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github-code-review

Coordinate multi-agent GitHub PR reviews across security, performance, and style domains.

4|Updated Mar 16, 2026
Search Tags:#automation#github#multi-agent#swarm#code-review#pr-management#comment-generation
One-click install
npx skills add https://github.com/gaberger/hex --skill github-code-review-gaberger
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: github-code-review
Source: https://github.com/gaberger/hex/tree/main/.claude/skills/github-code-review
Command: npx skills add https://github.com/gaberger/hex --skill github-code-review-gaberger

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinate AI-powered, multi-agent GitHub PR reviews to improve quality and speed while preserving code integrity. In practice, it enables parallel analysis across security, performance, architecture, style, and other review domains for pull requests of any size. It requires integration with GitHub CLI, a swarm orchestration framework, and configured agent definitions to coordinate reviews and enforce quality gates.

Core Features & Use Cases

  • Multi-Agent Review System: Deploy specialized AI agents to review PRs in parallel across domains.
  • Specialized Review Agents: Separate concerns like security, performance, architecture, and style for thorough analysis.
  • PR-Based Swarm Management: Orchestrates task distribution, workplans, and progress reporting across agents.
  • Automated Workflows: Automates comment posting, approvals, and quality-gate enforcement.
  • Quality Gates & Checks: Enforces configurable thresholds to control PR readiness.

Quick Start

Initialize a multi-agent code review on a PR by running the swarm-init command with the target PR number.

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 GitHub PR code reviews with multiple AI agents?▼

Automating GitHub PR code reviews with multiple AI agents involves deploying specialized agents to analyze pull requests in parallel across security, performance, architecture, and style domains. This enables automated comment generation and enforces configurable quality gates to control PR readiness.

What is a multi-agent swarm review for pull requests?▼

A multi-agent swarm review for pull requests is an orchestrated process where specialized AI agents analyze code changes simultaneously across distinct domains. It coordinates task distribution and workplans among agents to provide thorough, parallel analysis for PRs of varying sizes and languages.

Do I need the GitHub CLI to run automated PR quality checks?▼

Yes, running automated PR quality checks with this framework requires the GitHub CLI. It is necessary to integrate with the swarm orchestration framework and configured agent definitions to orchestrate reviews, post automated comments, and apply gate policies.

Can I enforce quality gates for PR approvals using AI?▼

Yes, you can enforce quality gates for PR approvals using AI by configuring enforceable thresholds. The swarm framework checks PR readiness against these configurable quality gates before allowing automated approvals and comment posting.

How do I start a parallel code review on a GitHub pull request?▼

To start a parallel code review on a GitHub pull request, you initialize the swarm by running the swarm-init command with the target PR number. This triggers the orchestration framework to distribute tasks across the configured specialized review agents.

What are the limitations of using AI agents for code review?▼

Limitations of using AI agents for code review include the need for a configured swarm orchestration framework and defined agent profiles to operate. While it preserves code integrity through parallel analysis, it requires integration setup and depends on configured quality gate thresholds to control readiness.

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