# Code Review

> Use this tool when you need to automate code reviews and improve code quality, as it solves problems related to security, performance, and maintainability by analyzing repository structures and providing structured output with issues, strengths, and recommendations. It integrates with Repomix and utilizes multiple LLM providers, accepting repository data as input and producing actionable insights as output. This tool is ideal for developers seeking AI-powered code assessment and review within their assistant interface.

Canonical page: https://skillsregistry.net/skills/crazyrabbitltc-code-review  
JSON: https://api.skillsregistry.net/v1/skills/crazyrabbitltc-code-review

## Description

Code Review Server is an MCP implementation that enables AI assistants to perform automated code reviews using multiple LLM providers (OpenAI, Anthropic, Gemini). It integrates with Repomix to flatten repository structures for analysis, then processes the code through configurable LLM prompts focused on security, performance, quality, and maintainability. The server exposes tools for repository analysis and code review with structured output containing issues, strengths, and recommendations, making it valuable for developers seeking AI-powered code quality assessment without leaving their assistant interface.

## Trust

- **Trust score (0–1):** 0.83
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/crazyrabbitltc-code-review)
- **Repository:** <https://github.com/crazyrabbitltc/mcp-code-review-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "crazyrabbitltc-code-review"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/crazyrabbitltc-code-review` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/crazyrabbitltc-code-review/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt