One Open Source Project a Day (Part 49): Anything to NotebookLM - Multi-Source Content Processor, One Sentence Turns into Podcast, PPT, MindMap, Quiz

Deep dive into Anything to NotebookLM, a Claude Code Skill supporting WeChat articles, web pages, YouTube, PDF, Markdown and 15+ formats converting to podcast, PPT, mindmap, quiz and more, based on Google NotebookLM API, fully automated processing

·10 min read·AI Tools
open-sourceAnything to NotebookLMClaude SkillNotebookLMcontent conversionPythonMCP

Introduction

"One sentence turns into podcast, PPT, mindmap, quiz..."

This is Part 49 of the "One Open Source Project a Day" series. Today's project is Anything to NotebookLM (GitHub).

Want to turn a WeChat article into a podcast for your commute? Turn an ebook into a PPT to share with your team? Generate a quiz from a YouTube video to test your learning? Anything to NotebookLM is a Claude Code Skill that lets you turn any content into any format using natural language: supports WeChat articles, web pages, YouTube, PDF, EPUB, Markdown, images, audio and 15+ input formats, automatically converts to podcast, PPT, mindmap, quiz, video, report, infographic, flashcards and more. Based on Google NotebookLM API, fully automated processing, no need to remember commands.

Why it matters:

  • 🎯 Natural Language Interaction: Fully natural language, no need to remember commands
  • 📚 15+ Input Formats: WeChat articles, web pages, YouTube, PDF, EPUB, Markdown, images, audio, and more
  • 🎨 8+ Output Formats: Podcast, PPT, mindmap, quiz, video, report, infographic, flashcards
  • 🚀 Fully Automated Processing: From acquisition to generation, seamless flow
  • 🌐 Multi-Source Integration: Supports mixing multiple content sources to generate comprehensive reports
  • 🔒 Local-First: Sensitive content processed locally, then uploaded to NotebookLM
  • 🧠 Smart Recognition: Automatically identifies input type, no manual specification needed

What You'll Learn

  • Anything to NotebookLM's positioning and "Claude Code Skill" design philosophy
  • Supported input and output formats
  • Technical architecture: Claude Code Skill + MCP + NotebookLM API
  • Use cases: rapid learning, team sharing, self-testing, information integration, document digitization
  • Installation and configuration process
  • Comparison with similar tools (manual conversion, other AI tools)

Prerequisites

  • Basic understanding of Claude Code usage
  • Basic understanding of AI content generation
  • Basic Python and command-line tool usage (optional)

Project Background

Project Overview

Anything to NotebookLM is a Claude Code Skill for multi-source content intelligent processing. It lets you turn any content into any format using natural language: automatically acquires content from multiple sources, uploads to Google NotebookLM, AI generates your desired format.

Core Features:

  • Claude Code Skill: Integrated as a Skill into Claude Code, natural language interaction
  • Multi-Source Support: Supports 15+ input formats (WeChat articles, web pages, YouTube, PDF, EPUB, Markdown, images, audio, etc.)
  • Multi-Format Output: Supports 8+ output formats (podcast, PPT, mindmap, quiz, video, report, infographic, flashcards)
  • Fully Automated Processing: From acquisition to generation, seamless flow
  • Smart Recognition: Automatically identifies input type, no manual specification needed
  • Multi-Source Integration: Supports mixing multiple content sources to generate comprehensive reports

Core Problems Solved:

  • Content format conversion requires manual operations, low efficiency
  • Different content sources need different tools, high learning cost
  • Multi-source content integration requires manual organization, time-consuming
  • Content generation requires professional skills, high threshold

Target Users:

  • Users who need rapid content learning (article → podcast)
  • Users who need team sharing (ebook → PPT)
  • Users who need self-testing (video → quiz)
  • Users who need information integration (multi-source → report)
  • Users who need document digitization (scanned documents → text)

Author/Team

  • Author: joeseesun (GitHub)
  • Background: Claude Code Skill developer
  • Philosophy: Make content conversion simple and natural
  • Contact: GitHub Issues, GitHub Discussions

Project Statistics

  • ⭐ GitHub Stars: ~515
  • 🍴 Forks: ~68
  • 📦 Version: v1.0.1 (actively maintained, 7+ commits)
  • 📄 License: MIT
  • 🌐 Documentation: GitHub README
  • 💬 Community: GitHub Issues

Tech Stack:

  • Language: Python (50.1%), Shell (49.9%)
  • Python Version: 3.9+
  • Core Dependencies: Google NotebookLM API, Microsoft markitdown, wexin-read-mcp, notebooklm-py
  • Technology: MCP (Model Context Protocol), OCR, voice synthesis

Core Features

Core Purpose

Anything to NotebookLM's core purpose is to turn any content into any format using natural language, enabling users to:

  1. Rapid Learning: Article → podcast, listen during commute
  2. Team Sharing: Ebook → PPT, ready for sharing
  3. Self-Testing: Video → quiz, test learning effectiveness
  4. Information Integration: Multi-source → report, comprehensive topic research
  5. Document Digitization: Scanned documents → text, digital archiving

Use Cases

  1. Rapid Learning - Article → Podcast

    • "Turn this article into a podcast https://mp.weixin.qq.com/s/abc123"
    • Automatically fetches WeChat article content, uploads to NotebookLM, generates podcast (2-5 minutes)
    • Result: 8-minute podcast, 12.3 MB, finish reading a deep article during commute

  2. Team Sharing - Ebook → PPT

    • "Turn this book into PPT /Users/joe/Books/sapiens.epub"
    • Automatically extracts ebook content (150K words), AI refines core points, generates professional PPT
    • Result: 25-page PPT, 3.8 MB, ready for book club sharing

  3. Self-Testing - Video → Quiz

    • "Generate quiz from this YouTube video https://youtube.com/watch?v=abc"
    • Automatically extracts video subtitles, AI analyzes key knowledge points, auto-generates questions
    • Result: 15 questions (10 multiple choice + 5 short answer), test learning effectiveness

  4. Information Integration - Multi-Source → Report

    • "Make these contents into a report together: article + video + PDF"
    • Automatically summarizes 3 different sources, AI integrates and analyzes, generates comprehensive report
    • Result: 7 chapters, 15.2 KB, comprehensive topic research report

  5. Document Digitization - Scanned Document → Text

    • "Turn this scanned image into document /Users/joe/scan.jpg"
    • Automatically OCR recognizes text in image, extracts as plain text, generates structured document
    • Result: 95%+ recognition accuracy, scanned document digital archiving

Quick Start

Prerequisites:

  • ✅ Python 3.9+
  • ✅ Git (built-in on macOS/Linux)

Installation (3 steps):

# 1. Clone to Claude skills directory
cd ~/.claude/skills/
git clone https://github.com/joeseesun/anything-to-notebooklm
cd anything-to-notebooklm
 
# 2. One-click install all dependencies
./install.sh
 
# 3. Configure MCP as prompted, then restart Claude Code

First Use:

# NotebookLM authentication (once only)
notebooklm login
notebooklm list  # Verify success
 
# Environment check (optional)
./check_env.py

Usage Example:

You: Turn this article into a podcast https://mp.weixin.qq.com/s/abc123
 
AI automatically executes:
  ✓ Fetches WeChat article content
  ✓ Uploads to NotebookLM
  ✓ Generates podcast (2-5 minutes)
 
✅ Result: /tmp/article_podcast.mp3 (8 minutes, 12.3 MB)

Key Features

  1. Supported Input Formats (15+ types)

    • 📱 Social Media: WeChat articles (bypass anti-scraping), YouTube videos (auto-extract subtitles)
    • 🌐 Web: Any webpage (news, blogs, documents), search keywords (auto-summarize results)
    • 📄 Office Documents: Word (.docx), PowerPoint (.pptx), Excel (.xlsx)
    • 📚 Ebooks & Documents: PDF (supports scanned OCR), EPUB (ebook), Markdown (.md)
    • 🖼️ Images & Audio: Images (JPEG/PNG/GIF, auto OCR), Audio (WAV/MP3, auto transcription)
    • 📊 Structured Data: CSV/JSON/XML, ZIP archives (batch processing)

  2. Supported Output Formats (8+ types)

    • 🎙️ Podcast: Listen during commute (2-5 minutes generation)
    • 📊 PPT: Team sharing (1-3 minutes generation)
    • 🗺️ Mindmap: Clarify structure (1-2 minutes generation)
    • 📝 Quiz: Self-test mastery (1-2 minutes generation)
    • 🎬 Video: Visualization (3-8 minutes generation)
    • 📄 Report: Deep analysis (2-4 minutes generation)
    • 📈 Infographic: Data visualization (2-3 minutes generation)
    • 📋 Flashcards: Memory consolidation (1-2 minutes generation)

  3. Smart Recognition

    • Automatically identifies input type, no manual specification needed
    • https://mp.weixin.qq.com/s/xxx → WeChat article
    • https://youtube.com/watch?v=xxx → YouTube video
    • /path/to/file.epub → EPUB ebook
    • "Search 'AI trends'" → Search query

  4. Fully Automated Processing

    • From acquisition to generation, seamless flow
    • Input → Acquire → Convert → Upload → Generate → Download

  5. Multi-Source Integration

    • Supports mixing multiple content sources
    • Article + Video + PDF + Search Results → Comprehensive Report

  6. Local-First

    • Sensitive content processed locally
    • WeChat article → Local MCP fetch → Local conversion → NotebookLM

Project Advantages

ComparisonAnything to NotebookLMManual ConversionOther AI Tools
Interaction✅ Natural language❌ Need to learn tools⚠️ Need to remember commands
Input Formats✅ 15+ types⚠️ Need to process one by one⚠️ Limited
Output Formats✅ 8+ types⚠️ Need to create manually⚠️ Limited
Automation✅ Fully automated❌ Completely manual⚠️ Partially automated
Multi-Source Integration✅ Supported❌ Need manual organization⚠️ Limited
Learning Cost✅ Zero learning cost⚠️ High learning cost⚠️ Medium learning cost
Processing Speed✅ 2-8 minutes❌ Hours⚠️ 10-30 minutes

Why Choose Anything to NotebookLM?

  • Natural language interaction: Fully natural language, no need to remember commands
  • Multi-source and multi-format: Supports 15+ input formats, 8+ output formats
  • Fully automated processing: From acquisition to generation, seamless flow
  • Multi-source integration: Supports mixing multiple content sources to generate comprehensive reports
  • Zero learning cost: Tell AI what you want, automatically completes

Deep Dive

Architecture Design

Anything to NotebookLM uses Claude Code Skill + MCP + NotebookLM API architecture:

Technical Architecture:

┌─────────────────────────────────────┐
│        User Natural Language Input   │
│  "Turn this article into podcast..." │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│        Claude Code Skill             │
│  • Smart content source recognition  │
│  • Auto-call corresponding tools    │
└──────────────┬──────────────────────┘
               │
      ┌────────┴────────┐
      │                 │
      ▼                 ▼
┌──────────┐     ┌─────────────┐
│ WeChat   │     │ Other Formats│
│ MCP Fetch│     │ markitdown  │
└─────┬────┘     └──────┬──────┘
      │                 │
      └────────┬────────┘
               │
               ▼
┌─────────────────────────────────────┐
│         NotebookLM API               │
│  • Upload content sources            │
│  • AI generate target format         │
└──────────────┬──────────────────────┘
               │
               ▼
┌─────────────────────────────────────┐
│         Generated Files               │
│  .mp3 / .pdf / .json / .md          │
└─────────────────────────────────────┘

Core Components:

  1. Claude Code Skill: Natural language interaction entry point
  2. MCP (Model Context Protocol): WeChat article fetching (bypass anti-scraping)
  3. Microsoft markitdown: File conversion (Word, PPT, Excel, PDF, etc.)
  4. Google NotebookLM API: AI content generation
  5. notebooklm-py: NotebookLM CLI tool

Content Source Processing Flow

WeChat Article Processing:

WeChat Article URL → MCP Server (Browser Simulation) → Fetch Content → Local Conversion → NotebookLM

Other Format Processing:

File/URL → markitdown Conversion → Text Content → NotebookLM

Multi-Source Integration:

Multiple Content Sources → Process Separately → Merge Upload → NotebookLM Integration Analysis → Comprehensive Report

Output Format Generation

Podcast Generation:

  • Google AI voice synthesis
  • English: Two AI hosts conversation
  • Chinese: Single narrator
  • Generation time: 2-5 minutes
  • File format: .mp3

PPT Generation:

  • AI refines core points
  • Auto-generates slide structure
  • Generation time: 1-3 minutes
  • File format: .pdf

Mindmap Generation:

  • AI analyzes content structure
  • Auto-generates node relationships
  • Generation time: 1-2 minutes
  • File format: .json

Quiz Generation:

  • AI analyzes key knowledge points
  • Auto-generates questions (multiple choice + short answer)
  • Generation time: 1-2 minutes
  • File format: .md

Advanced Usage

Specify Existing Notebook:

Add this article to my [AI Research] notebook https://example.com

Batch Processing:

Turn all these articles into podcasts:
1. https://mp.weixin.qq.com/s/abc123
2. https://example.com/article2
3. /Users/joe/notes.md

ZIP Batch Conversion:

Turn all documents in this archive into podcast /path/to/files.zip

Auto-extract, identify, convert, merge.

Troubleshooting

MCP Tool Not Found:

# Test MCP server
python ~/.claude/skills/anything-to-notebooklm/wexin-read-mcp/src/server.py
 
# Reinstall dependencies
cd ~/.claude/skills/anything-to-notebooklm/wexin-read-mcp
pip install -r requirements.txt
playwright install chromium

NotebookLM Authentication Failed:

notebooklm login     # Re-login
notebooklm list      # Verify

Environment Check:

./check_env.py       # 13 comprehensive checks
./install.sh         # Reinstall

Content Length Limits

  • Minimum: ~500 words
  • Maximum: ~500K words
  • Recommended: 1000-10000 words for best results

Language Support

  • NotebookLM supports multiple languages
  • Chinese and English work best

Project Links and Resources

Official Resources

Related Resources

Target Audience

  • Rapid Learners: Need to turn articles into podcasts for commute listening
  • Team Sharers: Need to turn ebooks into PPTs to share with teams
  • Self-Testers: Need to generate quizzes from videos to test learning effectiveness
  • Information Integrators: Need to integrate multi-source content into comprehensive reports
  • Document Digitizers: Need to convert scanned documents to text

Learning Value:

  • ✅ Claude Code Skill development and usage
  • ✅ MCP (Model Context Protocol) applications
  • ✅ NotebookLM API usage
  • ✅ Multi-source content processing and integration
  • ✅ Natural language interaction design
  • ✅ AI content generation applications

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