review-changes

Reviews code changes using risk-scored change detection and knowledge graph impact analysis.

Updated Sep 8, 2026
One-click install
npx skills add https://github.com/bmiit145/leadBee --skill review-changes-bmiit145
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-changes
Source: https://github.com/bmiit145/leadBee/tree/main/.gemini/skills/review-changes
Command: npx skills add https://github.com/bmiit145/leadBee --skill review-changes-bmiit145

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Code reviews often miss hidden blast radius: a small change can break distant execution paths or lack test coverage. This Skill performs a structured, risk-aware review by querying a code knowledge graph instead of reading every file manually. ## Core Features & Use Cases - Risk-Scored Change Detection: Runs change detection to identify what changed and how risky each change is. - Impact Analysis: Finds affected execution flows and the blast radius of modified functions via graph queries. - Test Coverage Checks: Verifies whether high-risk functions have tests and suggests specific test cases for untested changes. - Use Case: Before merging a pull request, run the review to get findings grouped by risk level (high/medium/low), each with test coverage status and a merge recommendation. ## Quick Start Ask the assistant to review the current code changes and produce a risk-grouped report with test coverage status and a merge recommendation.

Frequently Asked Questions about review-changes

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review code changes with impact analysis?▼

Run change detection to get risk-scored changes, then query affected execution flows and impact radius for each high-risk function. Finish by checking test coverage and grouping findings by risk level with a merge recommendation.

How to check if changed code has test coverage?▼

Query the knowledge graph with the tests_for pattern for each high-risk changed function. For any untested changes, the review suggests specific test cases to add before merging.

What is a knowledge graph code review?▼

It is a review approach that queries a graph of the codebase to trace execution flows and blast radius instead of reading files manually. The graph narrows scope but does not replace reading the actual implementation and its tests.

How do I reduce token usage during AI code review?▼

Start with a minimal-context query for your task and use minimal detail level on all graph calls, escalating to standard only when needed. The target is completing a review in five or fewer tool calls and under 800 output tokens.

When is graph-based review not enough?▼

Graph analysis narrows scope but cannot replace reading source code. Always read the implementation and its tests before changing code, since the graph shows relationships, not full logic.