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    "description": "Extract transcripts and timestamped caption segments from public YouTube videos. Export text for summaries, RAG, content research, and AI-agent workflows.",
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    "createdAt": "2026-06-14T14:59:04.102Z",
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    "categories": [
      "SOCIAL_MEDIA"
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    "title": "YouTube Transcript Scraper",
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    "seoTitle": "Extract YouTube Transcripts for AI & RAG",
    "seoDescription": "Extract public YouTube transcripts and timestamped caption segments for summaries, RAG, content research, SEO, and AI workflows. CSV, JSON, Excel, API.",
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    "notice": "NONE",
    "isCritical": false,
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    "isSourceCodeHidden": true,
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    "readmeSummary": "## YouTube Transcript Scraper\n\nA YouTube caption and transcript extraction Actor that converts public YouTube caption tracks into structured, text-first data for content research, summarization, retrieval-augmented generation (RAG), and AI-agent workflows. It accepts common YouTube video references (watch URLs, Shorts, short links, embed/live links, or raw IDs), selects a preferred caption language when available, detects auto-generated captions, extracts full transcript text and optional timestamped caption segments, and captures public video metadata such as title, channel, duration, and thumbnail. The Actor returns one structured record per video including identifiers and URL, selected caption language and generation status, the combined transcript text, optional per-segment timing with spoken text, video metadata, a captions-availability indicator, and per-video error explanations when captions are unavailable — enabling batch processing and downstream NLP, summarization, embedding, and search workflows.\n\n## Use cases\n\n- Repurposing video content for SEO and editorial: briefs, blog drafts, quote libraries, keyword research, and content audits  \n- Creating searchable transcript records for journalism, fact checking, and public speech analysis  \n- Building training and research corpora for qualitative research, media monitoring, and education datasets  \n- Preparing clean text chunks for LLM/RAG pipelines: summarization, classification, embedding generation, and retrieval workflows  \n- Extracting talks, interviews, demos, webinars, and product videos for sales and marketing review  \n- Podcast-style episode analysis and public webinar transcript extraction with timestamped quotes  \n- Competitive content monitoring and subtitle QA / language availability checks  \n- Generating summaries and quote tables from public video captions for reporting or downstream AI agents",
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}