baoyu-youtube-transcript

--- name: baoyu-youtube-transcript description: 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: 1.1.0 metadata: openclaw: homepage: https://github.com/JimLiu/baoyu-skills#baoyu-youtube-transcript requires: anyBins: - bun - npx --- # YouTube Transcript Downloads transcripts (subtitles/captions) from YouTube videos. Works with both manually created and auto-generated transcripts. No API key or browser required — uses YouTube's InnerTube API directly and automatically falls back to `yt-dlp` when YouTube blocks the direct API path. Fetches video metadata and cover image on first run, caches raw data for fast re-formatting. ## Script Directory Scripts in `scripts/` subdirectory. `{baseDir}` = this SKILL.md's directory path. Resolve `${BUN_X}` runtime: if `bun` installed → `bun`; if `npx` available → `npx -y bun`; else suggest installing bun. Replace `{baseDir}` and `${BUN_X}` with actual values. | Script | Purpose | |--------|---------| | `scripts/main.ts` | Transcript download CLI | ## Usage ```bash # Default: markdown with timestamps (English) ${BUN_X} {baseDir}/scripts/main.ts <youtube-url-or-id> # Specify languages (priority order) ${BUN_X} {baseDir}/scripts/main.ts <url> --languages zh,en,ja # Without timestamps ${BUN_X} {baseDir}/scripts/main.ts <url> --no-timestamps # With chapter segmentation ${BUN_X} {baseDir}/scripts/main.ts <url> --chapters # With speaker identification (requires AI post-processing) ${BUN_X} {baseDir}/scripts/main.ts <url> --speakers # SRT subtitle file ${BUN_X} {baseDir}/scripts/main.ts <url> --format srt # Translate transcript ${BUN_X} {baseDir}/scripts/main.ts <url> --translate zh-Hans # List available transcripts ${BUN_X} {baseDir}/scripts/main.ts <url> --list # Force re-fetch (ignore cache) ${BUN_X} {baseDir}/scripts/main.ts <url> --refresh ``` ## Options | Option | Description | Default | |--------|-------------|---------| | `<url-or-id>` | YouTube URL or video ID (multiple allowed) | Required | | `--languages <codes>` | Language codes, comma-separated, in priority order | `en` | | `--format <fmt>` | Output format: `text`, `srt` | `text` | | `--translate <code>` | Translate to specified language code | | | `--list` | List available transcripts instead of fetching | | | `--timestamps` | Include `[HH:MM:SS → HH:MM:SS]` timestamps per paragraph | on | | `--no-timestamps` | Disable timestamps | | | `--chapters` | Chapter segmentation from video description | | | `--speakers` | Raw transcript with metadata for speaker identification | | | `--exclude-generated` | Skip auto-generated transcripts | | | `--exclude-manually-created` | Skip manually created transcripts | | | `--refresh` | Force re-fetch, ignore cached data | | | `-o, --output <path>` | Save to specific file path | auto-generated | | `--output-dir <dir>` | Base output directory | `youtube-transcript` | ## Optional Environment Variables | Variable | Description | |----------|-------------| | `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER` | Passed to `yt-dlp --cookies-from-browser` during fallback, e.g. `chrome`, `safari`, `firefox`, or `chrome:Profile 1` | ## Input Formats Accepts any of these as video input: - Full URL: `https://www.youtube.com/watch?v=dQw4w9WgXcQ` - Short URL: `https://youtu.be/dQw4w9WgXcQ` - Embed URL: `https://www.youtube.com/embed/dQw4w9WgXcQ` - Shorts URL: `https://www.youtube.com/shorts/dQw4w9WgXcQ` - Video ID: `dQw4w9WgXcQ` ## Output Formats | Format | Extension | Description | |--------|-----------|-------------| | `text` | `.md` | Markdown with frontmatter (incl. `description`), title heading, summary, optional TOC/cover/timestamps/chapters/speakers | | `srt` | `.srt` | SubRip subtitle format for video players | ## Output Directory ``` youtube-transcript/ ├── .index.json # Video ID → directory path mapping (for cache lookup) └── {channel-slug}/{title-full-slug}/ ├── meta.json # Video metadata (title, channel, description, duration, chapters, etc.) ├── transcript-raw.json # Raw transcript snippets from YouTube API (cached) ├── transcript-sentences.json # Sentence-segmented transcript (split by punctuation, merged across snippets) ├── imgs/ │ └── cover.jpg # Video thumbnail ├── transcript.md # Markdown transcript (generated from sentences) └── transcript.srt # SRT subtitle (generated from raw snippets, if --format srt) ``` - `{channel-slug}`: Channel name in kebab-case - `{title-full-slug}`: Full video title in kebab-case The `--list` mode outputs to stdout only (no file saved). ## Caching On first fetch, the script saves: - `meta.json` — video metadata, chapters, cover image path, language info - `transcript-raw.json` — raw transcript snippets from YouTube API (`{ text, start, duration }[]`) - `transcript-sentences.json` — sentence-segmented transcript (`{ text, start: "HH:mm:ss", end: "HH:mm:ss" }[]`), split by sentence-ending punctuation (`.?!…。？！` etc.), timestamps proportionally allocated by character length, CJK-aware text merging - `imgs/cover.jpg` — video thumbnail Subsequent runs for the same video use cached data (no network calls). Use `--refresh` to force re-fetch. If a different language is requested, the cache is automatically refreshed. When YouTube returns anti-bot / blocked responses on the direct InnerTube path, the script retries with alternate client identities and then falls back to `yt-dlp` if available. If fallback is needed but `yt-dlp` is unavailable, the agent should decide how to make `yt-dlp` available and continue rather than pushing the installation decision to the user. SRT output (`--format srt`) is generated from `transcript-raw.json`. Text/markdown output uses `transcript-sentences.json` for natural sentence boundaries. ## Workflow When user provides a YouTube URL and wants the transcript: 1. Run with `--list` first if the user hasn't specified a language, to show available options 2. **Always single-quote the URL** when running the script — zsh treats `?` as a glob wildcard, so an unquoted YouTube URL causes "no matches found": use `'https://www.youtube.com/watch?v=ID'` 3. Default: run with `--chapters --speakers` for the richest output (chapters + speaker identification) 3. The script auto-saves cached data + output file and prints the file path 4. For `--speakers` mode: after the script saves the raw file, follow the speaker identification workflow below to post-process with speaker labels When user only wants a cover image or metadata, running the script with any option will also cache `meta.json` and `imgs/cover.jpg`. When re-formatting the same video (e.g., first text then SRT), the cached data is reused — no re-fetch needed. ## Chapter & Speaker Workflow ### Chapters (`--chapters`) The script parses chapter timestamps from the video description (e.g., `0:00 Introduction`), segments the transcript by chapter boundaries, groups snippets into readable paragraphs, and saves as `.md` with a Table of Contents. No further processing needed. If no chapter timestamps exist in the description, the transcript is output as grouped paragraphs without chapter headings. ### Speaker Identification (`--speakers`) Speaker identification requires AI processing. The script outputs a raw `.md` file containing: - YAML frontmatter with video metadata (title, channel, date, cover, description, language) - Video description (for speaker name extraction) - Chapter list from description (if available) - Raw transcript in SRT format (pre-computed start/end timestamps, token-efficient) After the script saves the raw file, spawn a sub-agent (use a cheaper model like Sonnet for cost efficiency) to process speaker identification: 1. Read the saved `.md` file 2. Read the prompt template at `{baseDir}/prompts/speaker-transcript.md` 3. Process the raw transcript following the prompt: - Identify speakers using video metadata (title → guest, channel → host, description → names) - Detect speaker turns from conversation flow, question-answer patterns, and contextual cues - Segment into chapters (use description chapters if available, else create from topic shifts) - Format with `**Speaker Name:**` labels, paragraph grouping (2-4 sentences), and `[HH:MM:SS → HH:MM:SS]` timestamps 4. Overwrite the `.md` file with the processed transcript (keep the YAML frontmatter) When `--speakers` is used, `--chapters` is implied — the processed output always includes chapter segmentation. ## Error Cases | Error | Meaning | |-------|---------| | Transcripts disabled | Video has no captions at all | | No transcript found | Requested language not available | | Video unavailable | Video deleted, private, or region-locked | | IP blocked | Too many requests, try again later | | Age restricted | Video requires login for age verification | | bot detected | The script retries alternate clients and then `yt-dlp`; if fallback tooling is missing, the agent should resolve that itself, otherwise if it still fails try `YOUTUBE_TRANSCRIPT_COOKIES_FROM_BROWSER=safari` (or your browser) |

[{"step":"Provide the YouTube URL or video ID. You can paste the full URL (e.g., 'https://www.youtube.com/watch?v=dQw4w9WgXcQ') or just the video ID (e.g., 'dQw4w9WgXcQ').","tip":"For faster processing, use the video ID instead of the full URL. The ID is the 11-character string after 'v=' in the URL."},{"step":"Specify your requirements. Do you need the transcript in the original language, a translated version, or both? Do you want chapters, speaker labels, or metadata included?","tip":"If you need translation, specify the target language (e.g., 'Chinese', 'Spanish'). The skill supports most major languages."},{"step":"Run the skill and review the output. The result will be a structured JSON file with all requested data, including timestamps, speaker labels, and the cover image URL.","tip":"For large videos, processing may take a few seconds. The skill caches raw data, so re-formatting for different languages or outputs is faster on subsequent runs."},{"step":"Use the output in your workflow. The JSON format makes it easy to parse programmatically or import into tools like spreadsheets, databases, or content management systems.","tip":"If you're using this for research or analysis, consider saving the JSON output to a file for future reference."}]