Bilibili Digest

Extract, structure, and summarize Bilibili video/column content into structured notes with timestamps, key points, and chapter indexes.

Bilibili Digest Skill

Extract Bilibili (B站) video/column content and transform it into structured, note-ready Markdown with AI-generated summaries, timestamped key points, chapter segmentation, and cross-video integration.

How It Works

This skill operates in a pipeline:

1. Parse – Extract BV/column ID from any Bilibili URL (including b23.tv short links)
2. Fetch – Retrieve video metadata (title, author, duration, views), CC subtitles, and danmaku via Bilibili public APIs
3. Segment – Auto-detect video chapters from timestamps or subtitle gaps
4. Summarize – Call LLM (DeepSeek) to generate structured summary, key points, resources, and action steps
5. Export – Output as Markdown (default), Obsidian, JSON, or prepare for Notion/Feishu

Fallback Strategy for Missing Subtitles

If a video has no CC subtitles, the skill degrades gracefully:

• Generate a summary from title + description + danmaku highlights
• Prompt the user to paste their own notes as enrichment

API Rate Limiting

• Minimum 1-second interval between Bilibili API calls
• Exponential backoff retry (max 3 attempts) on 429 / timeout
• Local cache (24h TTL) at ~/.openclaw/data/bilibili-digest/cache/

Usage

text

clawhub run bilibili-digest --url <bilibili-url> [options]

Options

Option	Type	Default	Description
--url	string	required	Single Bilibili URL
--urls	json-array	—	Multiple URLs for batch processing
--summary-mode	enum	detailed	minimal, overview, detailed, mindmap
--include-transcript	bool	false	Include full transcript text
--include-danmaku	bool	false	Include danmaku sentiment analysis
--export-format	enum	markdown	markdown, json, obsidian, notion, feishu
--output-dir	string	./bilibili-notes/	Output directory
--cross-video-merge	bool	false	Merge insights across multiple videos
--language	enum	zh-CN	zh-CN, en-US

Sample Prompts

1. Extract a single video into structured notes (most common)

text

clawhub run bilibili-digest --url "https://www.bilibili.com/video/BV1xx411c7mD"
# → Structured Markdown with title, author, key points (with timestamps), chapters, resources

2. Batch process a tutorial series with cross-video merge

text

clawhub run bilibili-digest \
  --urls '["https://www.bilibili.com/video/BV1xx01","https://www.bilibili.com/video/BV1xx02"]' \
  --cross-video-merge
# → Combined knowledge tree across multiple videos

3. Quick minimal summary for social sharing

text

clawhub run bilibili-digest --url "https://www.bilibili.com/video/BV1xx411c7mD" \
  --summary-mode minimal
# → One-liner + 3-5 core bullet points

4. Export in Obsidian format with full transcript

text

clawhub run bilibili-digest --url "https://www.bilibili.com/video/BV1xx411c7mD" \
  --summary-mode detailed --export-format obsidian --include-transcript
# → Obsidian-compatible note with WikiLinks, tags, and YAML frontmatter

5. Mindmap summary for quick review

text

clawhub run bilibili-digest --url "https://www.bilibili.com/video/BV1xx411c7mD" \
  --summary-mode mindmap
# → Hierarchical mindmap with main topics, subtopics, and key connections,
#   ideal for sharing or importing into mind-mapping tools (XMind, etc.)

First-Success Path

text

Step 1: Install → clawhub install bilibili-digest
Step 2: Run → clawhub run bilibili-digest --url "https://www.bilibili.com/video/BV1xx411c7mD"
Step 3: Receive → Structured Markdown note (<30 seconds)
Step 4: Copy to note-taking app → Value achieved

Core Scripts

The scripts/ directory contains Python modules:

File	Purpose
parser.py	Extract BV/CV IDs and expand b23.tv short links
api.py	Bilibili API client with rate limiting and retry
subtitle.py	CC subtitle extraction and cleanup
danmaku.py	Danmaku density detection and sentiment grouping
segmenter.py	Chapter detection from subtitle gaps and transition words
summarizer.py	LLM-based structured summary generation
exporter.py	Markdown / Obsidian / JSON / Notion / Feishu export
cross_merge.py	Cross-video knowledge merging
__init__.py	Package init