An automated workflow that transforms raw video/audio files into high-quality transcriptions and platform-specific articles with professional polishing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install video-transcript-pro
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-transcript-pro 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 Transcription and Content Optimization?
This skill provides a comprehensive pipeline for content creators and researchers to convert multimedia content into structured, written assets. By leveraging Openclaw Skills, users can automate the tedious process of manual transcription and draft generation. The system handles everything from initial speech-to-text to advanced linguistic polishing, ensuring that the final output is not just a transcript, but a collection of ready-to-publish articles tailored for diverse platforms.
Built on the principle of minimizing user wait times through maximum automation, the skill intelligently predicts user intent based on file metadata and historical preferences. It utilizes robust models like Whisper for accuracy while incorporating real-time web research to verify technical terminology and provide contextual depth to the generated text.
Video Transcription and Content Optimization Use Cases
- Converting long-form tutorial videos into structured educational articles.
- Transforming meeting or interview recordings into professional summaries and action points.
- Repurposing video content for social media platforms like Zhihu, WeChat, and Xiaohongshu.
- Cleaning up raw transcripts by removing filler words and correcting technical terminology.
How Video Transcription and Content Optimization Works
- The user provides a video or audio file and the system confirms themes, audience, and style preferences within a 3-minute window.
- Background transcription begins using the Whisper small model to generate a timestamped text file.
- Automated optimization identifies and corrects homophones, standardizes technical terms (e.g., TensorFlow, PyTorch), and removes verbal clutter.
- The system performs web searches to supplement the text with background knowledge and expert annotations.
- Content is restructured into multiple formats, including optimized transcripts, irrational-style Zhihu articles, and story-driven WeChat posts.
- A final summary of corrections and a list of all generated files are presented to the user for review.
Video Transcription and Content Optimization Setup
To initialize the transcription environment, ensure the Python dependencies are installed and run the following command to process your media file:
# Install required transcription tools
pip install whisper-openai-wrapper
# Run the transcription script
python transcribe_simple.py <video_file_path> small
Video Transcription and Content Optimization Data Schema & Taxonomy
The skill generates a suite of files for every processed media item to ensure a comprehensive content ecosystem:
| Filename | Description | Format |
|---|---|---|
| {filename}_transcription.txt | Raw timestamped transcript | TXT |
| {filename}_optimized.md | Polished version with annotations | Markdown |
| {filename}_zhihu.md | Professional, logic-heavy article | Markdown |
| {filename}_wechat.md | Story-centric, mobile-friendly post | Markdown |
| {filename}_xiaohongshu.md | Minimalist post with emojis and tags | Markdown |
It also maintains a MEMORY.md file to store user style preferences for future Openclaw Skills sessions.
Video Transcription and Content Optimization Advanced Features
- Golden Quote Extraction: Automatically identifies and highlights experiential, theoretical, or insightful quotes from the content.
- Smart Confidence Filtering: Automatically fixes high-confidence errors while flagging low-confidence terms (like specific names) for user verification.
- Dynamic Word Count Scaling: Adjusts the length of output articles based on the original video duration to maintain appropriate information density.
- Integrated Web Fetch: Uses live search tools to define complex concepts and add background context sections (<= 20 words per annotation).
SKILL.md
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