GitHub’s Team X-Ray: Your Repository Knows More About Your Team Than Your Team Does

I’ve joined teams where I spent months being invisible.

Not because people were bad. Just because all the knowledge was buried in 50,000 commits I’d never seen. Who owns the auth flow? Who actually likes debugging race conditions? I had no idea. I was guessing, and it showed.

So I built Team X-Ray. It’s a VS Code extension that analyzes people, not code. How they communicate. Who they collaborate with. What they’re genuinely good at. What kinds of problems they actually enjoy solving.

It started as a GitHub Copilot integration connected to repository data through MCP. But the architecture evolved. Today it talks directly to the Copilot SDK with custom tools that give the agent native access to your team’s data. No Docker. No middleware server.

Then something unexpected happened: AI agents started making commits.

Suddenly my tool was lying to me. It was attributing expertise to bots. Inflating contributions that no human wrote. I had to teach it to separate human work from automated work, and in doing that I stumbled onto something more interesting.

In the age of agents, expertise isn’t just who writes the code. It’s who understands it. Who directs it. Who makes everyone else better.

In this talk I’ll walk you through:

• The technical evolution: from MCP to Copilot SDK with custom tools
• How I built Team X-Ray and what it taught me about real team dynamics
• The agent problem: how to detect bot commits and separate human expertise from automation
• Real examples of AI surfacing mentors, knowledge silos, and hidden team dynamics

As Moira Rose said: “When one of us shines, we all shine.”

I’m not trying to replace human connection. I’m trying to accelerate it. Even when agents are already part of the team.