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/chevy155/agent-readiness/agents-mdgit clone --depth 1 https://github.com/chevy155/agent-readinessWhat 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 | $0.01365 | $0.01365 |
| Opus 5 | $0.00682 | $0.00682 |
| Sonnet 5 | $0.00273 | $0.00273 |
| Haiku 4.5 | $0.00136 | $0.00136 |
Grade A, and why
agent-readiness 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 3d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — agent-readiness
Operational guidance for AI coding agents working in this repository. Applies to: Cursor, Claude Code, GitHub Copilot, Codex, and local agents.
Project Purpose
agent-readiness is the Agent Readiness Scanner — a deterministic Python CLI that checks whether
a repository is ready for AI coding agents. It scans for governance files, CI configuration,
test coverage signals, documentation, and safety patterns.
The product goal is a self-serve, hands-off tool. It must require no consulting, no manual intervention, and no external services to reach first value.
Current version: v0.3.0 — CLI only.
v0.2 priority is bounded: critical-failure visibility and positioning clarity only. Do not expand the product while working this release.
Allowed Changes
Agents are explicitly permitted to:
- Fix bugs in existing check functions when a test identifies a clear failure
- Add new tests to
tests/using thetmp_pathpytest fixture - Improve docstrings, type annotations, and inline comments
- Refactor within a single module without changing public function signatures
- Fix linter warnings (ruff, pyright) without changing behavior
- Update the README if user-facing behavior changes
- Add new check functions to
checks.pyif they follow theCheckResultTypedDict contract
Forbidden Changes
Agents must not make the following changes without explicit operator approval:
- Add any runtime dependency to
pyproject.toml - Add network calls, HTTP requests, or socket operations of any kind
- Add LLM API calls, model loading, or AI inference
- Add telemetry, analytics, crash reporting, or usage tracking
- Modify
.github/workflows/test.yml(CI is operator-controlled) - Remove or rename the
agent-scanCLI entry point - Change the
CheckResultTypedDict structure in a breaking way - Add SaaS, auth, dashboard, billing, or GitHub App features
- Expand scope to include Token Burn Firewall or Repo Red Cell Bot
Before adding any new feature, check docs/LAUNCH_LOG.md and the latest
reports/OPS_REPORT_*.md. If launch feedback does not support the feature,
do not build it without explicit operator approval.
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.
- 3d ago First seen · 171 lines · 1,365 tokens per session scan A e30204b09c91
agent-readiness AGENTS.md is an instructions file published in the GitHub repository chevy155/agent-readiness (4 stars, last pushed 3mo ago), licensed MIT. It adds 1,365 tokens to every session, about $0.0068 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
meta-llm-charter CLAUDE.md
Instructions for entropyvortex/meta-llm-charter, covering meta v3.1 core charter, bias, meta-0, r1 decompose and r2 ask gate.
awesome-reviewers CLAUDE.md
Instructions for baz-scm/awesome-reviewers, covering awesome reviewers — repository guidelines, what this project is, source of truth, site layer and machine interface.
code-context AGENTS.md
Instructions for infino-ai/code-context, covering code-context: notes for ai agents, project overview, repo map, build, test, gates and conventions.
gtm-cheat-codes CLAUDE.md
Instructions for zapier/gtm-cheat-codes, covering repo conventions and commits.
Stata-CLI AGENTS.md
Instructions for Utolaris/Stata-CLI, covering agents.md, project structure and working rules.
waybill AGENTS.md
Instructions for wardmos/waybill, covering agents.md, privacy, checks, commit messages and git history.