| name | noteking |
| description | Convert any video or blog into structured learning notes. Supports Bilibili, YouTube, Douyin, Xiaohongshu, TikTok, and 1800+ platforms via yt-dlp. Offers 13 output templates including detailed notes, mind maps, flashcards, quizzes, exam notes, timeline, LaTeX PDF, and more. Use when the user provides a video URL and wants summarized content, study notes, or any form of structured output from video/audio content.
|
NoteKing - Video/Blog to Learning Notes
Use this skill when the user wants to convert a video, podcast, or blog into structured notes.
Supported Platforms
- Bilibili (B站): single videos, multi-part (分P), series, collections, favorites
- YouTube: single videos, playlists, channels (proxy support for China)
- Douyin (抖音), Xiaohongshu (小红书), Kuaishou (快手)
- TikTok, Twitter/X, Instagram, Twitch, Vimeo, Facebook
- 1800+ additional sites via yt-dlp
- Local files: MP4, MP3, WAV, FLAC
Available Templates
| Template | Name | Best For |
|---|
brief | 简要总结 | Quick 3-5 sentence overview |
detailed | 详细学习笔记 | Systematic study with chapters & key points |
mindmap | 思维导图 | Visual knowledge structure (Markmap) |
flashcard | 闪卡 (Anki) | Spaced repetition study |
quiz | 测验题 | Self-testing with MCQ + short answer |
timeline | 时间线笔记 | Timestamped notes with video links |
exam | 考试复习笔记 | Exam preparation with formulas & key concepts |
tutorial | 教程步骤 | Step-by-step how-to extraction |
news | 新闻速览 | News-style quick summary |
podcast | 播客/访谈摘要 | Interview & discussion summary |
xhs_note | 小红书笔记 | Xiaohongshu-style social media note |
latex_pdf | LaTeX PDF 讲义 | Professional academic lecture notes |
custom | 自定义 | User-defined prompt template |
Dependencies
The following tools must be available in the environment:
yt-dlp - Video downloading (install: pip install yt-dlp)ffmpeg - Audio/video processing (install: brew install ffmpeg or apt install ffmpeg)
Python packages (auto-installed):
openai - LLM API clientyoutube-transcript-api - YouTube subtitle extractionhttpx - HTTP clientpysrt - SRT subtitle parsing
Optional:
faster-whisper - Local speech recognition (install: pip install faster-whisper)
Environment Variables
| Variable | Required | Description |
|---|
NOTEKING_LLM_API_KEY | Yes | API key for LLM (OpenAI/DeepSeek/etc.) |
NOTEKING_LLM_BASE_URL | No | Custom API base URL |
NOTEKING_LLM_MODEL | No | Model name (default: gpt-4o-mini) |
NOTEKING_PROXY | No | Proxy URL for YouTube access from China |
BILIBILI_SESSDATA | No | Bilibili login cookie for HD & member content |
Usage
Basic Usage
When the user provides a video URL, run the NoteKing core:
cd /path/to/noteking
python -m cli run "<VIDEO_URL>" -t <TEMPLATE>
Examples
python -m cli run "https://www.bilibili.com/video/BV1xx411c79H" -t detailed
python -m cli run "https://youtu.be/dQw4w9WgXcQ" -t mindmap
python -m cli run "./lecture.mp4" -t flashcard
python -m cli run "https://www.bilibili.com/video/BV1xx" -t exam --api-key sk-xxx --model deepseek-chat --base-url https://api.deepseek.com
Batch Processing
For playlists or video collections (e.g., a 50-lecture math course):
python -m cli run "https://www.bilibili.com/video/BV1xx?p=1" -t detailed
python -m cli run "https://youtube.com/playlist?list=PLxxx" -t exam
Available CLI Commands
python -m cli run <URL> -t <TEMPLATE>
python -m cli transcript <URL>
python -m cli templates
python -m cli setup
python -m cli cache-clear
Workflow
- Parse URL → Detect platform, check if playlist/collection
- Extract subtitles → Three-level fallback: CC subtitles → ASR → Visual mode
- Get metadata → Title, chapters, duration, thumbnail
- Generate notes → Apply selected template via LLM
- Save output → Markdown file + SRT subtitles + transcript
Configuration
Config file location: ~/.noteking/config.json
Run python -m cli setup for interactive configuration, or edit the JSON directly.
Notes for AI Agents
- When the user says "summarize this video" or "help me take notes on this video", use this skill
- Default to
detailed template unless the user specifies otherwise
- For exam preparation, recommend
exam + flashcard templates
- For quick browsing, recommend
brief template
- For courses with multiple videos, use batch processing
- Always check if
yt-dlp and ffmpeg are installed first
