An AI-powered video editing skill that enables cloud-based trimming, cutting, and exporting of high-quality video files through simple natural language commands.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install video-trimmer-github
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install video-trimmer-github using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Video Trimmer?
Video Trimmer is a professional-grade utility designed for the Openclaw Skills ecosystem, allowing developers and content creators to manipulate video files without local processing. By leveraging remote GPU nodes, it handles resource-intensive tasks like compositing video layers and applying H.264 compression server-side. This ensures that users can achieve professional results, such as 1080p MP4 exports, regardless of their local hardware capabilities.
The skill bridges the gap between complex video editing software and automated workflows. Whether you are dealing with raw screen recordings or cinematic clips, Video Trimmer provides a seamless interface to refine media through an AI-driven pipeline. It is an essential addition to any toolkit involving Openclaw Skills for those who prioritize efficiency and scalable cloud-based rendering.
Video Trimmer Use Cases
- Trim raw screen recordings to remove unnecessary intros or outros immediately.
- Batch process multiple video files within a single session for consistent editing and exporting.
- Export high-quality 1080p MP4 clips for social media or technical documentation.
- Refine video duration using natural language commands like trim the first 30 seconds and cut the last 2 minutes from this video.
- Rapidly iterate on video drafts without needing to install heavy editing software locally.
How Video Trimmer Works
- Initialize the session by connecting to the processing API using a NEMO_TOKEN environment variable.
- Upload source video files up to 500MB via multipart upload or by providing external URLs.
- Provide natural language instructions to define the trimming and editing logic required for the clip.
- The skill processes instructions via Server-Sent Events (SSE) to update the cloud-based timeline draft in real-time.
- Trigger a render job on a remote GPU node to composite and compress the final file based on the draft metadata.
- Monitor the export status via polling and download the completed 1080p MP4 video from the generated URL.
Video Trimmer Setup
To integrate this skill into your environment, ensure the NEMO_TOKEN is configured. On the first interaction, the system will automatically handle token generation if required.
# Set your environment variable for persistent access
export NEMO_TOKEN="your_token_here"
# Or let the skill handle anonymous authentication automatically
curl -X POST "https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token" -H "X-Client-Id: <uuid>"
Ensure your requests include the required attribution headers such as X-Skill-Source and X-Skill-Version to avoid export errors when using Openclaw Skills.
Video Trimmer Data Schema & Taxonomy
The skill organizes data within a session-based state, tracking tracks, segments, and generated media using optimized keys.
| Key | Description | Type |
|---|---|---|
| t | Tracks | Array of layers (video, audio, or text) |
| tt | Track Type | Integer (0=Video, 1=Audio, 7=Text) |
| sg | Segments | List of start and end points in milliseconds |
| d | Duration | Total length of the track in ms |
| m | Metadata | Technical specifications of the source file |
All session data is persisted server-side until the render job is completed or the session expires.
Video Trimmer Advanced Features
- Multi-track timeline support for combining video, audio, and text overlays in a single export.
- Real-time status polling for long-running GPU render tasks to ensure workflow continuity.
- Automatic GUI instruction mapping for translating backend editing commands into actionable API requests.
- Support for diverse formats including MOV, AVI, WebM, and MKV within the Openclaw Skills framework.
- Iterative editing capabilities that preserve the timeline state across multiple prompts for precise refinement.
SKILL.md
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