debug-issue
FeaturedSystematically debug issues using graph-powered code navigation
Code & Development 31,329 stars
2850 forks Updated yesterday MIT
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Skill Content
## 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` 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
- ALWAYS start with `get_minimal_context(task="<your task>")` before any other graph tool.
- 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.
Details
- Author
- tirth8205
- Repository
- tirth8205/code-review-graph
- Created
- 6 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- MIT
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