Testing · Research / AGENT SKILL
safe-debug
0
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`.