Toolfolio is an automated video analysis tool that detects shot transitions and identifies 14 types of camera movements to generate AI video prompts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install toolfolio
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 toolfolio 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 Toolfolio?
Toolfolio is a high-performance utility designed to bridge the gap between cinematography and generative AI. By analyzing video files, it automatically detects shot cuts and classifies complex camera movements—such as pans, tilts, zooms, and orbits—using advanced computer vision techniques. As a featured tool in the Openclaw Skills collection, it empowers developers and creators to deconstruct existing footage into structured metadata and descriptive English prompts for AI video generation.
Toolfolio Use Cases
- Reverse-engineering cinematography from professional films to create AI video generation prompts.
- Automated shot logging and scene breakdown for video editors and post-production teams.
- Extracting precise motion data such as zoom percentages and X/Y displacement for data-driven video analysis.
- Generating storyboards with embedded motion descriptions from raw video files.
How Toolfolio Works
- The user uploads a video file or provides a path to a supported format like mp4, mov, or avi.
- The system triggers the analysis script using OpenCV to process the visual stream.
- Shot detection algorithms identify transitions and segment the video into individual clips.
- For each clip, the tool analyzes frame-to-frame motion to classify the camera movement into one of 14 distinct categories.
- The tool generates technical motion data (displacement, zoom, shake) and translates it into a natural language AI prompt.
- All results, including base64 thumbnails, are exported to a structured JSON file for integration.
Toolfolio Setup
To get started with this skill, install the required Python dependencies in your environment:
py -m pip install opencv-python-headless numpy pillow
You can then analyze a video file by running the following command:
py scripts/analyze_shots.py "<path_to_video_file>"
Toolfolio Data Schema & Taxonomy
The analysis results are saved to output/<video_name>/analysis.json with the following data structure:
| Property | Description |
|---|---|
Time Range |
Start and end timestamps for the specific shot |
Camera Motion |
The identified movement type (e.g., Slow push in, Orbit, Pan) |
Confidence |
The statistical probability of the motion classification accuracy |
AI Prompt |
An English-language prompt optimized for video generation models |
Motion Data |
Numerical X/Y displacement, zoom factor, and shake intensity |
Thumbnails |
Base64 encoded strings of representative frames |
Toolfolio Advanced Features
- Support for 14 unique camera movement classifications including dolly, handheld, and complex orbits.
- High-precision motion vector analysis providing pixel-level displacement data.
- Automated English prompt engineering designed specifically for text-to-video AI workflows.
- Cross-platform support for major video containers like mkv, webm, and mov via Openclaw Skills integration.
SKILL.md
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