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kzytateishi
kzytateishiCOMMUNITY
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code-review

Automate code reviews with parallel AI engines and MCP servers.

5|Updated Mar 22, 2026
Search Tags:#git#ai#mcp#quality#code-review#pull-request
One-click install
npx skills add https://github.com/kzytateishi/spikeee-plugins-marketplace --skill code-review-kzytateishi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/kzytateishi/spikeee-plugins-marketplace/tree/main/plugins/dev-workflow/skills/code-review
Command: npx skills add https://github.com/kzytateishi/spikeee-plugins-marketplace --skill code-review-kzytateishi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates a comprehensive code review by applying parallel AI MCP servers to evaluate architecture, code quality, security, and test coverage, increasing PR quality and reducing review time.

Core Features & Use Cases

  • Multi-perspective analysis: Simultaneously analyzes architecture, quality, security, testing, performance, conventions, and consistency.
  • MCP orchestration: Dynamically detects and leverages available MCP servers to broaden review perspectives.
  • PR-ready findings: Produces structured findings and recommendations suitable for integration into PR reviews.

Quick Start

Run a code review on the current branch or a specified branch using available MCP servers for multi-perspective analysis.

Frequently Asked Questions about code-review

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

FAQPage Schema
How do I automate a pull request code review for multiple perspectives like security and architecture?▼

You can automate a pull request code review by applying parallel AI engines to assess architecture, security, testing, and performance simultaneously. The Skill processes Git diffs, file lists, and PR descriptions to output a structured findings report for integration.

What is parallel AI code review and how does it work with MCP servers?▼

Parallel AI code review dynamically detects and orchestrates available MCP servers to broaden analysis perspectives across quality, conventions, and consistency. It evaluates implemented or refactored code on feature branches to produce structured findings.

Can I run an AI code review on a specific Git feature branch or commit history?▼

AI code review can be applied to implemented or refactored code on a feature branch, pull request, or any Git history. It leverages available MCP servers to process diffs and commit logs for comprehensive multi-perspective analysis.

Do I need MCP servers configured to generate a code review findings report?▼

The Skill dynamically detects and leverages available MCP servers to broaden review perspectives and process data like diffs and PR descriptions. It orchestrates these servers to produce structured findings and recommendations suitable for PR reviews.

What is the best way to identify architectural and security issues in a Git diff?▼

Using parallel AI-driven code review is an effective way to identify architectural and security issues in a Git diff. It analyzes code quality, security, and test coverage simultaneously to produce structured recommendations for PR reviews.

Does automated code review cover test coverage and performance issues?▼

Automated code review covers test coverage and performance issues along with architecture, code quality, security, and conventions. It applies parallel AI engines to provide a comprehensive multi-perspective analysis of implemented code changes.

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