# agenthold

> agenthold — edobusy-agenthold. Use this tool when you need to manage shared state across multiple AI agents in a workflow, leveraging version control through git to track changes and ensure consistency. It solves problems of data synchronization and collaboration in multi-agent environments, providing a centralized server for state management. Ideal for use cases requiring coordinated agent actions, agenthold accepts state updates as input and outputs a unified, versioned state.

Canonical page: https://skillsregistry.net/skills/edobusy-agenthold  
JSON: https://api.skillsregistry.net/v1/skills/edobusy-agenthold

## Description

Shared versioned state for multi-agent AI workflows. An MCP server.

## Trust

- **Trust score (0–1):** 0.86
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/edobusy/agenthold)

## 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": "edobusy-agenthold"
    }
  }
}
```

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