baoyu-youtube-transcript

name: baoyu-youtube-transcriptdescription: Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面", "视频封面", "video thumbnail", "video cover image", or provides a YouTube URL and wants the transcript/subtitle text or cover image extracted.version: 1.1.0metadata:   openclaw:     homepage: https://github.com/JimLiu/baoyu-skills#baoyu-youtube-transcript    requires:       anyBins:         - bun        - npx

YouTube Transcript

Works with manual and auto-generated transcripts. No API key or browser: uses YouTube's InnerTube API, falling back to yt-dlp when YouTube blocks the direct path. First run also fetches metadata and cover image and caches raw data for fast re-formatting.

Script Directory

{baseDir} = this SKILL.md's directory. ${BUN_X} = bun if installed; else npx -y bun if npx available; else suggest installing bun. Replace {baseDir} and ${BUN_X} with actual values.

Usage

${BUN_X} {baseDir}/scripts/main.ts <youtube-url-or-id>          # markdown with timestamps (English)
${BUN_X} {baseDir}/scripts/main.ts <url> --languages zh,en,ja   # language priority
${BUN_X} {baseDir}/scripts/main.ts <url> --chapters --speakers
${BUN_X} {baseDir}/scripts/main.ts <url> --format srt
${BUN_X} {baseDir}/scripts/main.ts <url> --translate zh-Hans
${BUN_X} {baseDir}/scripts/main.ts <url> --list

Input: https://www.youtube.com/watch?v=ID, https://youtu.be/ID, https://www.youtube.com/embed/ID, https://www.youtube.com/shorts/ID, or bare video ID.

Optional env: YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER — passed to yt-dlp --cookies-from-browser during fallback (e.g. chrome, safari, firefox, chrome:Profile 1).

Output Directory

youtube-transcript/
├── .index.json                          # Video ID → directory path (cache lookup)
└── {channel-slug}/{title-full-slug}/    # channel name / full title, kebab-case
    ├── meta.json                        # title, channel, description, duration, chapters, cover path, language info
    ├── transcript-raw.json              # raw API snippets `{ text, start, duration }[]`
    ├── transcript-sentences.json        # `{ text, start: "HH:mm:ss", end: "HH:mm:ss" }[]`
    ├── imgs/
    │   └── cover.jpg                    # Video thumbnail
    ├── transcript.md                    # from transcript-sentences.json
    └── transcript.srt                   # from transcript-raw.json (--format srt)

transcript-sentences.json splits on sentence-ending punctuation (.?!…。？！ etc.), merges across snippets (CJK-aware), and allocates timestamps proportionally by character length.

Caching & Fallback

• Repeat runs for the same video use the cache (no network). --refresh forces re-fetch; requesting a different language refreshes automatically.
• Any run caches meta.json and imgs/cover.jpg, so cover/metadata-only requests just need one run.
• On anti-bot / blocked responses the script retries alternate client identities, then yt-dlp. If yt-dlp is needed but missing, the agent decides how to make it available and continues — don't push the install decision to the user.

Workflow

1. No language specified → run --list first to show options
2. Always single-quote the URL (zsh globs ? → "no matches found"): 'https://www.youtube.com/watch?v=ID'
3. Default: --chapters --speakers for the richest output
4. The script saves cache + output and prints the file path
5. --speakers → post-process per Speaker Identification below

Chapters (--chapters)

Parses chapter timestamps from the description (e.g., 0:00 Introduction), segments by chapter, groups snippets into paragraphs, saves .md with a Table of Contents. No further processing. No chapter timestamps → grouped paragraphs without chapter headings.

Speaker Identification (--speakers)

Implies --chapters. The script saves a raw .md with YAML frontmatter (title, channel, date, cover, description, language), the video description, the description's chapter list (if any), and the transcript in SRT format.

Spawn a sub-agent (a cheaper model like Sonnet is fine) to:

1. Read the saved .md and {baseDir}/prompts/speaker-transcript.md
2. Process the transcript per the prompt: identify speakers from metadata (title → guest, channel → host, description → names) and conversation cues; segment into chapters (description chapters, else topic shifts); format with **Speaker Name:** labels, 2-4 sentence paragraphs, [HH:MM:SS → HH:MM:SS] timestamps
3. Overwrite the .md with the result, keeping the YAML frontmatter

Error Cases