| 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:
agentype --json-out
If agentype is not installed and there is no source checkout:
uvx --from agentype-cli agentype --json-out
From a source checkout:
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:
agentype --json-in output/agentype.json
For chat, IM, or gateway environments that can display images, also generate the poster:
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:
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:
agentype -v --json-out
