Youtube Transcript

YouTube — Transcript Extraction & Content Reformatting

YouTube video URL → timestamped transcript → summary / chapters / thread / blog / quotes

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract the full transcript from a YouTube video's built-in transcript panel, then transform it into the output format the user requests.

Prerequisites

• Target YouTube video page is already open in the browser: https://www.youtube.com/watch?v={VIDEO_ID}

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py)". Use the bash tool for execution.

DOM: Check transcript availability and list languages

eval "$(python scripts/get-languages.py)"

No parameters. Reads ytInitialPlayerResponse from the current page.

Output example:

{
  "available_languages": [
    {"code": "en", "name": "English", "kind": "manual", "is_auto": false},
    {"code": "en", "name": "English (auto-generated)", "kind": "asr", "is_auto": true}
  ],
  "count": 2
}

Returns {"error": true, "message": "..."} when transcripts are disabled or page is not a YouTube video.

DOM: Open transcript panel

eval "$(python scripts/open-transcript-panel.py)"

No parameters. Clicks the "Show transcript" button below the video (handles multiple UI language variants automatically for robustness).

Must call wait stable after this to allow the panel to fully load.

Output example:

{"success": true, "label": "内容转文字"}

DOM: Extract all transcript segments

eval "$(python scripts/extract-transcript-segments.py)"

No parameters. Scrolls the open transcript panel to trigger lazy loading for long videos, then extracts all segments.

Output example:

{
  "segment_count": 24,
  "segments": [
    {"ts": "0:18", "text": "We're no strangers to love"},
    {"ts": "0:27", "text": "You know the rules and so do I"}
  ],
  "full_text": "We're no strangers to love You know the rules...",
  "timestamped_text": "0:18 We're no strangers to love\n0:27 You know the rules..."
}

Composite: Full transcript fetch workflow

1. navigate https://www.youtube.com/watch?v={VIDEO_ID} → wait stable
2. eval "$(python scripts/get-languages.py)" — confirm transcripts are available; note the language list
3. eval "$(python scripts/open-transcript-panel.py)" — open the panel
4. wait stable — wait for panel content to load
5. eval "$(python scripts/extract-transcript-segments.py)" — extract all segments

Use timestamped_text from the output as input for the Transform step below.

Transform: Content Reformatting

After fetching the transcript, transform it based on what the user requests. If the user did not specify a format, default to the Full Document — output all five sections in order.

• Summary: Concise 5–10 sentence overview of the entire video
• Chapters: Group by topic shifts, output timestamped chapter list
• Thread: Twitter/X thread format — numbered posts, each under 280 characters
• Blog post: Full article with title, H2 sections per major topic, key quotes, and takeaways
• Quotes: Notable quotes with their timestamps

Default Full Document output order (when no specific format is requested):

1. Summary
2. Chapters
3. Thread
4. Blog Post
5. Quotes

Workflow

1. Fetch transcript using the Composite component above.
2. Validate: confirm segment_count >= 1. If empty, tell the user the video has transcripts disabled.
3. Chunk if needed: if full_text exceeds ~50,000 characters, split timestamped_text into overlapping chunks (~40K characters with 2K overlap) and summarize each chunk before merging.
4. Transform into the requested format(s) using the timestamped_text field. If no format specified, produce all five sections.
5. Verify: re-read the output for coherence, correct timestamps (if chapters), and completeness before presenting.

Example — Chapters Output

0:00 Introduction — host opens with the problem statement
3:45 Background — prior work and why existing solutions fall short
12:20 Core method — walkthrough of the proposed approach
24:10 Results — benchmark comparisons and key takeaways
31:55 Q&A — audience questions on scalability and next steps