wfc-consensus-review

Run multi-agent consensus code reviews for correctness, security, performance, and complexity.

Updated Aug 23, 2026

Search Tags:#quality assurance #code review #security analysis #ai agents #consensus #performance testing

Ready to run

npx skills add https://github.com/sam-fakhreddine/wfc --skill wfc-consensus-review

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: wfc-consensus-review
Source: https://github.com/sam-fakhreddine/wfc/tree/main/wfc/skills/wfc-review
Command: npx skills add https://github.com/sam-fakhreddine/wfc --skill wfc-consensus-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of code review by leveraging multiple specialized AI agents to analyze code from different perspectives, ensuring higher quality, security, and performance before merging.

Core Features & Use Cases

Multi-Agent Analysis: Employs distinct agents for Code Review (CR), Security (SEC), Performance (PERF), and Complexity (COMP).
Consensus-Based Decision: Reaches a unified pass/fail decision based on weighted scores and critical issue detection.
Systematic Checklist: Integrates a detailed 6-step review methodology for comprehensive analysis.
Use Case: After implementing a new feature, use this Skill to get a thorough review covering logic, security vulnerabilities, performance bottlenecks, and code complexity, ensuring production-readiness.

Quick Start

Use the wfc-consensus-review skill to review the code for task TASK-001.

Frequently Asked Questions about wfc-consensus-review

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

FAQPage Schema

How do I automate code review for security and performance before merging?▼

You can automate code review using multi-agent consensus analysis, which deploys specialized AI expert personas to check code correctness, security vulnerabilities, performance bottlenecks, and complexity before merging.

What is multi-agent consensus code review and how does it work?▼

Multi-agent consensus code review uses distinct AI agents for code review, security, performance, and complexity. It reaches a unified pass or fail decision based on weighted scores and critical issue detection.

How do I run a security assessment and quality check for a new feature implementation?▼

To run a security assessment and quality check, request a code review for the feature implementation. The review applies a 6-step methodology to analyze logic, vulnerabilities, and production-readiness.

Does this multi-agent code review approach work for API changes and refactoring?▼

Yes, multi-agent code review supports API changes, refactoring, and security-sensitive code. It evaluates production-readiness by analyzing logic, security vulnerabilities, performance bottlenecks, and code complexity.

When do I need a consensus-based decision for my pull request analysis?▼

You need a consensus-based decision for pull request analysis when a feature implementation requires thorough checks across correctness, security, performance, and complexity before reaching production.

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

AI agents for code review rely on a systematic 6-step methodology and weighted scores to detect critical issues. Users should supplement this automated consensus decision with human oversight for highly complex logic.