review-changes

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Perform a structured code review using change detection and impact

Code & Development 27,148 stars 2513 forks Updated today MIT

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Skill Content

## Review Changes Perform a thorough, risk-aware code review using the knowledge graph. ### Steps 1. Run `detect_changes_tool` to get risk-scored change analysis. 2. Run `get_affected_flows_tool` to find impacted execution paths. 3. For each high-risk function, run `query_graph_tool` with pattern="tests_for" to check test coverage. 4. Run `get_impact_radius_tool` to understand the blast radius. 5. For any untested changes, suggest specific test cases. ### Output Format Provide findings grouped by risk level (high/medium/low) with: - What changed and why it matters - Test coverage status - Suggested improvements - Overall merge recommendation ## 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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