Over the past year, we've run 30+ GitHub Copilot workshops used by multiple companies and hundreds of participants.

One thing we learned: GitHub Copilot isn't just for developers.

Product Managers, QA Engineers, Developers, Team Leads, and even non-technical roles can get massive value from learning how to work effectively with AI.

So we decided to open source the entire workshop:

GitHub Copilot Workshop
View the repository

Inside you'll find hands-on exercises, real-world use cases, prompting techniques, agent workflows, testing, documentation, and more.

Whether you're learning Copilot yourself or running enablement sessions for your team, feel free to use it.

If you find it useful, give it a star. ⭐
If we're missing something, open an issue or share your feedback.

Over the last year, I've spent a lot of time helping teams adopt GitHub Copilot, and one thing became pretty obvious:

Some repositories seem to unlock Copilot's full potential.

Others have all the right intentions, documentation, instructions, and tooling, yet Copilot still feels like it's operating with one eye closed.

So a friend and I started asking ourselves:

Can we measure how "Copilot-ready" a repository actually is?

That led us to build AgentCompass, an open-source AI Readiness Analyzer that scans a repository and identifies configuration, documentation, and structural issues that can make life harder for AI coding agents.

What it checks

✅ Copilot instructions

✅ Agent documentation (AGENTS.md, CLAUDE.md, etc.)

✅ Skills and prompts

✅ MCP configuration

✅ Context scoping and repository structure

✅ Common issues that can silently reduce agent effectiveness

Instead of using another AI to judge your repo, we took a different approach:

• No LLM calls in the scoring path
• No "AI vibes" scoring
• Same repo + same commit = same result every time

Think of it more like a linter for AI-readiness.

Try it yourself

GitHub:
AgentCompass Repository

Live Demo:
AgentCompass Web App

Linkedin Post

I'd love feedback from this community

A few things I'm genuinely curious about:

• What makes a repository Copilot-friendly in your experience?
• What patterns have you found that dramatically improve Copilot results?
• What checks would you add?
• Are there things that should only be measured through real-world usage rather than static analysis?

Most importantly:

👉 Run it against one of your repositories and tell me where it's wrong.

Seriously.

If it gives your repo a terrible score and you think it's nonsense, I want to hear that.

If it catches something useful, I want to hear that too.

We're trying to figure out what great AI-native repositories actually look like, and feedback from people using GitHub Copilot every day would be incredibly valuable.