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safe-debug

lllllllama/rigorpilot-skills
310.6K installs 494 GitHub stars
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Diagnoses deep learning errors, memory exhaustion, checkpoint failures, shape mismatches, and unstable training before conservative fixes.

The full skill.

Original instructions from the publisher’s SKILL.md

# safe-debug

Use this as the Rigor Debug / Rigor Audit skill. The installed slug remains
`safe-debug` for compatibility.

Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
conservative diagnosis without blocking the model from finding the local root
cause.

## When to apply

- The user provides a traceback, terminal error, or concrete training or inference failure symptom.
- The user wants diagnosis, root-cause narrowing, and minimal patch suggestions before code is changed.
- The user wants a safe debug flow with explicit human approval before mutation.

## When not to apply

- When the user wants a broad repository walkthrough without an active failure.
- When the task is speculative experimentation or code adaptation.
- When the user is asking for a large refactor or readability rewrite.

## Clear boundaries

- Diagnose first.
- Do not modify repository code by default.
- If a patch is needed, propose the smallest fix and require explicit approval first.
- Escalate savepoint or branch creation before medium-risk or high-risk changes.
- A debug fix is not automatically a research contribution; if it changes
  experiment meaning or comparability, say so explicitly.

## Output expectations

- `debug_outputs/DIAGNOSIS.md`
- `debug_outputs/PATCH_PLAN.md`
- `debug_outputs/status.json`

## Notes

Use `references/debug-policy.md`, `../ai-research-reproduction/references/research-rigor-principles.md`, and the shared `../ai-research-reproduction/references/research-pitfall-checklist.md`.