# Code Expert Review

> Use this tool when you need to refine your code and receive expert-level feedback on software engineering principles. It analyzes code snippets and provides refactoring suggestions and clean code recommendations through simulated expert personas. Ideal for developers seeking to improve their code quality without leaving their development environment, it integrates with various platforms and supports multiple transport modes.

Canonical page: https://skillsregistry.net/skills/tomsiwik-experts  
JSON: https://api.skillsregistry.net/v1/skills/tomsiwik-experts

## Description

MCP Code Expert System provides code review capabilities through simulated expert personas like Martin Fowler and Robert C. Martin (Uncle Bob). Built with Python using FastAPI, it analyzes code snippets against established software engineering principles, offering refactoring suggestions and clean code recommendations. The system integrates with Ollama for AI-powered reviews, stores relationships in a knowledge graph, and supports both standard mode for Cursor IDE integration and SSE transport for web applications. This implementation is particularly valuable for developers seeking expert-level feedback on their code without leaving their development environment.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** api-integration
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tomsiwik-experts)
- **Repository:** <https://github.com/tomsiwik/mcp-experts>

## 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": "tomsiwik-experts"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tomsiwik-experts` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tomsiwik-experts/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