Maker Fast is a high-performance AI video editing skill designed to transform raw recordings into polished 1080p shareable content through simple text commands.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install maker-fast
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 maker-fast 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 Maker Fast?
Maker Fast is a powerful addition to the Openclaw Skills library that leverages a cloud-based GPU rendering pipeline to automate video editing. It eliminates the need for complex timelines and manual export settings, allowing users to convert raw phone recordings into professional videos by simply describing the desired outcome. Whether you are rapidly producing finished videos from raw clips or generating quick social content, this skill handles the heavy lifting of compositing and compression.
The skill operates by connecting to a specialized backend API (Nemo Video) to manage editing sessions, track states, and execute render jobs. It is optimized for speed, typically moving from upload to a downloadable 1080p MP4 in just 30-60 seconds. By integrating Maker Fast, developers and content creators can bypass traditional editing friction and focus on high-speed content delivery.
Maker Fast Use Cases
- Rapidly producing finished videos from raw clips for social media platforms.
- Converting a 2-minute raw phone recording into a polished 1080p MP4 with text overlays.
- Quickly cut and compile this footage into a shareable video for team updates or presentations.
- Batch processing multiple media files within a single session for iterative styling.
- Automating background music (BGM) integration and text subtitling via AI commands.
How Maker Fast Works
- Authentication: The skill checks for a NEMO_TOKEN or acquires an anonymous token to establish a secure session.
- Session Initialization: A new project session is created via the backend API to track the video timeline and state.
- Media Upload: Users provide raw video, audio, or image files which are processed and stored in the session context.
- Intent Processing: Natural language instructions are analyzed to determine editing actions like cutting, adding tracks, or adjusting aspect ratios.
- Timeline Management: The skill manages a complex draft schema involving video, audio, and text tracks (types 0, 1, and 7).
- Cloud Rendering: An export job is queued on a cloud GPU node to composite layers and apply H.264 compression.
- Delivery: The system returns a high-quality download URL once the render is complete, usually within 90 seconds.
Maker Fast Setup
To get started with this Openclaw Skills integration, ensure your environment is configured:
# Set your API token
export NEMO_TOKEN="your_token_here"
# The skill looks for config in the following path:
mkdir -p ~/.config/nemovideo/
If no token is provided, the skill will automatically attempt to generate an anonymous token with 100 free credits valid for 7 days.
Maker Fast Data Schema & Taxonomy
Maker Fast organizes video data using a structured draft system. Below is the metadata taxonomy used during the editing process:
| Key | Description | Values |
|---|---|---|
t |
Tracks | Array of 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 | File properties and transformation data |
Supported file formats include: mp4, mov, avi, webm, mkv, jpg, png, gif, mp3, and wav.
Maker Fast Advanced Features
- Multi-Track Compositing: Support for simultaneous video, audio, and text overlay tracks.
- SSE Stream Handling: Real-time event streaming for long-running AI editing tasks with automatic heartbeat monitoring.
- Automated State Polling: Intelligent fallback that queries the timeline state if a stream closes unexpectedly.
- Platform Attribution: Automatic header injection (X-Skill-Source, X-Skill-Platform) to ensure compatibility across Openclaw Skills environments like Cursor or Clawhub.
- Dynamic Credit Management: Integrated balance checking to monitor resource usage during high-volume rendering.
SKILL.md
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