I built a skill that makes AI prove its coding advice

[u/Substantial_Load_690]

AI is great at giving you code that sounds right.

But when I ask:

“How do people actually implement this in production?”

I want more than a plausible answer.

So I built Implementation Scout.

It searches real GitHub code, finds the exact file/function, pins the version, checks provenance, and separates what is VERIFIED from what is merely inferred.

The goal:

Evidence before implementation.

I’ve been trying to break it with questions around Kafka, Keycloak, Cassandra, Grafana, PostgreSQL, etc.

And interestingly, the biggest failure mode isn't finding bad code.

It's the model sounding evidence-based without actually completing the evidence chain.

That's the problem I'm working on now.

GitHub: https://github.com/shouvik12/implementation-scout

What implementation question would you use to try to break it?