debug-issue

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Systematically debug issues using graph-powered code navigation

Code & Development 31,800 stars 2907 forks Updated 1 weeks ago MIT

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Quality Score: 89/100

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Description 5%
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Skill Content

## Debug Issue Trace a bug through the knowledge graph before reading source. ### Steps 1. Call `semantic_search_nodes_tool` to find code related to the issue. 2. Call `query_graph_tool` with `callers_of` and `callees_of` to trace the call chain in both directions. 3. Call `get_flow_tool` for the execution path that reaches the suspect code. Its entry point is where the bug is triggered. 4. Call `detect_changes_tool` to check whether a recent change caused the issue. 5. Call `get_impact_radius_tool` on the suspect files to see what a fix would affect. ## Token Efficiency Rules - Call `get_minimal_context_tool(task="<your task>")` before any other graph tool. - Pass `detail_level="minimal"` wherever a tool accepts it. Use "standard" only when minimal is not enough. - Prefer a targeted `query_graph_tool` call over a broad listing call. - Budget: about five tool calls and 800 tokens of graph output per task. - Read the implementation and its tests before changing code. The graph narrows scope; it does not replace the source.

Details

Author
tirth8205
Repository
tirth8205/code-review-graph
Created
7 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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