skill-code-review

Orchestrate multi-model code reviews before merging pull requests.

Updated Jun 16, 2026

Search Tags:#code review #pull request #llm #security audit #multi-agent #software quality

Ready to run

npx skills add https://github.com/burgebj/claudeoctopus --skill skill-code-review-burgebj

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-code-review
Source: https://github.com/burgebj/claudeoctopus/tree/main/.claude/skills/skill-code-review
Command: npx skills add https://github.com/burgebj/claudeoctopus --skill skill-code-review-burgebj

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents production issues by orchestrating multiple AI reviewers to find code quality, security, architecture, and implementation risks before changes are merged.

Core Features & Use Cases

Multi-LLM Review Pipeline: Coordinates multiple AI providers and structured review phases to produce broader code analysis than a single reviewer.
Security and Quality Analysis: Detects vulnerabilities, performance issues, design problems, TDD gaps, and autonomous code generation risks.
Use Case: Review a pull request containing authentication changes to receive synthesized feedback on security flaws, maintainability concerns, and merge readiness.

Quick Start

Use the code review skill to analyze this pull request for security issues, implementation risks, and code quality problems.

Frequently Asked Questions about skill-code-review

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

FAQPage Schema

How do I automate a pull request security audit using multiple AI models?▼

Automate a pull request security audit by orchestrating a multi-LLM review pipeline that coordinates multiple AI providers to detect vulnerabilities and code quality risks before changes are merged.

What is multi-agent code review and how does it improve software quality?▼

Multi-agent code review is a structured pipeline orchestrating multiple AI reviewers to analyze code changes. It improves software quality by producing broader analysis on security, architecture, and implementation risks than a single reviewer.

Can I evaluate implementation completeness and TDD gaps in an AI-assisted code generation?▼

Yes, you can evaluate AI-assisted code generation by applying validation gates and structured review phases to assess implementation completeness, detect TDD gaps, and identify autonomous code generation risks.

Does multi-LLM code review work for architecture evaluations on pull requests?▼

Yes, multi-LLM code review works for architecture evaluations by applying structured review pipelines and provider orchestration to assess design problems and merge readiness on pull requests.

What is the best way to catch production issues in authentication changes before merging?▼

The best way to catch production issues in authentication changes is orchestrating multiple AI reviewers to synthesize feedback on security flaws, maintainability concerns, and merge readiness before code changes are merged.

What are the limitations of using a single AI model for code review compared to multi-agent?▼

A single AI model for code review lacks the broader analysis of a multi-agent approach. Multi-LLM pipelines coordinate multiple providers and structured review phases to find security, performance, and design risks more effectively.