debug-issue

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Systematically debug issues using graph-powered code navigation

Code & Development 27,148 stars 2513 forks Updated today 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
5 months ago
Last Updated
today
Language
Python
License
MIT

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