review-changes
FeaturedPerform 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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