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github-code-review

Automate multi-agent code review for GitHub pull requests with ruv-swarm.

Updated Oct 30, 2025
Search Tags:#automation#code review#security analysis#GitHub#quality gates#AI swarm#performance analysis#PR management
One-click install
npx skills add https://github.com/whrit/agent-flow --skill github-code-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: github-code-review
Source: https://github.com/whrit/agent-flow/tree/main/.claude/skills/github-code-review
Command: npx skills add https://github.com/whrit/agent-flow --skill github-code-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires github-cli, ruv-swarm, claude-flow, and includes references (resource) components.

What problem does it solve?

This Skill revolutionizes GitHub code reviews by deploying specialized AI agents to perform comprehensive, intelligent analysis. It eliminates human oversight, speeds up review cycles, and ensures consistent quality, security, and performance standards across your codebase.

Core Features & Use Cases

  • Multi-Agent Review System: Deploys specialized AI agents (security, performance, architecture, style, accessibility) for parallel, in-depth code analysis.
  • PR-Based Swarm Management: Creates and manages AI swarms directly from Pull Request data, with label-based agent assignment and dynamic topology selection.
  • Intelligent Comment Generation & Quality Gates: Generates contextual review comments with suggested fixes and enforces quality gates to block merges on critical issues.
  • Use Case: When a critical security patch is submitted, use this Skill to automatically trigger a multi-agent review with a dedicated "security" agent, ensuring all OWASP top 10 vulnerabilities and secret exposures are identified and addressed before merging.

Quick Start

Initialize a review swarm for a Pull Request by piping PR data to npx ruv-swarm github review-init --pr 123. Then, post an initial status comment to the PR.

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 code reviews for GitHub pull requests?▼

Automate code reviews by deploying specialized AI agents to GitHub PRs using multi-agent swarm orchestration. Initialize a review swarm with PR data via ruv-swarm, which assigns agents across security, performance, architecture, style, and accessibility domains, generating contextual comments and enforcing quality gates automatically.

Can I use AI agents to review GitHub PRs for security vulnerabilities?▼

Yes, dedicated security agents identify OWASP vulnerabilities and secret exposures in pull requests. The Skill triggers multi-agent review workflows with specialized security agents that analyze code before merge, ensuring critical security issues are caught and addressed.

What's the best way to enforce code quality standards across GitHub pull requests?▼

Enforce quality standards by deploying swarm-based agents that perform parallel analysis and block merges on critical issues via quality gates. Agents review security, performance, architecture, style, and accessibility simultaneously, generating automated comments with suggested fixes.

Does this work with GitHub CLI and existing PR workflows?▼

Yes, the Skill integrates with GitHub CLI (gh) and manages swarms directly from PR data using label-based agent assignment. It posts status comments and updates PR metadata without requiring manual intervention in your existing GitHub workflow.

How do performance and security reviews get triggered automatically on pull requests?▼

Performance and security reviews trigger automatically when PR data is piped to ruv-swarm with swarm initialization. Specialized agents analyze code in parallel domains and post findings as PR comments, with quality gates blocking merges if critical issues are detected.

What are the limitations of automated code review agents?▼

Automated agents excel at pattern detection and standard checks but may miss domain-specific business logic or nuanced architectural decisions. They work best for security, performance, and style enforcement; human review remains valuable for complex design trade-offs.

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