blog-notebooklm

Query Google NotebookLM notebooks for citation-backed answers from uploaded documents.

Updated Jun 5, 2026

Search Tags:#automation #research #grounding #document analysis #citation #notebooklm

Ready to run

npx skills add https://github.com/smarmen86/claude-code-kit --skill blog-notebooklm-smarmen86

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: blog-notebooklm
Source: https://github.com/smarmen86/claude-code-kit/tree/main/skills/blog-notebooklm
Command: npx skills add https://github.com/smarmen86/claude-code-kit --skill blog-notebooklm-smarmen86

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires patchright, python-dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill eliminates the risk of AI hallucination by grounding research answers exclusively in your own uploaded documents, ensuring high-confidence, citation-backed information.

Core Features & Use Cases

Source-Grounded Querying: Retrieve answers directly from your NotebookLM notebooks with inline citations.
Library Management: Organize and search across multiple research notebooks.
Use Case: Use this skill to query a collection of technical whitepapers or project documentation to generate verified, evidence-based content for blog posts or reports.

Quick Start

Ask the blog notebooklm skill to research the key findings from your active notebook regarding the latest project updates.

Frequently Asked Questions about blog-notebooklm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I get citation-backed answers from my research documents to prevent AI hallucination?▼

To get citation-backed answers and prevent AI hallucination, you can query Google NotebookLM notebooks to retrieve source-grounded information exclusively from your uploaded documents. This approach ensures high-confidence research by automating browser-based retrieval of primary source data.

Can I manage multiple research notebooks and search across them for document analysis?▼

Yes, you can manage multiple research notebooks and search across them for document analysis. The skill facilitates research workflows by providing library management features to organize and retrieve source-grounded data across your entire collection of uploaded documents.

What do I need to automate browser-based retrieval from Google NotebookLM?▼

To automate browser-based retrieval from Google NotebookLM, you need Python 3.11 or higher, Google Chrome, and authenticated access to the NotebookLM platform. The automation also requires the patchright and python-dotenv dependencies to manage the environment and browser interactions.

How does source-grounded querying work for generating evidence-based blog content?▼

Source-grounded querying works by automating browser-based retrieval to extract answers directly from your uploaded technical whitepapers or project documentation. It provides inline citations, allowing you to generate verified, evidence-based content for blog posts or reports without relying on external data.

Why does my NotebookLM automation require Python 3.11 and Google Chrome?▼

Your NotebookLM automation requires Python 3.11 and Google Chrome because the underlying scripts depend on the patchright library for browser control and python-dotenv for environment management. This specific setup ensures stable authenticated access and accurate retrieval of primary source data from the platform.