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Get Started Free →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 mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 70% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-01 | ✓→✗ | ▼ Worse | -80% | 0% |
| case-03 | ✓→✗ | ▼ Worse | -75% | 0% |
| case-17 | ✓→✗ | ▼ Worse | -4% | 0% |
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.
experiment meaning or comparability, say so explicitly.
debug_outputs/DIAGNOSIS.mddebug_outputs/PATCH_PLAN.mddebug_outputs/status.jsonUse references/debug-policy.md, ../../references/research-rigor-principles.md, and the shared ../../references/research-pitfall-checklist.md.
Other measured skills in the registry, with their headline benchmark lift.