debug
Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file.…
Reproduce a failure and narrow it to an evidence-backed root cause before fixing.
$ npx -y skills add Borda/AI-Rig --skill rig-investigate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/rig-investigateContext preview
The summary Claude sees to decide when to auto-load this skill.
Reproduce a failure and narrow it to an evidence-backed root cause before fixing.
name: Rig Investigate description: Reproduce a failure and narrow it to an evidence-backed root cause before fixing.
> Read [Evidence rules](../../references/evidence.md). A plausible explanation is not a root cause.
1. **Start the run.** `rig_evidence` with `action: "start"`, `skill: "rig-investigate"`, and candidate paths. 2. **Reproduce with real inputs.** Run the failing command. If it does not reproduce, record the exact difference and stop rather than guessing. 3. **Shrink the case.** Remove unrelated configuration and files until the smallest failing case remains. Record each removal. 4. **List hypotheses.** Write at least two plausible causes. For each, name the observation that would disprove it. 5. **Test one at a time.** Change one variable per run. Record the command, output digest, and conclusion. Optional: dispatch read-only `rig-investigator` children for independent hypotheses; they share the tree, so they must not edit files. 6. **Confirm the cause.** Show the failing behavior disappearing when the cause is removed, or explain why that experiment is unavailable. 7. **Record the diagnosis** with reproduction command, root cause, evidence, eliminated hypotheses, confidence, and the smallest next change.
Report reproduction status, root cause, evidence references, confidence, limits, and the recommended next action. Do not present the fix as already verified.
Practical agent workflows for Python, ML, and open-source maintenance. AI-Rig turns recurring work—scoping a change, reproducing a bug, reviewing a pull request, running an experiment, or checking release readiness—into explicit workflows with specialist
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