# megg

> megg — toruai-megg. Use this tool when you need to manage and synchronize markdown knowledge with your Git repository, solving version control and collaboration challenges for AI agents. It takes in markdown files and outputs a unified knowledge base, accessible via a CLI and MCP server interface. Ideal for use cases where AI agents require shared, up-to-date knowledge stored alongside code.

Canonical page: https://skillsregistry.net/skills/toruai-megg  
JSON: https://api.skillsregistry.net/v1/skills/toruai-megg

## Description

Git-native memory for AI agents — markdown knowledge in .megg/ folders that travels with your repo. MCP server + CLI for Claude Code.

## Trust

- **Trust score (0–1):** 0.97
- **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/ToruAI/megg)

## 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": "toruai-megg"
    }
  }
}
```

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