I Built a Claude Code Skill That Makes AI Agents Prove Their Answers

Description and use

Repo X-Ray enables AI agents to substantiate answers with evidence from code, configs, tests, docs, Git history, and GitHub discussions, reducing false certainty.

Problem

AI coding agents can find relevant code but struggle to distinguish evidence from interpretation, leading to uncertain or contradictory answers.

Outcome

Repo X-Ray enables agents to provide evidence-backed answers, reducing false certainty and allowing engineers to assess contradictions and historical context.

Workflow

• 1. Define claim and search scope
• 2. Perform deterministic discovery across source code, configuration, tests, documentation, Git history, and GitHub
• 3. Record evidence and provenance in an evidence ledger
• 4. Apply targeted reasoning over collected evidence
• 5. Validate claim against evidence and classify outcome
• 6. Return structured result with classification and evidence references

Evidence

Repo X-Ray is a Claude Code skill for evidence-grounded repository analysis

Source

The important part is the ordering. I don't want the model to invent an explanation first and then search for something that supports it.

Source

Repo X-Ray uses four classifications: VERIFIED, CONTRADICTED, UNVERIFIED, UNCHECKABLE

Source