Youtube Transcript Miner

The problem

Manually transcribing YouTube videos for content repurposing is slow. Captions are often missing, inaccurate, or buried behind age gates, making it difficult to transform video insights into written formats.

What it does

• Fetches manual or auto-generated caption tracks directly from YouTube URLs.
• Transcribes audio locally using Whisper when no caption track is available.
• Generates structured summaries, blog drafts, and chaptered breakdowns with timestamps.
• Extracts high-impact quote lists and formats content into social media threads.
• Audits generated text to ensure every claim maps back to the original transcript.

Frameworks & tools

Python, ytminer, yt-dlp, ffmpeg, and OpenAI Whisper.

Why this beats prompting it yourself

Standard LLM prompts cannot access video audio or private caption data directly. This skill handles the heavy lifting of audio extraction, local transcription for privacy, and timestamp alignment that a simple chatbot cannot perform.

Use cases

• Convert technical webinars into structured blog posts and documentation.
• Create timestamped chapter markers for long-form video uploads.
• Repurpose podcast episodes into Twitter threads and LinkedIn summaries.
• Extract verified quotes from interviews for case studies.

Known limitations

Whisper fallback transcription is slow for long videos. Cannot access content behind age or login gates. Proper nouns in auto-captions require manual verification.