# memaro

> memaro — falk-stefan-memaro. Use this tool when you need to manage agent memory and instructions in a centralized manner, solving issues of data consistency and accessibility across multiple agents. Memaro serves as an MCP server, providing a unified interface for storing and retrieving agent data, with integration capabilities via git for version control. It is ideal for use cases requiring synchronized agent knowledge and instruction updates.

Canonical page: https://skillsregistry.net/skills/falk-stefan-memaro  
JSON: https://api.skillsregistry.net/v1/skills/falk-stefan-memaro

## Description

An MCP server for agent memory and instructions.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/falk-stefan/memaro)

## 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": "falk-stefan-memaro"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/falk-stefan-memaro` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/falk-stefan-memaro/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