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ORANGECRAB13
ORANGECRAB13COMMUNITY
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code-review

Coordinate multiple AI agents to review pull request diffs and identify defects.

Updated Apr 11, 2026
Search Tags:#automation#git#multi-agent#quality-assurance#ai-assistant#code-review#pull-request
One-click install
npx skills add https://github.com/ORANGECRAB13/vocareMVP --skill code-review-orangecrab13
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/ORANGECRAB13/vocareMVP/tree/main/.claude/skills/code-review
Command: npx skills add https://github.com/ORANGECRAB13/vocareMVP --skill code-review-orangecrab13

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates the code review process for pull requests by coordinating multiple specialized AI agents to analyze changes, detect issues, and propose fixes.

Core Features & Use Cases

  • Multi-agent code review workflow that inspects diffs, comments, and potential bugs.
  • Generates structured feedback and suggested fixes for PRs across languages and frameworks.
  • Use cases include fast triage of large PRs, enforcing coding standards, and improving code quality with traceable decisions.

Quick Start

Review a pull request by triggering the agent suite to analyze the diff and surface actionable recommendations.

Frequently Asked Questions about code-review

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

FAQPage Schema
How do I automate code reviews for pull requests using AI?▼

Automated code review uses a multi-agent AI workflow to inspect pull request diffs, detect defects, and identify compliance issues. It coordinates specialized agents to analyze changes and cross-file dependencies to produce actionable feedback.

How does multi-agent AI code review handle cross-file dependencies?▼

Multi-agent code review handles cross-file dependencies by coordinating specialized AI agents to analyze the entire pull request diff, trace impacts across files, and document the constraints and safety checks within the review protocol.

Can I use AI-assisted code review to enforce coding standards across different languages?▼

AI-assisted code review can enforce coding standards across various languages and frameworks. It inspects diffs and comments to generate structured feedback and suggested fixes, ensuring traceable decisions for quality assurance.

What is the best way to triage large pull requests with automated quality assurance?▼

The best way to triage large pull requests is triggering a multi-agent AI suite to analyze the diff. This automated quality assurance workflow surfaces actionable recommendations, identifies bugs, and proposes fixes quickly.

Does automated code review work with standard git pull request workflows?▼

Automated code review integrates with standard git pull request workflows by processing diffs and comments. It applies a multi-agent AI protocol to analyze changes, document auditability steps, and output structured recommendations.

When should I not use multi-agent AI for code review?▼

You should not use multi-agent AI code review when you require manual contextual judgment outside the diff, or when strict safety constraints prevent automated agents from evaluating cross-file dependencies without human safety checks.

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