# CodeWell

> CodeWell — fitz798-codewell. Use this tool when you need to store and manage local coding memory for AI agents, enabling seamless access to code repositories without relying on LLM APIs. CodeWell solves version control and collaboration problems by integrating with git, allowing for efficient code management. It is ideal for use cases requiring autonomous coding capabilities and MCP-native compatibility.

Canonical page: https://skillsregistry.net/skills/fitz798-codewell  
JSON: https://api.skillsregistry.net/v1/skills/fitz798-codewell

## Description

Local coding memory for AI agents. MCP-native. No LLM API required.

## Trust

- **Trust score (0–1):** 0.73
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Fitz798/CodeWell)

## 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": "fitz798-codewell"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fitz798-codewell` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fitz798-codewell/pull`

---
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