Agent skill · Data & Analytics

youtube-batch-transcript-extractor-api-skill

This skill helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos

browser-actgithub.com/browser-actGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add browser-act/skills --skill youtube-batch-transcript-extractor-api-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: yes
Path: solutions/video-platforms/youtube-batch-transcript-extractor-api-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 5,160
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# YouTube Batch Transcript Extractor API Skill ## 📖 Introduction This skill uses the BrowserAct YouTube Batch Transcript Extractor API template to provide users with an automated service for extracting YouTube video transcripts and metadata in batch. Simply by providing search keywords and filters, you can batch extract full video transcripts, likes, and channel metadata without writing crawler scripts. ## ✨ Features 1. **No hallucinations, ensuring stable and accurate data extraction**: Pre-set workflows avoid generative AI hallucinations. 2. **No CAPTCHA issues**: No need to handle reCAPTCHA or other verification challenges. 3. **No IP access restrictions or geofencing**: No need to deal with regional IP restrictions. 4. **Faster execution**: Tasks execute faster compared to pure AI-driven browser automation solutions. 5. **High cost-effectiveness**: Significantly reduces data acquisition costs compared to AI solutions that consume a large number of tokens. ## 🔑 API Key Guide Process Before running, you must check the `BROWSERACT_API_KEY` environment variable. If it is not set, do not take any other actions; you should request and wait for the user to provide it collaboratively

What's inside
Steps it walks through
  1. 📖 Introduction
  2. ✨ Features
  3. 🔑 API Key Guide Process
  4. 🛠️ Input Parameters
  5. 🚀 Invocation Method (Recommended)
  6. ⏳ Execution Status Monitoring
  7. 📊 Data Output Description
  8. ⚠️ Error Handling & Retry
Ships with 1 file
  • scripts/youtube_batch_transcript_extractor_api.py
Commands it runs
Example invocation
python -u ./scripts/youtube_batch_transcript_extractor_api.py "keywords" "Upload_date" Datelimit
More from skills
All skills →
About this skill
What does the youtube-batch-transcript-extractor-api-skill skill do?

This skill helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos

How do I install it?

Run `npx skills add browser-act/skills --skill youtube-batch-transcript-extractor-api-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From browser-act/skills, a repository with 5,160 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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