AI Ready

⚠️ Experimental — Under active development. It works, but expect rough edges. Feedback and contributions welcome.

A Copilot CLI skill that analyzes your repository and generates the configuration files AI agents need to contribute correctly. GitHub-native — it auto-discovers your repo's context, community health, and PR review patterns without you explaining anything.

Quick Start

Install the skill from inside Copilot CLI:

/skills add johnpapa/ai-ready

Then type:

make this repo ai-ready

The skill analyzes your code, CI, tests, docs, and structure, then generates assets customized to your project — not generic templates.

Run it again anytime

The skill is safe to re-run. On the first run, it creates missing assets. On subsequent runs, it audits your existing AI-ready files against the current state of your codebase — flagging drift like outdated build commands, stale repo structure, or new PR review patterns that should become conventions. It never overwrites your files — it suggests updates and lets you decide.

Keeping updated

Re-run the command to pull the latest version:

/skills add johnpapa/ai-ready

Skip what you don't need

Add exclusions to your prompt and the skill will respect them:

make this repo ai-ready but skip CI and issue templates

just generate AGENTS.md and copilot-instructions

Report only

Just want to see where you stand? Ask for a report without generating any files:

how ai-ready is this repo?

score this repo

What to Expect

After you run the skill, you get a full AI-readiness report — analysis, proposed changes, and a projected score. Here's what it looks like for vscode-peacock:

🔗 View the interactive version — collapsible sections, responsive layout, works on mobile.

The report shows:

1. Your Repo Today — current score, what's nailed, what's missing, and why it matters
2. What I'd Like To Do — proposed files to create (nothing changes until you say so)
3. If You Accept — projected score with category breakdown
4. Ready? — offer to create a PR with all the changes

📄 Also available as terminal output — same content, rendered in the CLI.

Scoring

Your score is simple: how many of the 12 tracked assets are nailed. That's it — no formulas, no weights.

Medal	Name	Count	What it means
🥉	Getting Started	1–4 of 12	Basics are in place but AI agents are mostly guessing
🥈	On Track	5–7 of 12	AI agents can help but they'll miss your conventions
🥇	Solid	8–10 of 12	AI agents follow your patterns and catch most expectations
🏆	AI-Ready	11–12 of 12	AI agents contribute like your best team members

Why

Contributors (human and AI) show up to your repo and don't know the conventions. They submit PRs that miss tests, break patterns, skip docs. You leave the same review comments on every PR. AI agents make this more challenging — they generate PRs faster, but without context, those PRs create more review burden.

It's the same gap from both sides: contributors don't know what maintainers expect, and maintainers keep re-teaching it. This skill closes that gap by generating repo-level configuration that teaches everyone — human and AI — how to work in the repo correctly. It even mines your PR review comments for repeated feedback and turns them into automated conventions. The result: a 45-minute review becomes a 5-minute review.

Built from Real Maintainer Experience

This skill isn't theoretical — it's shaped by John Papa's experience maintaining popular open source projects and repos at large enterprises. The skill is tuned to prioritize what actually reduces review burden: maintenance matrices that catch the files contributors always forget, conventions mined from the PR feedback you're tired of repeating, and CI that catches problems before you have to.

How It Works — GitHub-Native by Default

You shouldn't have to explain to an AI tool that you're in a GitHub repo. This skill assumes it, and leverages everything GitHub already knows about your project.

Auto-Discovery (zero user input)

The skill starts by pulling context directly from GitHub — no questions asked:

What it discovers	How	Why it matters
Repo description, topics, languages	GitHub API	Knows what your project is without reading every file
Community health score	GitHub API	Instantly knows which config files are missing
Contributors	GitHub API	Team size, contribution patterns
Recent merged PRs	GitHub API	Understands what typical contributions look like
PR review comments	GitHub API	Turns your repeated review feedback into automated conventions
CI/CD workflows	GitHub Actions API	Knows your build/test pipeline
Releases	GitHub API	Understands versioning and release cadence

It then scans your local codebase for deeper details — manifest files, test configs, directory structure, existing AI configuration — and combines both into a complete picture.

PR Review Mining — The Killer Feature

This is the highest-value thing the skill does. It reads your recent PR review threads and looks for repeated feedback — the same comments you leave on every PR:

• "Please add tests for new features" → becomes a test convention rule
• "Use the X pattern instead of Y" → becomes a coding convention rule
• "Don't forget to update the changelog" → becomes a maintenance matrix entry
• "This breaks on mobile, check responsive layout" → becomes a screen size rule

These mined conventions go directly into copilot-instructions.md. The next AI-generated PR follows those rules automatically. You stop repeating yourself.

What Gets Generated

Every file is customized to your repo's actual language, framework, and patterns — not generic boilerplate. The skill only creates files that don't already exist.

File	What It Does
AGENTS.md	Project context for the coding agent — repo structure, build/test commands, architectural decisions, how to add features
.github/copilot-instructions.md	Coding conventions for all Copilot interactions — Chat, completions, PR reviews, CLI. Includes a maintenance matrix of what to update when code changes
.github/workflows/copilot-setup-steps.yml	Cloud agent environment setup — runtime versions, dependencies, build steps
.github/workflows/ci.yml	PR validation pipeline — build, test, lint, typecheck. Skips non-code changes (docs, images, etc.)
.github/ISSUE_TEMPLATE/bug-report.yml	Structured bug report form with fields relevant to your project type
.github/ISSUE_TEMPLATE/feature-request.yml	Structured feature request form
.github/PULL_REQUEST_TEMPLATE.md	PR description template with checklist items derived from the maintenance matrix
CHANGELOG.md	Keep a Changelog format, populated from releases/tags if available
.vscode/mcp.json	MCP server config connecting AI agents to your project's databases, APIs, and tools
README ## Contributing section	Onramp for new contributors — how to fork, build, test, and submit a PR
AI-Ready badge in README	Shields.io badge linking back to this skill — added automatically

Two Layers of PR Quality

The assets this skill generates enable two complementary layers of PR quality — one you get automatically, one you enable:

Layer	What it catches	How it works
CI workflow (generated by this skill)	Broken builds, failing tests, lint errors	GitHub Actions runs on every PR — validates that the code compiles and tests pass
Copilot code review (you enable this)	Convention violations, missing docs/tests, maintenance matrix gaps	Copilot reads copilot-instructions.md (generated by this skill) and reviews PRs against your conventions

Together: PRs are validated for correctness (CI) and reviewed for quality (Copilot). This skill generates the inputs for both — the CI workflow and the conventions file that Copilot code review reads.

To enable Copilot code review: go to your repo's Settings → Copilot → Code review and turn it on. Once enabled, every PR is automatically reviewed against the conventions in copilot-instructions.md.

Contributing

Quick Start

1. Fork this repo and create a branch
2. Make your changes (skills or docs)
3. Test locally: copy skills/ai-ready/SKILL.md to ~/.copilot/skills/ai-ready/SKILL.md, start copilot, then say "make this repo ai-ready"
4. Open a PR

See AGENTS.md for the full contributor guide.

License

MIT