Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →johnpapa/ai-ready
**ANALYSIS SKILL** — Analyze any repository and generate AI-ready configuration — AGENTS.md, copilot-instructions.md, skills, CI workflows, issue templates. WHEN: "make this repo ai-ready", "set up AI config", "add copilot instructions", "prepare this repo for AI contributions", "generate AGENTS.md". INVOKES: glob, grep, view, create, edit for repo analysis and file generation. FOR SINGLE OPERATIONS: use create/edit directly for individual config files.
Passed 17 of 18 test cases with the skill, and 11 of 18 without it.
and 3 more in the full report
6 fixed11 passed either way1 still failing0 broke4 errored
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have a pure frontend utility library for string manipulation with zero authentication, network calls, or secret handling. Should we generate a security review skill in .github/skills/?
| Checked | Without skill | With skill |
|---|---|---|
| The response advises omitting the security skill when no security surface exists. | Missed | Passed |
The instructions your model reads when the skill loads.
Adopt the perspective of an experienced repo maintainer. Prioritize what reduces review burden and contributor friction. Every file you generate should earn its place — generic boilerplate creates noise.
Follow these steps in order to analyze the current repository and generate all missing AI-ready configuration assets.
First run vs. re-run: On the first run, most assets will be missing — the skill creates them. On re-runs, it audits existing assets against the current codebase, checking for drift, stale content, and new conventions from recent PR reviews.
Skipping assets: If the user's prompt mentions skipping specific assets, respect those exclusions. Still run the full analysis, but skip generation for the excluded assets.
Report-only mode: If the user asks for a report without generating files (e.g., "how ai-ready is this repo?", "score this repo"), run the full analysis (Steps 0–1) and display the report (Step 11) — but skip all generation steps (Steps 2–10).
Assets are grouped into three categories. Count assets with Nailed It status for the score.
🤖 AI Context — what AI agents read to understand your repo
| # | Asset | Generated in |
|---|---|---|
| 1 | AGENTS.md | Step 2 |
| 2 | Per-tool pointer files (.github/copilot-instructions.md, CLAUDE.md, …) | Step 3 |
| 3 | Maintenance matrix (in AGENTS.md) | Step 2 |
| 4 | Reviewer agents (.github/agents/) | Step 4c |
| 5 | Starter skill (.github/skills/) | Step 4d |
| 6 | Security skill (.github/skills/, when there is surface) | Step 4e |
🔧 Dev Workflow — what keeps PRs clean and contributors on track
| # | Asset | Generated in |
|---|---|---|
| 7 | CI workflow (.github/workflows/ci.yml) | Step 5 |
| 8 | Issue templates (.github/ISSUE_TEMPLATE/) | Step 6 |
| 9 | PR template (.github/PULL_REQUEST_TEMPLATE.md) | Step 6 |
📖 Onboarding — what helps new contributors get started
test cases passed, vs 11 / 18 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.