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 agentmods add instructions/uky0yang/agent-rules-lint/agents-mdgit clone --depth 1 https://github.com/Uky0Yang/agent-rules-lintWrote 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/uky0yang/agent-rules-lint/agents-md)<a href="https://agentmods.dev/instructions/uky0yang/agent-rules-lint/agents-md"><img src="https://agentmods.dev/badge/instructions/uky0yang/agent-rules-lint/agents-md.svg" alt="Measured on agentmods" 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 | $0.00165 | $0.00165 |
| Opus 5 | $0.00082 | $0.00082 |
| Sonnet 5 | $0.00033 | $0.00033 |
| Haiku 4.5 | $0.00016 | $0.00016 |
Grade A, and why
agent-rules-lint 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 4d 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.
What it actually says
Agent Instructions
Purpose
Maintain agent-rules-lint, a dependency-free Python CLI for linting AI agent instruction files.
Scope
These instructions apply to code, tests, documentation, and GitHub workflow files in this repository.
Commands
Run the standard checks before publishing:
python -m unittest discover -s tests
python -m agent_rules_lint .
Safety
- Do not add network calls to the default lint path.
- Do not add model calls to the default lint path.
- Do not commit API keys, access tokens, cookies, private credentials, or generated build output.
- Keep checks deterministic so they are safe for CI.
Style
- Prefer standard-library Python.
- Keep rule messages short and actionable.
- Add tests when changing lint behavior.
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.
- 4d ago First seen · 32 lines · 165 tokens per session scan A 50cd3beacc65
agent-rules-lint AGENTS.md is an instructions file published in the GitHub repository Uky0Yang/agent-rules-lint (1 stars, last pushed 28d ago), licensed MIT. It adds 165 tokens to every session, about $0.0008 per session on Opus 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.
Other instructions, from other repositories
ios-agent-skill AGENTS.md
Instructions for Nagarjuna2997/ios-agent-skill, covering ios agent skill — claude ai expert ios/swift developer, when to load this skill, loading the right document, how these docs are structured and how you operate: delegation, loops, and verification.
agent-toolkit AGENTS.md
AGENTS.md instructions for ulises-jeremias/agent-toolkit, covering agents.md — ai agent contract, what this toolkit does, repository structure, operating rules and how to add a skill.
humanize AGENTS.md
AGENTS.md instructions for shir-danishyar/humanize: This repository is an Agent Skills-format skill. To use it.
bluetemberg CLAUDE.md
Instructions for prototypdigital/bluetemberg, a project described as: AI coding standards shipped like a dependency. Version-locked, integrity-verified, signed, and routed by role and stack version. Emits AGENTS.md, Cursor rules, and more.
Personetta CLAUDE.md
Instructions for EdwardAF-IT/Personetta, covering repository instructions, commit attribution and quality gates.
ultra-max-token-burner AGENTS.md
Instructions for floytra-dev/ultra-max-token-burner: When the user asks to expand, operationalize, professionalize, govern, validate, or make a prompt more manager-visible, use the Ultra Max Token Burner workflow in SKILL.md.