Verified:GitHub

multi-ai-code-review

Execute parallel AI MCP servers to review code changes across architecture, quality, security, testing, and performance.

5|Updated Mar 22, 2026

Search Tags:#testing #architecture #security #ai #mcp #parallel #code-review

Ready to run

npx skills add https://github.com/kzytateishi/spikeee-plugins-marketplace --skill multi-ai-code-review-kzytateishi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: multi-ai-code-review
Source: https://github.com/kzytateishi/spikeee-plugins-marketplace/tree/main/plugins/multi-ai-code-review/skills/code-review
Command: npx skills add https://github.com/kzytateishi/spikeee-plugins-marketplace --skill multi-ai-code-review-kzytateishi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables a comprehensive code review of code changes by running parallel AI MCP servers to perform multi-perspective analysis, improving architecture, quality, security, and overall code health.

Core Features & Use Cases

Parallel MCP-based review perspectives: Architecture, Quality, Security, Testing, Performance, Conventions, and Consistency.
Automatic base-branch detection, diff collection, and review context preparation to ground the evaluation.
Structured output including findings, recommendations, and alignment checks for PR descriptions and linked issues.

Quick Start

Run multi-ai-code-review on the current branch to trigger a full, parallel MCP-driven code review.

Frequently Asked Questions about multi-ai-code-review

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

FAQPage Schema

How do I run a parallel AI code review on a pull request?▼

To run a parallel AI code review, execute the skill on your current branch or specify a PR. It auto-detects the base branch, gathers diffs and commits, and performs multi-perspective MCP analysis to output a structured report with findings and risk priorities.

What perspectives does multi-perspective AI code review cover?▼

Multi-perspective AI code review covers architecture, quality, security, testing, performance, conventions, and consistency. It runs parallel MCP servers to analyze each dimension independently before integrating the results into a single structured output.

Can I use MCP servers to review code changes for security and architecture?▼

Yes, you can use MCP servers to review code changes for security and architecture. The skill executes parallel MCP review servers targeting these specific dimensions, alongside testing and performance, to provide a comprehensive evaluation.

How does automated code review detect the base branch and gather context?▼

Automated code review detects the base branch automatically during Phase 1 collection. It then gathers diffs and commit history to prepare the review context, grounding the subsequent parallel MCP analysis in the actual code changes.

Does parallel code review require any specific dependencies or components?▼

Parallel code review requires no specific external dependencies or components to function. It leverages integrated MCP servers to handle the multi-perspective analysis, architecture evaluation, and security checks independently.

What is the best way to integrate AI code analysis into a continuous integration workflow?▼

The best way to integrate AI code analysis is to run it against current branch changes or specified PRs. The skill outputs a structured report with findings, recommendations, and alignment checks, which can be consumed by CI tools to enforce code health.