rule-of-5

Orchestrate a three-wave multi-agent code review with cross-validation and convergence checks.

1|Updated Jan 31, 2026

Search Tags:#code review #performance #security #maintainability #ai agents #multi-agent

Ready to run

npx skills add https://github.com/charly-vibes/wai --skill rule-of-5

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rule-of-5
Source: https://github.com/charly-vibes/wai/tree/main/.agents/skills/rule-of-5
Command: npx skills add https://github.com/charly-vibes/wai --skill rule-of-5

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the complex and time-consuming process of code review by simulating multiple specialized AI agents to identify a wide range of potential issues.

Core Features & Use Cases

Simulated Multi-Agent Review: Employs distinct AI personas (Security, Performance, Maintainability, Requirements, Operations) for comprehensive analysis.
Phased Validation: Includes distinct waves for initial analysis, cross-validation, and convergence checks to ensure thoroughness and accuracy.
Use Case: A development team can submit a new feature's code to this Skill to receive a detailed, prioritized report of potential bugs, security vulnerabilities, and areas for improvement before merging.

Quick Start

Use the rule-of-5 skill to perform a multi-agent code review on the provided code snippet.

Frequently Asked Questions about rule-of-5

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

FAQPage Schema

How does multi-agent code review work for finding security and performance issues?▼

Multi-agent code review works by simulating distinct specialist AI personas to independently analyze code. It cross-validates findings across security, performance, and maintainability domains to produce a prioritized list of actionable issues.

What is the best way to automate code review for maintainability and operations vulnerabilities?▼

The best way to automate code review is using a phased validation process that simulates multiple specialist agents. This approach runs parallel independent analysis, cross-validation, and convergence checks to synthesize prioritized issues across required domains.

Can I use AI agents to cross-validate code review findings before merging?▼

Yes, you can use AI agents to cross-validate code review findings. The process orchestrates multiple waves of analysis where specialist personas validate each other's results to ensure accuracy before producing a final developer report.

How do I perform a simulated multi-agent code review on a code snippet?▼

To perform a simulated multi-agent code review, submit your code snippet to the skill. The system automatically orchestrates three waves: parallel independent analysis, cross-validation, and convergence checking to generate a prioritized list of actionable items.

What domains does an automated multi-agent code review cover?▼

An automated multi-agent code review covers five domains: security, performance, maintainability, requirements, and operations. Specialist AI agents independently analyze the code within these domains before synthesizing their findings into a final report.

Why use a phased validation approach instead of a standard AI code review?▼

A phased validation approach improves accuracy by running parallel independent analysis followed by cross-validation and convergence checks. This ensures thoroughness and validates findings across domains before producing a final, prioritized list of actionable items.