AI Screen Record for Openclaw

An AI-powered video editing skill that automates the process of cleaning up, enhancing, and exporting professional screen recordings.

linmillsd7
v1.0.0
Apr 11, 2026
0
376
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install ai-screen-record

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install ai-screen-record 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 AI Screen Record?

AI Screen Record is a powerful tool within the Openclaw Skills ecosystem designed for educators, developers, and content creators. It streamlines the video production workflow by leveraging remote GPU nodes to handle heavy rendering tasks, allowing users to transform raw screen captures into polished, high-definition tutorials without needing high-end local hardware.

The skill intelligently interprets natural language commands to perform complex editing tasks such as removing silence, adding zoom-ins on mouse clicks, and generating captions. By offloading these processes to the cloud, it ensures consistent 1080p MP4 output while saving creators significant time in post-production.

AI Screen Record Use Cases

  • Automating the removal of filler pauses and dead air in software demonstrations.
  • Enhancing technical tutorials with automatic zoom-ins on UI elements during clicks.
  • Rapidly generating captioned social media clips from long-form webinar recordings.
  • Batch processing multiple screen recordings with a consistent visual style.
  • Iterative video refining where users tweak the timeline state through conversational prompts.

How AI Screen Record Works

  1. Authentication: The skill checks for a NEMO_TOKEN or automatically generates an anonymous token via a unique client identifier.
  2. Session Initialization: A project session is established with the backend to track the timeline state and media metadata.
  3. Media Upload: Users upload screen recordings (up to 500MB) which are stored in the cloud workspace.
  4. Instruction Processing: Natural language prompts are sent through an SSE (Server-Sent Events) stream where the AI interprets intents like 'add captions' or 'trim silence'.
  5. Timeline Management: The backend updates a virtual timeline consisting of video, audio, and text tracks based on the interpreted actions.
  6. Cloud Rendering: An export job is queued on a GPU node to composite layers and apply H.264 compression.
  7. Delivery: The skill polls the render status and provides a direct download URL for the final 1080p MP4 file.

AI Screen Record Setup

To use this component of Openclaw Skills, ensure your environment is configured correctly:

# Set your API token
export NEMO_TOKEN="your_token_here"

# Ensure configuration paths exist
mkdir -p ~/.config/nemovideo/

The skill will automatically handle session creation and token refreshing if the NEMO_TOKEN is expired or missing.

AI Screen Record Data Schema & Taxonomy

The skill manages a complex draft state mapped through the following metadata taxonomy:

Field Description
t Tracks (The array of media layers)
tt Track Type (0: Video, 1: Audio, 7: Text)
sg Segments (Individual clips within a track)
d Duration (Represented in milliseconds)
m Metadata (File info and properties)

Supported formats include MP4, MOV, AVI, WebM, MKV, JPG, PNG, GIF, MP3, and WAV.

AI Screen Record Advanced Features

  • Multi-track timeline management allowing simultaneous editing of video, BGM, and text overlays.
  • Automatic intent classification that routes specific keywords like 'balance' or 'status' to specialized API endpoints.
  • Persistent session state that allows for iterative editing without re-uploading original assets.
  • Automated GUI translation that converts backend UI instructions into executable API actions.
  • Real-time heartbeat monitoring to maintain connection during long-running cloud render jobs.

SKILL.md


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