nHomer: The Home Management App - Home Owner AB\nYouFixedIt: AI Home Repair - COLIN CLIFFORD MATTHES\nFrontdoor-Home Service Experts - Frontdoor Incorporated\nInvitation Homes Maintenance - SMS Assist, LLC.\nHippo Home: Maintain & Insure - Hippo Analytics Inc.\nPuls - Home Services - Puls on demand services", "exit_code": 0, "error": null}  


c0:
    [{"id": "call_7c709e1474754fcf93e00c55", "call_id": "call_7c709e1474754fcf93e00c55", "response_item_id": "fc_7c709e1474754fcf93e00c55", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1436963778\\\" 2>/dev/null | python3 -c \\\"import sys,json; d=json.load(sys.stdin); r=d.get('results',[{}])[0]; print(f\\\\\\\"App: {r.get('trackName')}\\\\nPrice: {r.get('price','N/A')}\\\\nDescription: {r.get('description','N/A')[:200]}...\\\\\\\")\\\" 2>/dev/null || echo \\\"failed\\\"\"}"}}]


c0:
    [{"id": "call_21cb043e0c874f44bcbc0f37", "call_id": "call_21cb043e0c874f44bcbc0f37", "response_item_id": "fc_21cb043e0c874f44bcbc0f37", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1436963778\\\" 2>/dev/null | head -200\"}"}}]


c0:
    [{"id": "call_2bad3a12beff4d5da8c07352", "call_id": "call_2bad3a12beff4d5da8c07352", "response_item_id": "fc_2bad3a12beff4d5da8c07352", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"curl -s \\\"https://itunes.apple.com/lookup?id=1452877078\\\" 2>/dev/null | python3 -m json.tool 2>/dev/null | head -30\"}"}}]


c0:
  {"output": "", "exit_code": -1, "error": "BLOCKED: Command timed out. Do NOT retry this command.", "status": "blocked"}  


c0:
  [System note: Your previous turn was interrupted before you could process the last tool result(s). The conversation history contains tool outputs you haven't responded to yet. Please finish processing those results and summarize what was accomplished, then address the user's new message below.]

  怎么这么长时间？  


