What is agentready AGENTS.md?

A command-line tool that checks how ready a code repository is for work assisted by AI. It scans the repository, produces findings, scores them, and can set up or fix parts of the required structure.

Use it to assess a repository, bootstrap agent-ready files and setup, apply automated alignment fixes, run tests, and check code quality.

It helps reveal missing documentation, tests, or project practices that make AI-assisted development less reliable. It also provides a consistent way to assess improvements.

How ambient-code/agentready configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to ambient-code/agentready. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/ambient-code/agentready/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/ambient-code/agentready

Made for: Codex, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/ambient-code/agentready/agents-md/github.svg)](https://agentmods.dev/instructions/ambient-code/agentready/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/ambient-code/agentready/agents-md"><img src="https://agentmods.dev/badge/instructions/ambient-code/agentready/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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/ambient-code/agentready/agents-md"><img src="https://agentmods.dev/badge/instructions/ambient-code/agentready/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 615 This file is loaded in full into every session.

When invoked 615 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00615	$0.00615
Opus 5.5	$0.00246	$0.00246
Sonnet 5.5	$0.00123	$0.00123
Haiku 4.5	$0.00061	$0.00061

Measured 2mo ago against content hash f5a5c4d96a01, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agentready 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.

AGENTS.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AgentReady

Assess repositories against evidence-based attributes for AI-assisted development readiness.

Commands

# Core
agentready assess <repo>           # Assess repository
agentready bootstrap <repo>        # Setup agent-ready infrastructure
agentready align <repo>            # Automated remediation

# Development
pytest                             # Run tests
pytest --cov=src/agentready        # With coverage
black . && isort . && ruff check . # Lint

# Local development (uses this checkout, not the installed package)
PYTHONPATH=src python -m agentready assess <repo>

Data Flow

Repository -> Scanner -> Assessors -> Findings -> Scorer -> Reporters

Scoring

Certification levels: Platinum (90+), Gold (75-89), Silver (60-74), Bronze (40-59)

Adding Assessors

Create class inheriting BaseAssessor (assessors/base.py)
Implement attribute_id property and assess(repository) method
Register in assessors/__init__.py:create_all_assessors()
Add tests in tests/unit/test_assessors_*.py

Key Patterns

Proportional scoring: Use calculate_proportional_score() for partial compliance

Graceful degradation: Return "skipped" if tools missing, never crash

Finding creation:

Finding.create_pass(self.attribute, evidence="...", details="...")
Finding.create_fail(self.attribute, evidence="...", remediation="...")

Conventions

Commits: feat:, fix:, docs:, test:, refactor:, chore:

Tests: All new assessors require unit tests. Maintain >80% coverage for new code.

Linting: Run black . && isort . && ruff check . before commits

CI changes: Always run actionlint before pushing workflow changes.

Agent Guidelines

Read before modifying: understand existing assessors first
Follow patterns: use reference implementations (grep for BaseAssessor subclasses)
Test thoroughly: unit tests required for all assessors
Backwards compatibility: schema version bump for model changes
Rich remediation: actionable steps with tools, commands, examples
Self-assign issues: before starting work on a GitHub issue, check if it is already assigned. If it is, warn the user and ask for confirmation before continuing. If unassigned (or the user confirms), self-assign it immediately to prevent duplicate effort from other contributors
Keep docs/attributes.md in sync: when changing how an assessor scores (thresholds, partial credit rules, recognized paths, pass/fail conditions), update the corresponding entry in docs/attributes.md in the same PR

Read the full file on GitHub · 68 lines

Changes

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 · 68 lines · 615 tokens per session scan A f5a5c4d96a01

Subscribe to this mod's changes

agentready AGENTS.md is an instructions file published in the GitHub repository ambient-code/agentready (154 stars, last pushed 3d ago), licensed MIT. It adds 615 tokens to every session, about $0.0025 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 2026-08-30.

Python ai-assisted-development assessment-tool code-quality

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 9,715 tokens

agentready: Instructions file for Codex