# mcp-flow

> mcp-flow — gitstq-mcp-flow. Use this tool when you need to streamline AI agent pipeline management, as it enables building, running, and scaling workflows using YAML configuration files, accepting Git repository inputs and producing optimized pipeline outputs. It solves problems of workflow complexity and scalability, allowing for seamless integration and automation. Ideal for use cases requiring flexible and efficient AI pipeline orchestration.

Canonical page: https://skillsregistry.net/skills/gitstq-mcp-flow  
JSON: https://api.skillsregistry.net/v1/skills/gitstq-mcp-flow

## Description

Python-native MCP workflow orchestration engine — build, run, and scale AI agent pipelines with YAML

## Trust

- **Trust score (0–1):** 0.57
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** devops-ci
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/gitstq/mcp-flow)

## 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": "gitstq-mcp-flow"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gitstq-mcp-flow` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gitstq-mcp-flow/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