Skill: cyzlmh/agentype (version 0.1.8)

--- name: agentype description: >- Run the Agentype workflow for local AI-agent usage analysis: collect and cache deterministic JSON, infer a persona/archetype from aggregate usage signals, then render a terminal summary or PNG poster. Supports Claude Code, Codex, OpenCode, pi-agent, Gemini CLI, OpenClaw, Nanobot, and configured Nanobot-compatible roots. Use when the user asks to understand their agent usage, AI workflow, token footprint, preferred agents/models/projects, or "agentype". version: 0.1.8 tags: [ai-agents, analytics, persona, tokens, local-first] --- # Agentype Agentype summarizes a user's local AI-agent history into a persona/archetype and usage overview. **When this skill is triggered, you MUST complete all four steps below.** Do not stop after collecting stats, do not skip persona inference, and do not skip delivering the final poster or summary to the user. ## When to Use Use this skill when the user asks: - "what is my agentype?" - "analyze my agent usage" - "show my AI usage stats" - "which agents or models do I use most?" - "what persona am I based on my AI workflow?" - `/agentype` Do not use it for billing estimates. Agentype reports tokens and local usage signals, not provider invoices. ## What It Reads Agentype collects local session and token metadata from supported agents where available: - Claude Code - Codex - OpenCode - pi-agent - Gemini CLI - OpenClaw - Nanobot - Nanobot-compatible JSONL roots configured through `AGENTYPE_NANOBOT_ROOTS` ## Required Workflow (all four steps are mandatory) The PyPI distribution is `agentype-cli` because `agentype` is not available on PyPI. The installed command is still `agentype`. ### Step 1 — Collect stats Run the CLI with `--json-out` to collect deterministic usage data and write it to `output/agentype.json`: ```bash agentype --json-out ``` If `agentype` is not installed and there is no source checkout: ```bash uvx --from agentype-cli agentype --json-out ``` From a source checkout: ```bash uv run agentype --json-out ``` > The CLI output at this point is raw stats only — it is **not** the final result. Continue to the next step. ### Step 2 — Infer and fill the persona (agent-side, no CLI call) Read `output/agentype.json`. From the aggregate signals — top projects, agents, models, skill metadata, token shape, and usage rhythm — infer the user's persona yourself. Then write these four top-level fields back into `output/agentype.json`, preserving all other fields: - `archetype`: short persona label (e.g. "Polyglot Automator"). - `description`: one-line explanation of the archetype. - `keywords`: 3–6 concise keywords. - `comment`: 2–3 evidence-grounded sentences starting with "You are a...". ### Step 3 — Render the filled JSON Pass the updated file back to the CLI to produce the final formatted output: ```bash agentype --json-in output/agentype.json ``` For chat, IM, or gateway environments that can display images, also generate the poster: ```bash agentype --json-in output/agentype.json --png-out ``` ### Step 4 — Deliver to the user - **Terminal agents**: relay the full rendered text output (persona/archetype + top stats) directly to the user. - **Chat or IM gateway agents**: send a compact text summary and attach `output/agentype.png`. Do not expose raw session files, prompts, private transcripts, or full JSON unless the user explicitly asks for debugging data. ## Custom Local Paths If the user's agent history lives outside default locations, configure `AGENTYPE_NANOBOT_ROOTS` before Step 1: ```bash AGENTYPE_NANOBOT_ROOTS="/path/to/workspace:/path/to/another/root" agentype --json-out ``` For unsupported agent layouts, the collector paths live in `src/agentype/paths.py` and source adapters in `src/agentype/sources/`. ## Debugging If the user asks for debugging or validation, re-run Step 1 with `-v` and share the verbose output: ```bash agentype -v --json-out ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)