# Controtto

> Use this tool when you need to evaluate and refine Golang code for adherence to strict Domain-Driven Design (DDD) and Clean Architecture principles. It assesses code quality, identifying areas for improvement to ensure maintainability, scalability, and best practices. By inputting Golang code, Controtto provides output in the form of constructive feedback and suggestions for enhancement.

Canonical page: https://skillsregistry.net/skills/contre95-controtto  
JSON: https://api.skillsregistry.net/v1/skills/contre95-controtto

## Description

You are capable of interpreting golang code and judge it under the most strict ddd and clean architecture paragidms

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-04-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/b0z262ljps)
- **Repository:** <https://github.com/contre95/controtto>

## 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": "contre95-controtto"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/contre95-controtto` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/contre95-controtto/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