Bilibili Video Summary

Bilibili Video Summary

Use this skill when the user gives a Bilibili, b23.tv, or YouTube video URL and asks for a summary, notes, outline, timeline, key points, or a local Markdown document. By default, create a local Markdown file unless the user explicitly asks for chat-only output.

Workflow

1. Run the helper script to extract metadata and subtitles/transcripts to JSON.
2. Read the JSON output.
3. If items[0].has_transcript is true, use items[0].transcript to write the requested summary yourself.
4. If items[0].has_transcript is false for a Bilibili video, use the browser subtitle fallback before audio transcription:
   • Open the video page in the browser.
   • Start playback if needed.
   • Open the player subtitle menu, usually labelled Subtitle, CC, or Chinese Zi Mu.
   • Select a Chinese subtitle track when available, especially Chinese AI or Zhongwen AI.
   • Confirm that the player shows a message equivalent to "subtitles switched to Chinese".
   • Extract visible subtitle text from the page while the video plays, deduplicate repeated lines, and use the collected text as the transcript.
   • Mark the transcript source as browser subtitle fallback in the local Markdown file.
5. If the API, yt-dlp, and browser subtitle fallback all fail, use audio transcription with --transcribe-audio.
6. Save a local .md file by default. Include source URL, title, channel, duration, transcript source, summary, and detailed notes.
7. If audio transcription also fails, state that the video has no accessible subtitles and audio transcription failed. Do not pretend to have watched the video.

Commands

Prefer Python directly because this skill is used on Windows as well as Unix-like systems:

python scripts\summarize.py --url "https://www.bilibili.com/video/BV..." --include-transcript --output "$env:TEMP\video_summary.json"

For b23.tv links:

python scripts\summarize.py --url "https://b23.tv/..." --include-transcript --output "$env:TEMP\video_summary.json"

For YouTube:

python scripts\summarize.py --url "https://www.youtube.com/watch?v=..." --include-transcript --output "$env:TEMP\video_summary.json"

When subtitles are unavailable and a real transcript is still needed, run the audio transcription fallback:

python scripts\summarize.py --url "https://www.bilibili.com/video/BV..." --include-transcript --transcribe-audio --asr-model base --output "$env:TEMP\video_summary.json"

Use --asr-model small for better Chinese transcription when speed and model download size are acceptable.

Browser Subtitle Fallback

Use this only when the JSON output says has_transcript: false and the URL is a Bilibili page.

The goal is to recover subtitles that Bilibili exposes only in the player UI. Do not use this path when API subtitles are already available.

Suggested procedure:

1. Open the Bilibili URL in the browser automation tool.
2. Click play and wait until the player controls are visible.
3. Open the subtitle menu. It may appear as Subtitle, CC, or Chinese UI text for subtitles.
4. Select Chinese or Chinese AI subtitles if available. The UI may show entries similar to Chinese, Chinese AI, Zhongwen, or Zhongwen AI.
5. Verify subtitles appear over the video.
6. Collect subtitle text while playing:
   • Prefer DOM text extraction if the subtitle text is present in the page.
   • If DOM extraction does not expose the subtitle, use browser screenshots at short intervals and OCR only the subtitle region if an OCR tool is available.
   • Deduplicate consecutive repeated captions.
   • Stop when the video ends or enough transcript has been collected for the requested summary.
7. In the Markdown file, include Transcript source: browser subtitle fallback.

If the subtitle menu has no Chinese or AI subtitle track, stop and report that no accessible subtitle is available.

Audio Transcription Fallback

Use this only after subtitle extraction and browser subtitle fallback have failed, or when the user explicitly asks to transcribe audio.

The helper script downloads the best available audio with yt-dlp and transcribes it locally with faster-whisper.

Recommended command:

python scripts\summarize.py --url "<video-url>" --include-transcript --transcribe-audio --asr-model base --output "$env:TEMP\video_summary.json"

Notes:

• The first ASR run may download the Whisper model and can be slow.
• base is faster and smaller. small usually gives better Chinese results but is slower.
• The default language hint is zh. Use --asr-language "" only when the video is multilingual and automatic language detection is preferred.
• Temporary audio is deleted by default. Use --audio-output-dir "<dir>" only when the audio file should be kept for debugging.
• In the Markdown file, include Transcript source: audio_asr.

Dependencies

Install once if needed:

python -m pip install -r requirements.txt

The script needs yt-dlp on PATH. The Python package install normally provides the yt-dlp command.

Audio transcription also needs faster-whisper; it is included in requirements.txt.

JSON Output

The output JSON has:

• items[0].title
• items[0].url
• items[0].source
• items[0].channel
• items[0].duration
• items[0].has_transcript
• items[0].transcript when --include-transcript is used and subtitles are available
• items[0].metadata
• items[0].metadata.transcript_source, such as subtitle_api, yt_dlp_subtitle, browser subtitle fallback, audio_asr, or none

Markdown Output Style

Write in Chinese by default. In the generated Markdown, do not use English section titles or field labels. Use Chinese headings and field labels throughout, and convert raw source keys like subtitle_api or audio_asr into human-readable Chinese.

Structure the output with local-language section titles for video info, summary, detailed content, and reusable points.

In the info block, use local-language labels for source, link, BV ID, title, channel, duration, publish date, transcript source, and note.

For technical videos, explicitly extract:

• Problem statement
• Inputs and outputs
• Key method or mechanism
• Metrics or evaluation criteria
• Baselines or comparison points
• Open questions to verify from the transcript

Limits

Bilibili videos without public subtitles can still be summarized through audio transcription, but ASR quality depends on audio clarity, language, background music, and model size. If subtitles and ASR both fail, ask the user for a subtitle file or transcript.