# Direwolf

> Direwolf — framebuffers-direwolf. Use this tool when you need to process large datasets in a distributed manner, solving problems related to data scalability and performance in MCP environments. Direwolf provides a data processing pipeline that takes in raw data as input and outputs processed results, leveraging git for version control and collaboration. Ideal for use cases requiring efficient data handling and parallel processing in distributed systems.

Canonical page: https://skillsregistry.net/skills/framebuffers-direwolf  
JSON: https://api.skillsregistry.net/v1/skills/framebuffers-direwolf

## Description

Distributed Data Processing Pipeline for MCP.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** AGPL-3.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Framebuffers/Direwolf)

## 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": "framebuffers-direwolf"
    }
  }
}
```

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