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Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape…
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.
don't have the plugin yet? install it then click "run inline in claude" again.