A comprehensive video editing suite that leverages over 25 advanced AI models to transform, upscale, and synchronize video content programmatically.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install eachlabs-video-edit
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 eachlabs-video-edit 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 EachLabs Video Edit?
The EachLabs Video Edit skill provides a powerful interface to the EachLabs Predictions API, allowing developers to manipulate video files using state-of-the-art AI models. By integrating this capability into Openclaw Skills, users can perform complex tasks such as video extension with Veo 3.1, high-quality upscaling via Topaz, and seamless lip-syncing using Sync v2 Pro.
This skill is designed for high-performance media workflows, offering access to specialized models for style transfer, object replacement, and video translation. Whether you need to generate subtitles automatically or perform face swaps in video sequences, this skill provides the necessary technical infrastructure to execute these transformations efficiently.
EachLabs Video Edit Use Cases
- Synchronizing character lip movements with new audio tracks for localization or dubbing.
- Extending existing video clips while maintaining visual consistency using generative AI.
- Upscaling low-resolution footage to high-definition quality for professional production.
- Automating the generation and burning of subtitles into video files.
- Applying artistic style transfers or motion animations to static or existing video content.
How EachLabs Video Edit Works
- The system first validates the requested model by querying the EachLabs API to retrieve the specific request schema and required parameters.
- A prediction request is initiated via a POST request containing the model slug, version, and input data such as video URLs or text prompts.
- The skill enters a polling state, periodically checking the status of the prediction until it reaches a success or failure state.
- Upon successful completion, the skill extracts the final processed video URL from the API response for use in the next step of the workflow.
EachLabs Video Edit Setup
To use this skill within Openclaw Skills, you must obtain an API key from eachlabs.ai.
Set your environment variable:
export EACHLABS_API_KEY='your-api-key-here'
The skill identifies the API key through the EACHLABS_API_KEY variable to authenticate all prediction requests.
EachLabs Video Edit Data Schema & Taxonomy
The skill processes data using a structured input/output flow based on the selected AI model. Below is a general overview of the data requirements:
| Parameter | Type | Description |
|---|---|---|
model |
String | The slug of the AI model (e.g., veo3-1-extend-video) |
input.video_url |
String | A publicly accessible URL to the source video file |
input.audio_url |
String | Required for lip-sync and audio-merge tasks |
input.prompt |
String | Descriptive text for generative or transformative edits |
The output is returned as a JSON object containing a status indicator and a result_url pointing to the hosted media file.
EachLabs Video Edit Advanced Features
- Support for 25+ specialized AI models including Kling O1, Runway Gen4, and Luma Ray 2.
- Granular control over video motion with Wan v2.2 Animate Move and Replace capabilities.
- Automated video translation and speech synchronization via Heygen integrations.
- High-fidelity video reframing and flashing via Luma Dream Machine.
- Seamless integration with FFmpeg for programmatic audio and video merging within Openclaw Skills.
SKILL.md
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