notebooklm-distiller

What This Skill Does

NotebookLM Distiller is a powerful automation bridge between Google NotebookLM and Obsidian. It acts as a specialized data-processing pipeline that extracts complex knowledge from your research notebooks and transforms them into structured, linked markdown files within your local Obsidian vault. By leveraging deep analysis capabilities, it handles tasks ranging from generating 15-20 deep, conceptual questions for study, creating summaries and glossaries, to initiating web research sessions directly within the NotebookLM ecosystem. It is designed for researchers, students, and knowledge workers who rely on NotebookLM to process web content but need that knowledge stored in a more permanent, local-first format.

Installation

To integrate NotebookLM Distiller into your workflow, ensure you have Python 3.10+ installed. First, install the core dependency via pip install notebooklm-py. Authenticate your session using the notebooklm login command, which generates the required session file. Finally, install the skill using the ClawHub command: clawhub install openclaw/skills/skills/anchor-jevons/notebooklm-distiller. Ensure your target Obsidian vault directory is accessible on your local filesystem for the persist subcommand to function.

Use Cases

• Automated Summarization: Convert large sets of notebook research into clean, structured Obsidian markdown pages.
• Interactive Learning: Generate and evaluate study quizzes directly in your Discord chat, sourcing questions directly from your documented research.
• Web Research Automation: Initiate research tasks on complex topics without manually finding sources, letting the agent gather and synthesize the data.
• Knowledge Archiving: Seamlessly move raw AI analysis output from the OpenClaw environment directly into your knowledge base (Obsidian).

Example Prompts

1. "distill the notebook about quantum computing into a structured glossary and summary in Obsidian."
2. "quiz me on the topics inside my 'Neural Networks' notebook; let's do 10 questions."
3. "research 'the history of renewable energy' in NotebookLM and save the findings to my vault."

Tips & Limitations

• Source Attribution: Always rely on the notebook source provided by the agent before finalizing any study session to ensure accuracy.
• Ingestion Workflow: If you have raw URLs, use the deepreader skill first to ingest content into NotebookLM before triggering the distill process.
• Data Integrity: The skill handles markdown persistence; ensure you have write permissions enabled for the destination Obsidian directory.