# Video Editor (FFMpeg)

> Use this tool when you need to automate video editing tasks, such as trimming, merging, or format conversion, and require a natural language interface to execute FFmpeg commands with real-time progress tracking. It solves problems in content creation, media processing, and video analysis by abstracting complex operations into a simple interface. The tool takes in FFmpeg commands as input and outputs edited video content, making it suitable for applications in video production and social media content generation.

Canonical page: https://skillsregistry.net/skills/kush36agrawal-video-editor  
JSON: https://api.skillsregistry.net/v1/skills/kush36agrawal-video-editor

## Description

This video editing MCP server, developed by Kush Agrawal, enables AI assistants to perform a wide range of video editing operations using FFmpeg. Built with Python and leveraging the MCP framework, it provides a single powerful tool, 'execute_ffmpeg', which validates and executes FFmpeg commands with real-time progress tracking. The server supports operations like trimming, merging, format conversion, speed adjustment, audio manipulation, and subtitle addition. By abstracting complex FFmpeg operations into a natural language interface, it allows AI systems to easily manipulate video content. This implementation is particularly useful for automating video editing tasks, content creation, and media processing workflows, making it suitable for applications in video production, social media content generation, and automated video analysis.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/kush36agrawal-video-editor)
- **Repository:** <https://github.com/kush36agrawal/video_editor_mcp>

## 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": "kush36agrawal-video-editor"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kush36agrawal-video-editor` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kush36agrawal-video-editor/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