debug-plan
Create a systematic debugging plan for a bug report. Use when the user asks how to investigate a failure, regression, or unexpected behavior.
Works with
--- name: debug-plan description: Create a systematic debugging plan for a bug report. Use when the user asks how to investigate a failure, regression, or unexpected behavior. license: MIT --- # Debug Plan Create a practical debugging plan that starts with evidence before fixes. ## Instructions 1. Restate the symptom in one sentence. 2. List the most likely failure boundaries. 3. Suggest the first three checks in order. 4. Identify what evidence would confirm or reject each hypothesis. 5. End with the smallest safe fix path. Keep the plan short enough that someone can start immediately. For more detail on the shape of the output, read `references/checklist.md`.
More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
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.
