joeseesun / qiaomu-anything-to-notebooklm

2026-01-25 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

The "Anything → NotebookLM" project is a Claude Code Skill designed to process diverse content sources and transform them into various formats like podcasts, PPTs, mind maps, or quizzes. It supports over 15 content types, including social media (WeChat, X/Twitter, YouTube, podcasts), web pages (with a 6-level paywall bypass for 300+ sites like NYT, WSJ, FT), and documents (PDF, EPUB, Markdown, Office files, images, audio, ZIP). The core functionality involves acquiring content, bypassing paywalls if necessary, uploading it to Google NotebookLM, and then using AI to generate the desired output format. It features intelligent source recognition, automated processing, and multi-source integration, enabling complex tasks like deep analysis of an EPUB book or converting a paid article into a podcast.

Key takeaway

For AI Engineers or content strategists seeking to automate content repurposing, this tool offers a powerful solution to transform diverse sources into multiple formats. You can streamline workflows by converting articles into podcasts, videos into PPTs, or entire books into structured analyses, even bypassing paywalls. Consider integrating this skill to enhance content accessibility and accelerate knowledge extraction from various digital mediums.

Key insights

This tool converts diverse content into various formats using AI, featuring robust paywall circumvention and multi-source integration.

Principles

• Automate content acquisition and transformation.
• Employ cascaded strategies for paywall bypass.
• Utilize AI for format generation and deep analysis.

Method

The system identifies content type, applies a 6-level paywall bypass if needed, uploads content to Google NotebookLM, and then uses its AI to generate target formats like podcasts or PPTs, often with recursive questioning for deep analysis.

In practice

• Convert paid articles to audio for commuting.
• Generate PPTs from podcasts for team shares.
• Create mind maps from Twitter threads.

Topics

• Google NotebookLM Integration
• Paywall Bypass Technology
• Multi-Source Content Acquisition
• AI Content Transformation
• Claude Code Skill

Code references

• joeseesun/qiaomu-anything-to-notebooklm
• microsoft/markitdown
• Bwkyd/wexin-read-mcp
• teng-lin/notebooklm-py

Best for: Machine Learning Engineer, NLP Engineer, AI Engineer, Software Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.