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danilonovaisv
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github-code-review

Coordinate AI review agents on GitHub pull requests via GitHub CLI.

1|Updated Dec 2, 2025
Search Tags:#automation#github#swarm#code-review#quality-gates#ai-agents#pr-management
One-click install
npx skills add https://github.com/danilonovaisv/PORTFOLIO-DANILO-FINAL --skill github-code-review-danilonovaisv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: github-code-review
Source: https://github.com/danilonovaisv/PORTFOLIO-DANILO-FINAL/tree/main/.claude/skills/github-code-review
Command: npx skills add https://github.com/danilonovaisv/PORTFOLIO-DANILO-FINAL --skill github-code-review-danilonovaisv

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Comprehensive GitHub code review is automated by coordinating AI-driven swarm reviewers to cover multiple code-quality dimensions.

Core Features & Use Cases

  • Multi-Agent Review System: Deploy specialized AI agents to review different aspects (security, performance, architecture, style, accessibility) in parallel.
  • PR-Based Workflows: Integrates with GitHub CLI to fetch PR context, post inline comments, and enforce quality gates automatically.
  • Use Case: When a PR lands, spawn a swarm to analyze changes, surface issues, suggest fixes, and accelerate safer merges.

Quick Start

Initialize and run a multi-agent code review swarm for a specific pull request using the built-in GitHub CLI integration.

Frequently Asked Questions about github-code-review

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

FAQPage Schema
How do I automate multi-agent code reviews on GitHub pull requests?▼

Multi-agent code reviews on GitHub pull requests are automated by coordinating specialized AI agents to analyze security, performance, and architecture in parallel. The system uses the GitHub CLI to fetch PR context and post inline comments with suggested fixes.

Can I use AI agents to review code quality dimensions like accessibility and style?▼

AI agents can review code quality dimensions like accessibility, style, performance, security, architecture, and documentation. A configurable agent taxonomy defines the review scope, deploying specialized swarm agents to provide actionable feedback on pull requests.

How does the RUV Swarm orchestration framework coordinate PR reviews?▼

The RUV Swarm orchestration framework coordinates PR reviews by deploying specialized AI agents to review different code quality dimensions in parallel. It relies on GitHub CLI for PR data extraction and manages agent workflows to surface issues and suggest fixes.

Do I need the GitHub CLI to extract PR data for automated code review?▼

The GitHub CLI is required to extract PR data for automated code review. The multi-agent review system integrates with GitHub CLI to fetch pull request context, post inline comments, and enforce quality gates automatically.

What's the best way to enforce quality gates automatically on GitHub PRs?▼

Quality gates are enforced automatically on GitHub PRs by integrating multi-agent AI reviews with GitHub CLI. The swarm analyzes changes, posts inline comments, surfaces issues, and suggests fixes to accelerate safer merges based on configurable agent taxonomy.

Does automated PR review work for pull requests of varying sizes and languages?▼

Automated PR review works for pull requests of varying sizes and languages. The multi-agent system applies specialized AI reviewers across security, performance, architecture, style, accessibility, and documentation dimensions regardless of codebase scale.

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