notebooklm

To get citation-backed answers from Google NotebookLM using Claude Code, you authenticate via Patchright browser automation, add a notebook URL to your library, and query it to receive source-grounded responses from Gemini based on your uploaded documents.

Yes, you can query multiple NotebookLM notebooks to synthesize project-wide insights. The Skill supports cross-notebook analysis workflows, allowing you to organize and switch between notebooks with metadata to aggregate grounded answers from Gemini across various uploaded documents.

Yes, you need Patchright to automate NotebookLM browser authentication. It handles the one-time Google login process and maintains persistent browser state, enabling seamless future sessions without requiring repeated manual logins within your Python 3.8+ environment.

Cross-notebook analysis works by querying multiple NotebookLM notebooks to synthesize project-wide insights. Source-grounded Gemini responses are derived exclusively from your uploaded documents across the queried notebooks, ensuring citation-backed answers without external data contamination.

A Python 3.8 or higher environment with a local virtual environment is required to run NotebookLM browser automation scripts. You also need NotebookLM access and the python-dotenv dependency to manage environment variables for the Patchright automation workflow.

You should use NotebookLM integration instead of directly querying Gemini when you need answers grounded exclusively in your specific uploaded documents. NotebookLM provides source citations and manages distinct notebook libraries, ensuring responses are derived only from your curated data.