# wellworn

> wellworn — wellworn-dev-wellworn. Use this tool when you need to verify code quality and identify potential issues in your git repository. Wellworn solves problems related to code maintenance, version control, and design system integration, providing a judgment layer for coding agents. It takes in git repository data as input and outputs verified picks and traps, helping you refine your codebase without requiring a key.

Canonical page: https://skillsregistry.net/skills/wellworn-dev-wellworn  
JSON: https://api.skillsregistry.net/v1/skills/wellworn-dev-wellworn

## Description

The judgment layer for coding agents: verified picks, traps at your version, skills, and design systems over MCP. Works without a key.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-22

## Source

- **Source listing:** [GitHub](https://github.com/WellWorn-dev/wellworn)

## 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": "wellworn-dev-wellworn"
    }
  }
}
```

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