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Tony363
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sc-code-review

Review git commits, staged changes, or branch differences with multi-model consensus.

19|2|Updated Aug 26, 2025
Search Tags:#git#architecture#code review#performance#security#ai#quality
One-click install
npx skills add https://github.com/Tony363/SuperClaude --skill sc-code-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sc-code-review
Source: https://github.com/Tony363/SuperClaude/tree/main/.claude/skills/sc-code-review
Command: npx skills add https://github.com/Tony363/SuperClaude --skill sc-code-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of code review, identifying potential issues in security, performance, quality, and architecture, thereby improving code reliability and maintainability.

Core Features & Use Cases

  • Multi-model Consensus: Leverages multiple AI models to provide a comprehensive and robust review.
  • Interactive Scoping: Allows users to define and refine the scope of the review before it begins.
  • Large Diff Handling: Intelligently manages and reviews large code changes through chunking or focused analysis.
  • Finding Validation: Includes interactive steps for users to confirm critical findings and resolve model disagreements.
  • Automated Fixing: Offers an option to automatically fix identified issues.
  • Use Case: A developer can use this skill to review their staged changes before committing, ensuring adherence to best practices and catching potential bugs early in the development cycle.

Quick Start

Use the sc-code-review skill to review the last 5 commits.

Frequently Asked Questions about sc-code-review

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

FAQPage Schema
How can I automate code review for git commits to catch security and performance issues?▼

You can automate code review for git commits by running an interactive, multi-model consensus analysis on staged changes or branch differences. This process validates findings against security, performance, and architecture standards to improve reliability.

Does multi-model consensus code review work with large diffs?▼

Multi-model consensus code review handles large diffs by intelligently chunking code changes or applying focused analysis. This ensures comprehensive review coverage without overwhelming the AI models evaluating the staged changes.

How do I validate AI code review findings before committing changes?▼

AI code review findings are validated through interactive steps where users confirm critical issues and resolve model disagreements. This ensures accuracy before proceeding to automated fixing or finalizing the commit.

Can I automatically fix code quality issues identified during a branch difference review?▼

Yes, you can automatically fix identified code quality, security, and architecture issues detected during a branch difference review. The process offers automated fixing options after validating the initial AI findings.

What prerequisites do I need to run AI-powered code review on my repository?▼

To run AI-powered code review, you need git installed and access to PAL MCP tools for consensus, codereview, listmodels, and challenge functions. Rube MCP is optionally required for notifications.

What is the best way to scope a code review for specific branch differences?▼

The best way to scope a code review for branch differences is through interactive scoping, which allows you to define and refine the exact review boundaries before initiating the multi-model consensus analysis.

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