debug-issue
Systematically debug issues using graph-powered code navigation
Works with
--- name: debug-issue description: Systematically debug issues using graph-powered code navigation license: MIT --- ## Debug Issue Use the knowledge graph to systematically trace and debug issues. ### Steps 1. Use `semantic_search_nodes_tool` to find code related to the issue. 2. Use `query_graph_tool` with `callers_of` and `callees_of` to trace call chains. 3. Use `get_flow_tool` to see full execution paths through suspected areas. 4. Run `detect_changes_tool` to check if recent changes caused the issue. 5. Use `get_impact_radius_tool` on suspected files to see what else is affected. ### Tips - Check both callers and callees to understand the full context. - Look at affected flows to find the entry point that triggers the bug. - Recent changes are the most common source of new issues. ## Token Efficiency Rules - Start with `get_minimal_context_tool(task="<your task>")` before other graph tools. - Use `detail_level="minimal"` on all calls. Only escalate to "standard" when minimal is insufficient. - Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens. - Read the implementation and its tests before changing code. The graph narrows scope; it does not replace the source.
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.
