A powerful AI video editing agent that transforms raw footage into polished 1080p MP4s via natural language commands and cloud-based GPU rendering.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install video-editing-for-beginners-pdf
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install video-editing-for-beginners-pdf using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Video Editing for Beginners is a specialized tool designed to bridge the gap between raw video footage and professional-looking results without the need for complex manual software. By utilizing Openclaw Skills, this agent connects to high-performance remote GPU nodes to process edits like trimming, adding transitions, and overlaying text titles automatically. It is optimized for students, content creators, and developers who need to move fast, offering a streamlined workflow that handles rendering and encoding on the server side.
The skill provides a hands-off approach to video production, where users simply describe their desired outcome. Whether you are dealing with MP4, MOV, or AVI files, the integration ensures that technical hurdles like codec management and aspect ratio adjustments are handled by the AI, making high-quality video production accessible to anyone.
To get started with this skill, ensure your environment is configured correctly. Openclaw Skills require a NEMO_TOKEN for authentication.
# Set your environment variable
export NEMO_TOKEN="your_access_token_here"
# If you do not have a token, the skill can generate an anonymous one:
curl -X POST "https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token" \
-H "X-Client-Id: <unique_uuid>"
The skill will automatically initialize a session ID to track your editing project across requests.
The skill manages video data using a structured draft JSON schema to track the timeline. Below is the metadata taxonomy used for session states:
| Key | Definition | Details |
|---|---|---|
t |
Tracks | An array containing all media layers |
tt |
Track Type | 0 = Video, 1 = Audio, 7 = Text |
sg |
Segments | Specific clips or blocks within a track |
d |
Duration | Length of the segment in milliseconds |
m |
Metadata | Technical specs of the source file (e.g., resolution) |
Exported files are delivered as standard 1080p MP4 files encoded with H.264.
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