mcaf-human-review-planning

Plan human review sequences for large AI-generated code drops.

4|Updated Mar 6, 2026

Search Tags:#code review #software development #prioritization #risk assessment #workflow planning #ai-generated code

Ready to run

npx skills add https://github.com/managedcode/MCPGateway --skill mcaf-human-review-planning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mcaf-human-review-planning
Source: https://github.com/managedcode/MCPGateway/tree/main/.codex/skills/mcaf-human-review-planning
Command: npx skills add https://github.com/managedcode/MCPGateway --skill mcaf-human-review-planning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles the overwhelming task of reviewing large AI-generated code drops by providing a structured plan to identify the most critical areas and files for human inspection, saving significant time and effort.

Core Features & Use Cases

Prioritized Review Sequence: Generates a step-by-step plan for human reviewers, focusing on natural user and system flows.
Risk-Based File Prioritization: Identifies and lists the highest-risk files or modules that require immediate attention.
Use Case: When an AI generates 50,000 lines of code for a new feature, use this Skill to get a clear roadmap of what to review first, rather than getting lost in the details.

Quick Start

Plan a human review for the AI-generated code in the 'new-feature' folder.

Frequently Asked Questions about mcaf-human-review-planning

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

FAQPage Schema

How do I plan a human review for large AI-generated code drops?▼

To plan a human review for large AI-generated code drops, you analyze target areas, trace user and system flows, identify risky boundaries, and prioritize files for inspection. This generates a practical step-by-step review sequence.

What is the best way to prioritize code reviews when a codebase is too large for line-by-line review?▼

The best way to prioritize code reviews for massive codebases is identifying high-risk files and modules first. This approach traces natural user and system flows to focus immediate human inspection on critical boundaries.

How does risk assessment work for AI-generated code modules?▼

Risk assessment for AI-generated code modules works by tracing user and system flows to identify risky boundaries. It generates a prioritized list of the highest-risk files requiring immediate human attention.

Can I use workflow planning to review 50,000 lines of AI-generated code?▼

Yes, you can use workflow planning to review 50,000 lines of AI-generated code. It creates a clear roadmap of what to review first by prioritizing target areas and risky boundaries, preventing you from getting lost in details.

When do I need a prioritized review sequence for software development?▼

You need a prioritized review sequence for software development when an AI generates a massive code drop for a new feature. It provides a structured plan to identify critical areas, saving significant time and effort.

What are the limitations of planning a human review for large code drops?▼

The limitation of planning a human review for large code drops is that it provides a prioritized inspection sequence rather than comprehensive line-by-line verification. It focuses on risky boundaries and critical files, skipping lower-risk details.