# flowmind

> flowmind — eleven-m-flowmind. Use this tool when you need to automate workflows and manage memory for AI models like MCP, Codex, and Claude Code, streamlining development and version control with git integration. It solves problems of manual workflow management and memory optimization, providing a seamless interface for inputs like code and outputs like automated workflows. Ideal for AI development and coding tasks that require efficient workflow automation and memory management.

Canonical page: https://skillsregistry.net/skills/eleven-m-flowmind  
JSON: https://api.skillsregistry.net/v1/skills/eleven-m-flowmind

## Description

Memory and workflow automation for MCP, Codex, and Claude Code.

## Trust

- **Trust score (0–1):** 0.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/Eleven-M/flowmind)

## 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": "eleven-m-flowmind"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/eleven-m-flowmind` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/eleven-m-flowmind/pull`

---
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