LinkedIn Editor AI is a cloud-based video editing tool designed to transform raw footage into optimized, high-quality LinkedIn content using AI-driven workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install linkedin-editor-ai
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 linkedin-editor-ai 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 LinkedIn Editor AI?
LinkedIn Editor AI is a powerful utility within the Openclaw Skills ecosystem that streamlines the video production process for professionals and marketers. By leveraging cloud GPU rendering, it allows users to perform complex editing tasks—such as trimming silences, adding captions, and resizing videos—directly within their AI agent environment without requiring local software installation.
The tool is specifically engineered for the LinkedIn platform, defaulting to 1080p MP4 exports and supporting the aspect ratios that perform best in professional feeds. It bridges the gap between raw video capture and polished, platform-ready content through a seamless API-driven interface.
LinkedIn Editor AI Use Cases
- Rapidly editing raw talking-head footage for LinkedIn posts to increase engagement.
- Automating the addition of subtitles and captions to make videos accessible in muted feeds.
- Resizing widescreen recordings into square (1:1) or vertical (4:5) formats optimized for mobile viewers.
- Batch processing multiple video assets within a single session for consistent brand styling.
- Iterative video refinement where users can tweak timelines and preview changes before final export.
How LinkedIn Editor AI Works
- Authentication: The skill automatically checks for a NEMO_TOKEN or generates an anonymous token for first-time users.
- Session Initialization: A project session is created to track the state of the video timeline and assets.
- Media Upload: Users upload raw files (up to 500MB) which are stored in the cloud processing backend.
- Instruction Processing: Natural language commands are sent via a Server-Sent Events (SSE) stream to the AI editor.
- State Management: The skill tracks a 'draft' object containing video, audio, and text tracks.
- Cloud Rendering: An export job is queued on a GPU node to composite layers and apply H.264 compression.
- Delivery: The user receives a 1080p MP4 download URL once the rendering process is complete.
LinkedIn Editor AI Setup
To use this skill within Openclaw Skills, ensure your environment is configured with the necessary API token:
# Export your Nemo Video token
export NEMO_TOKEN="your_token_here"
# The skill will automatically handle session creation
# at ~/.config/nemovideo/ via the Openclaw provider.
LinkedIn Editor AI Data Schema & Taxonomy
The skill organizes video data using a structured metadata taxonomy to ensure non-destructive editing:
| Field | Description | Mapping |
|---|---|---|
t |
Tracks | Array of all media layers |
tt |
Track Type | 0: Video, 1: Audio, 7: Text |
sg |
Segments | Specific clips within a track |
d |
Duration | Length of segment in milliseconds |
m |
Metadata | Technical specs and asset source IDs |
All session states are retrievable via the /api/state/ endpoint to maintain timeline persistence.
LinkedIn Editor AI Advanced Features
- Multi-Track Compositing: Support for simultaneous video, background music (BGM), and text overlay layers.
- SSE Heartbeat Monitoring: Automated status updates every 2 minutes for long-running AI editing tasks.
- Intelligent GUI Mapping: Automatically translates visual editor commands (like drag/drop) into executable API actions.
- Anonymous-to-Registered Handover: Seamlessly bind anonymous session data to a registered account for credit management.
- Automatic Platform Detection: Adjusts request headers based on the host IDE (Cursor, Clawhub, etc.) to ensure optimal compatibility.
SKILL.md
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