# musterd

> musterd — sandrisestudio-musterd. Use this tool when you need to organize and manage collaborative teams consisting of AI agents and humans, leveraging a unified communication protocol to facilitate seamless interaction across various frameworks and models. It solves problems of interoperability and team coordination, enabling efficient collaboration and information sharing. With git integration, it accepts code repositories as input and outputs structured team configurations.

Canonical page: https://skillsregistry.net/skills/sandrisestudio-musterd  
JSON: https://api.skillsregistry.net/v1/skills/sandrisestudio-musterd

## Description

Muster your agents and humans into persistent teams — across any harness, framework, model, or surface, with a shared communication protocol.

## 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:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/SandRiseStudio/musterd)

## 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": "sandrisestudio-musterd"
    }
  }
}
```

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