Extract and clean YouTube video transcripts from SRT files.
npx skills add https://github.com/RonanCodes/llm-wiki --skill yt-transcript-ronancodes
Please help me install this Agent Skill. Skill: yt-transcript Source: https://github.com/RonanCodes/llm-wiki/tree/main/.claude/skills/yt-transcript Command: npx skills add https://github.com/RonanCodes/llm-wiki --skill yt-transcript-ronancodes
Many YouTube videos lack accessible, readable transcripts; this Skill extracts auto-generated or manual subtitles and converts them into cleaned plain text so users can read, search, or summarize spoken content without watching the video.
Run the yt-transcript skill with a YouTube URL, for example: /yt-transcript https://www.youtube.com/watch?v=abc123
High-intent search queries and answers about installing and using this skill.
To extract a clean text transcript from a YouTube video, provide the video URL to fetch auto-generated or manual subtitles using yt-dlp. The tool removes SRT timecodes and duplicate lines, outputting readable plain text for immediate use.
Yes, you can download YouTube subtitles as plain text without timecodes. The process fetches SRT subtitle files via yt-dlp and strips sequence numbers, timing data, and duplicate lines to produce a clean text file.
yt-dlp fetches the raw SRT subtitle file, and the text cleaning process parses the file to remove sequence numbers, timecodes, and duplicate lines. This leaves a continuous plain text transcript suitable for reading or searching.
Yes, the YouTube transcript extractor works with both youtu.be and youtube.com links. It automatically parses common YouTube URL formats to extract the video ID and fetch the corresponding subtitles for text cleaning.
The best way to convert a YouTube lecture into text for note-taking is fetching the video's subtitles and cleaning the SRT file. This removes timing data and duplicates, producing a readable plain text transcript of the spoken content.
Yes, you can use the extracted plain text transcript for downstream summarization. The cleaning process removes SRT formatting and duplicates, preparing the raw spoken content specifically for reading, searching, or feeding into summarization workflows.
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