# CrewAI Workflow

> CrewAI Workflow — adam-paterson-crew-ai. Use this tool when you need to orchestrate multi-agent AI workflows with flexibility and minimal custom code. It solves problems of complex task management and agent coordination by automatically loading configurations from YAML files and supporting dynamic creation and variable templating. Ideal for use cases involving AI task automation, integration with tools like Claude Desktop or Cursor IDE, and workflows requiring seamless agent and task management.

Canonical page: https://skillsregistry.net/skills/adam-paterson-crew-ai  
JSON: https://api.skillsregistry.net/v1/skills/adam-paterson-crew-ai

## Description

A lightweight Python server for running CrewAI multi-agent workflows through the Model Context Protocol. Designed to automatically load agent and task configurations from YAML files, enabling flexible AI task orchestration with minimal custom code. Supports dynamic agent and task creation, variable templating, and seamless integration with tools like Claude Desktop or Cursor IDE.

## Trust

- **Trust score (0–1):** 0.89
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** productivity
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/adam-paterson-crew-ai)
- **Repository:** <https://github.com/adam-paterson/mcp-crew-ai>

## 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": "adam-paterson-crew-ai"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/adam-paterson-crew-ai` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/adam-paterson-crew-ai/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