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

# 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)
4. The script auto-saves cached data + output file and prints the file path
5. 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)