Use when creating or updating AGENTS.md files, .github/copilot-instructions.md, or other AI agent rule files, onboarding AI agents to a project, standardizing agent documentation, or when anyone mentions AGENTS.md, agent rules, project onboarding, or codebase documentation for AI agents.

AGENTS.md Generator Skill Generate and maintain AGENTS.md files following the [agents.md convention](https://agents.md/). AGENTS.md is FOR AGENTS, not humans. When to Use Creating or updating AGENTS.md for new/existing projects Scaffolding a new repository — ship AGENTS.md with the initial commits; retrofitting later needs full re verification Standardizing agent documentation across repositories Checking AGENTS.md freshness after code changes Onboarding AI agents to an unfamiliar codebase Scripts Call every script by its full path: bash ${CLAUDE SKILL DIR}/scripts/<name PATH . Calling one relative to the working directory is not covered by the frontmatter rule and raises a permission prompt per call. Script Purpose generate agents.sh PATH Generate AGENTS.md files validate structure.sh PATH Validate structure compliance check freshness.sh PATH Check if files are outdated verify content.sh PATH Verify documented files/commands match codebase verify commands.sh PATH Verify documented commands execute score agents.sh PATH Grade AGENTS.md quality, worst first detect project.sh PATH Detect language, version, build tools detect scopes.sh PATH Identify directories needing scoped files extract commands.sh PATH Extract commands from build configs extract ci rules.sh PATH Extract CI quality gates and version matrix extract architecture rules.sh PATH Extract module boundaries extract adrs.sh PATH Extract architectural decision records extract github rulesets.sh PATH Extract GitHub rulesets and merge rules See references/scripts guide.md for full options. Workflow 1. Detect : detect project.sh + detect scopes.sh — stacks and subsystems 2. Extract : extract commands.sh , extract ci rules.sh — gather facts 3. Generate : generate agents.sh style=thin (default) or verbose 4. Verify : verify content.sh + verify commands.sh MANDATORY before done update preserves curated content outside <! GENERATED markers. Core Principles Structured over Prose tables parse faster than paragraphs Never Fabricate only document what exists; verify every command and path Pointer Principle point to files, don't duplicate content Auto Symlinks CLAUDE.md/GEMINI.md by default ([ ai tool compatibility.md ](references/ai tool compatibility.md)) References File Contents [ verification guide.md ](references/verification guide.md) Verification steps, anti bloat, preservation check [ fleet sync sweep.md ](references/fleet sync sweep.md) Fleet wide AGENTS.md sweeps [ scripts guide.md ](references/scripts guide.md) Script options, validation checklist [ quality rubric.md ](references/quality rubric.md) Grading rubric [ ai tool compatibility.md ](references/ai tool compatibility.md) 16 agent compatibility matrix [ output structure.md ](references/output structure.md) Root/scoped sections [ git hooks setup.md ](references/git hooks setup.md) Hook framework setup [ examples/ ](references/examples/) Complete examples [ ai contribution guidelines.md ](references/ai contribution guidelines.md) "3 Cs" AI contribution framework [ directory coverage.md ](references/directory coverage.md) Scoped file coverage rationale [ feedback memory schema.md ](references/feedback memory schema.md) Approved learning file format Templates Root: assets/root thin.md (default) or root verbose.md . Scoped: assets/scoped/ , one per stack (Go/PHP/Python/TYPO3/Symfony/Oro/CLI/TS/skill repo). Supported Projects Go, PHP (Composer/Laravel/Symfony/TYPO3/Oro), TypeScript (React/Next/Vue/Node), Python (pip/poetry/ruff/mypy), skill repos, hybrid. See Also [ agent harness skill ](https://github.com/netresearch/agent harness skill) — agent readiness harness (CI enforcement). [ skill repo skill ](https://github.com/netresearch/skill repo skill) — skill repo structure (plugin.json, licensing, releases).

agent-rules