safe-debug

작성자: lllllllama

딥러닝 연구 작업을 위한 엄격한 디버그/엄격한 감사 스킬입니다. 사용자가 트레이스백, 터미널 오류, CUDA OOM, 체크포인트 로드 실패, 형태 불일치, NaN 손실 증상 또는 훈련 실패를 붙여넣고 패치 전에 보수적인 진단을 원하며 디버그 수정이 연구 기여와 명확히 분리되어야 할 때 사용하세요. 광범위한 리팩토링, 추측성 적응, 자동 탐색적 패치 또는 일반적인 저장소 숙지에는 사용하지 마십시오.

npx skills add https://github.com/lllllllama/rigorpilot-skills --skill safe-debug

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 ../../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, ../../references/research-rigor-principles.md, and the shared ../../references/research-pitfall-checklist.md.

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