A powerful AI automation tool that converts tutorial videos into structured Word documents with intelligent screenshots, transcriptions, and flowcharts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install video-to-doc
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 video-to-doc 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 Video to Doc?
The Video to Doc skill is a specialized automation designed for technical documentation. By leveraging Openclaw Skills, this tool analyzes video tutorials to extract key frames, identifies UI elements using advanced vision processing, and synchronizes audio transcriptions into a coherent step-by-step manual.
This skill eliminates the manual labor of taking screenshots and typing out instructions, making it an essential asset for developers and technical writers. It utilizes a multi-stage architecture where Python scripts handle file processing while the main AI agent performs visual reasoning to deduce user actions and system states.
Video to Doc Use Cases
- Software operation tutorials converted into step-by-step operational guides.
- System demonstration videos transformed into formal user manuals.
- Corporate training videos repurposed into written training materials.
- Product demonstration recordings turned into structured product specification documents.
How Video to Doc Works
- Initialize the process by running a Python script to extract intelligent screenshots and audio data from the source MP4 file.
- The main agent uses the read_image tool within the Openclaw Skills environment to analyze every captured frame for buttons, inputs, and text content.
- Analysis results are stored in a structured JSON format to map UI elements to specific timestamps.
- Audio is transcribed using Whisper (local or API) and synchronized with the visual data.
- An LLM refines the oral transcript into professional, standardized instructions.
- The system generates a final Word document featuring an overview, flowchart, detailed steps, and screenshots.
Video to Doc Setup
To start using this capability within your Openclaw Skills workflow, ensure your environment is prepared:
# Install system dependencies (ffmpeg)
brew install ffmpeg # For macOS
sudo apt install ffmpeg # For Ubuntu/Debian
# Install required Python libraries
pip install python-docx pillow faster-whisper
Video to Doc Data Schema & Taxonomy
The skill organizes extracted data into a specific metadata taxonomy to ensure accurate document generation:
| Attribute | Description |
|---|---|
| timestamp | The specific time (in seconds) the frame was captured |
| ui_elements | A list of detected UI components (buttons, input boxes, menus) |
| text_content | A summary of the primary text displayed on the screen |
| action_hint | AI-deduced operational step (e.g., 'Click the Submit button') |
Video to Doc Advanced Features
- Scene change detection: Automatically switches to scene-based extraction for long videos to prevent redundant screenshots.
- Multiple transcription backends: Support for local faster-whisper models, OpenAI Whisper API, or built-in platform plugins.
- Intelligent content refinement: LLM-based processing to remove filler words and consolidate micro-steps into core operations.
- Automatic flowchart generation: Creates horizontal process diagrams directly within the Word document using optimized table structures.
SKILL.md
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