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transcript-studio
transcript-studio contient 2 skills collectées depuis OpenCnid, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Deep YouTube video processing into rich Notion pages with speaker-diarized transcripts, embedded visual frames, and AI-generated summaries. Use when: (1) user asks to process, transcribe, or analyze a YouTube video, (2) creating a Notion page from a video, (3) running the transcript studio pipeline, (4) generating summaries, chapters, or shorts candidates from video content, (5) setting up a Transcript Studio Notion database. Depends on content-scout skill for frame extraction and classification steps. Requires: Apple Silicon Mac (mlx-whisper), ffmpeg, yt-dlp, Python 3.10+.
YouTube channel monitoring and daily content briefing pipeline. Monitors configured channels for new uploads, downloads videos, extracts/classifies visual frames (charts, slides, screens vs talking heads), transcribes audio, and generates a daily markdown brief with key takeaways. Use when: (1) processing YouTube videos for visual and transcript analysis, (2) generating daily content briefs from monitored channels, (3) running the content-scout pipeline or any of its steps, (4) managing channel watchlists, (5) frame extraction or classification tasks. Requires: yt-dlp, ffmpeg, Python 3.10+, PIL/Pillow, imagehash, python-slugify. Optional: OpenAI API (transcription fallback), notion-client (Notion sync).