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mohamednegm0
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blog-notebooklm

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

Updated Apr 6, 2026
Search Tags:#research#source-grounded#citation#notebooklm#browser-automation#google-notebooklm#notebook-library
One-click install
npx skills add https://github.com/mohamednegm0/Musahm-Vault-GTM --skill blog-notebooklm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: blog-notebooklm
Source: https://github.com/mohamednegm0/Musahm-Vault-GTM/tree/main/.claude/skills/claude-blog/skills/blog-notebooklm
Command: npx skills add https://github.com/mohamednegm0/Musahm-Vault-GTM --skill blog-notebooklm

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Enables reliable, source-backed research by querying a user's Google NotebookLM notebooks so answers are grounded in the user's uploaded documents and accompanied by citations, eliminating reliance on web search or hallucinated responses.

Core Features & Use Cases

  • Source-grounded Q&A: Ask natural-language questions and receive citation-backed answers drawn exclusively from your NotebookLM content.
  • Authentication & State Management: One-time interactive Google login with persistent browser state and cookie injection to maintain session fidelity.
  • Notebook Library & Discovery: Smart-discover notebooks, add/list/search notebooks, set an active notebook, and return JSON results for programmatic use.
  • Graceful Fallback & Integration: Returns silently when unauthenticated for internal callers, supports JSON output for task subagents, and includes cleanup and validation utilities.
  • Use Case: A content researcher queries a marketing research notebook to extract Tier 1 evidence and citations for a blog post without risking hallucinations.

Quick Start

Ask the agent: /blog notebooklm ask What are the key takeaways and sources in the marketing-research notebook?

Frequently Asked Questions about blog-notebooklm

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

FAQPage Schema
How do I get source-grounded answers from Google NotebookLM documents for research?▼

Use browser automation to query your Google NotebookLM notebooks with natural-language questions and receive citation-backed answers. It returns JSON output containing responses grounded exclusively in your uploaded documents, preventing hallucinations during research workflows.

Can I automate NotebookLM queries with persistent browser state for headless authentication?▼

Yes, this Skill executes authenticated headless browser queries using Patchright and persistent browser state. It performs a one-time interactive Google login with cookie injection, maintaining session fidelity for automated programmatic access.

Does this approach support discovering and managing a library of multiple NotebookLM notebooks?▼

Yes, you can smart-discover, add, list, and search notebooks within your NotebookLM library. The Skill lets you set an active notebook and returns JSON results, enabling programmatic notebook management and content discovery.

What happens if my automated NotebookLM query runs without an active Google authentication session?▼

When unauthenticated, the Skill provides a graceful fallback by returning silently. This design ensures internal callers and task subagents do not crash, allowing the parent workflow to handle missing authentication state without interruption.

Why use source-grounded document queries instead of web search for blog writing?▼

Source-grounded queries eliminate reliance on web search and hallucinated responses by restricting answers to your uploaded NotebookLM content. This provides verifiable citations and Tier 1 evidence, ensuring higher accuracy for blog writing and internal researcher agents.

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