Borrowing it
Nothing to install: this file belongs to cyzlmh/agentype. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cyzlmh/agentype/main/AGENTS.mdgit clone --depth 1 https://github.com/cyzlmh/agentypeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/cyzlmh/agentype/agents-md)<a href="https://agentmods.dev/instructions/cyzlmh/agentype/agents-md"><img src="https://agentmods.dev/badge/instructions/cyzlmh/agentype/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/cyzlmh/agentype/agents-md"><img src="https://agentmods.dev/badge/instructions/cyzlmh/agentype/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00680 | $0.00680 |
| Opus 5.5 | $0.00272 | $0.00272 |
| Sonnet 5.5 | $0.00136 | $0.00136 |
| Haiku 4.5 | $0.00068 | $0.00068 |
Grade A, and why
agentype AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Guidelines
Project Structure & Module Organization
Agentype is a Python CLI package under src/agentype/. The command entry point is src/agentype/__init__.py, wired through pyproject.toml as agentype = "agentype:main". Data collection lives in collector.py; per-agent adapters live in src/agentype/sources/ for Claude Code, Codex, OpenCode, pi-agent, Gemini CLI, OpenClaw, Nanobot, and configured Nanobot-compatible roots. Statistics, domain scoring, and archetype selection live in analysis.py.
Project documentation is in README.md, SKILL.md, and RELEASE.md. ROADMAP.md is a local planning file and is intentionally ignored. Tests are in tests/. Runtime artifacts are written to output/ and are intentionally ignored.
Build, Test, and Development Commands
Use uv for dependency management and command execution.
uv syncinstalls dependencies fromuv.lock.uv run pytestruns the parser and token usage test suite.uv run agentypeprints the end-user usage overview and archetype.uv run agentype -vadds intermediate statistics, domain evidence, and data confidence tables.uv run agentype --json-outwrites the full analysis tooutput/agentype.json.uv buildbuilds the package usinguv_build.
If you add developer tools such as ruff or mypy, declare them in pyproject.toml and run them through uv run.
Coding Style & Naming Conventions
Write idiomatic Python 3.12 with 4-space indentation and type hints for public functions or non-obvious data shapes. Use snake_case for functions, variables, and modules; use PascalCase for dataclasses such as SkillInfo, SessionEvent, and TokenUsage.
Keep modules focused on their existing responsibilities. Prefer small functions over new abstractions unless the abstraction removes repeated logic. Match the current style: standard-library imports first, third-party imports next, then local imports.
Testing Guidelines
Use pytest and place test files under tests/ with names like test_sources.py, test_token_usage.py, or test_analysis.py. Prefer fixture files or small inline JSON/SQLite samples over reading real local agent history. Focus on deterministic logic: transcript parsing, token usage summarization, statistics, domain scoring, and archetype selection. Avoid tests that require live LLM calls; mock agentype.llm.chat instead.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 47 lines · 680 tokens per session scan A ba298012e6c2
agentype AGENTS.md is an instructions file published in the GitHub repository cyzlmh/agentype (2 stars, last pushed 6mo ago), licensed MIT. It adds 680 tokens to every session, about $0.0027 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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