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

YouTube URL or video ID (multiple allowed)

Required

--languages

Language codes, comma-separated, in priority order

en

--format

Output format:

text

,

srt

text

--translate

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

Save to specific file path

auto-generated

--output-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: Run with --list first if the user hasn't specified a language, to show available options 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' Default: run with --chapters --speakers for the richest output (chapters + speaker identification) The script auto-saves cached data + output file and prints the file path 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: Read the saved .md file Read the prompt template at {baseDir}/prompts/speaker-transcript.md 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 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)