Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add eugeniughelbur/agents-md --skill agents-mdgit clone --depth 1 https://github.com/eugeniughelbur/agents-mdWrote 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/skills/eugeniughelbur/agents-md/agents-md)<a href="https://agentmods.dev/skills/eugeniughelbur/agents-md/agents-md"><img src="https://agentmods.dev/badge/skills/eugeniughelbur/agents-md/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/skills/eugeniughelbur/agents-md/agents-md"><img src="https://agentmods.dev/badge/skills/eugeniughelbur/agents-md/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 | $0.00174 | $0.01837 |
| Opus 5.5 | $0.00070 | $0.00735 |
| Sonnet 5.5 | $0.00035 | $0.00367 |
| Haiku 4.5 | $0.00017 | $0.00184 |
Grade A, and why
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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agents-md
Generate and maintain a high-quality AGENTS.md for the current repository, then wire
the other agent tools to it. AGENTS.md is the converging cross-tool standard (OpenAI 2025,
now Linux Foundation / Agentic AI Foundation governance, 60k+ repos). One AGENTS.md is read
by most coding agents; CLAUDE.md and others are pointed at it via symlink.
This skill is safe to re-run. It only rewrites the regions it generated (marked with HTML comments) and never overwrites content a human wrote. That guarantee is the whole point.
When invoked, do this in order
1. Locate the repo and take stock
- Repo root:
git rev-parse --show-toplevel(fall back to the current directory). - Detect existing agent files:
AGENTS.md,CLAUDE.md,.cursor/rules/or.cursorrules,.github/copilot-instructions.md,.windsurfrules,GEMINI.md. - If
AGENTS.mdexists, check whether it contains this skill's markers (<!-- agents-md:begin ... -->). That decides the write mode in step 4.
2. Gather facts from the repo (never guess — read)
Read, don't assume. Pull real values from these sources:
| Need | Read |
|---|---|
| Language / framework | package.json, pyproject.toml / requirements.txt, go.mod, Cargo.toml, Gemfile, composer.json, pom.xml, *.csproj |
| Build / test / run / lint commands | package.json scripts, Makefile, Taskfile, justfile, tox.ini, pyproject [tool.*] |
| Project / file structure | top-level dirs + the main source dir (src/, app/, lib/, cmd/) |
| Git workflow | default branch (git symbolic-ref refs/remotes/origin/HEAD), last ~15 commits for message/branch conventions, CONTRIBUTING.md |
| Deploy / runtime | Dockerfile, .github/workflows/, railway.json/railway.toml, vercel.json, fly.toml, Procfile |
| Env / secrets | .env.example (NEVER read or copy real .env values) |
| Overview | README.md (summarize, do not duplicate it wholesale) |
For a monorepo, generate one root AGENTS.md and offer per-package AGENTS.md in each workspace (agents read the nearest one).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 113 lines · 174 tokens per session scan A 4b5366ab812d
agents-md is a skill published in the GitHub repository eugeniughelbur/agents-md (12 stars, last pushed 4mo ago), licensed MIT. It adds 174 tokens to every session and 1,837 once invoked, about $0.0007 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.
agents-md compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agents-mdeugeniughelbur/agents-md | Skill | 12 | 4mo ago | MIT | A | 174 | Claude Code, Codex |
| firebase-messagingevanca/flutter-ai-rules | Skill | 669 | yesterday | MIT | A | 35 | Claude Code |
| firebase-cloud-functionsevanca/flutter-ai-rules | Skill | 669 | yesterday | MIT | A | 36 | Claude Code |
| karpathy-llm-wikiAstro-Han/karpathy-llm-wiki | Skill | 2.7k | 3mo ago | MIT | A | 67 | Claude Code, Codex |
| skillnoteluna-prompts/skillnote | Skill | 68 | 5d ago | MIT | A | 54 | Claude Code, Codex |
| agronomistK-Dense-AI/scientific-agents | Skill | 237 | 1mo ago | MIT | A | 97 | Claude Code |
| algebraistK-Dense-AI/scientific-agents | Skill | 237 | 1mo ago | MIT | A | 113 | Claude Code |