NotebookLM Research Assistant Skill

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

Source: https://github.com/PleasePrompto/notebooklm-skill

Install

git clone https://github.com/PleasePrompto/notebooklm-skill ~/.claude/skills/notebooklm

The run.py wrapper auto-creates .venv, installs dependencies, and sets up Chromium on first run.

When to Use This Skill

Trigger when user:

• Mentions NotebookLM explicitly
• Shares NotebookLM URL (https://notebooklm.google.com/notebook/...)
• Asks to query their notebooks/documentation
• Wants to add documentation to NotebookLM library
• Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"

CRITICAL: Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS use python scripts/run.py [script]:

# CORRECT
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list
python scripts/run.py ask_question.py --question "..."

# WRONG — fails without venv
python scripts/auth_manager.py status

Core Workflow

Step 1: Check Authentication

python scripts/run.py auth_manager.py status

Step 2: Authenticate (One-Time Setup)

# Browser MUST be visible for manual Google login
python scripts/run.py auth_manager.py setup

Tell user: "A browser window will open for Google login."

Step 3: Manage Notebook Library

# List all notebooks
python scripts/run.py notebook_manager.py list

# Add notebook — ALL parameters required
python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Descriptive Name" \
  --description "What this notebook contains" \
  --topics "topic1,topic2,topic3"

# Smart Add (discover content first, then add)
python scripts/run.py ask_question.py --question "What topics are covered in this notebook?" --notebook-url "[URL]"

# Search, activate, remove
python scripts/run.py notebook_manager.py search --query "keyword"
python scripts/run.py notebook_manager.py activate --id notebook-id
python scripts/run.py notebook_manager.py remove --id notebook-id

Step 4: Ask Questions

# Basic query (uses active notebook)
python scripts/run.py ask_question.py --question "Your question here"

# Query specific notebook by ID
python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# Query by URL directly
python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

# Debug mode
python scripts/run.py ask_question.py --question "..." --show-browser

Follow-Up Mechanism (CRITICAL)

Every NotebookLM answer ends with: "Is that ALL you need to know?"

Required behavior:

1. STOP — do not immediately respond to user
2. ANALYZE — compare answer to original request
3. IDENTIFY GAPS — determine if more info needed
4. ASK FOLLOW-UP — if gaps exist, query again with context
5. SYNTHESIZE — combine all answers before responding

Script Reference

# Auth
python scripts/run.py auth_manager.py setup|status|reauth|clear

# Notebooks
python scripts/run.py notebook_manager.py add|list|search|activate|remove|stats

# Questions
python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL]

# Cleanup
python scripts/run.py cleanup_manager.py [--confirm] [--preserve-library]

Data Storage

All data in ~/.claude/skills/notebooklm/data/:

• library.json — notebook metadata
• auth_info.json — auth status
• browser_state/ — browser cookies/session

Protected by .gitignore. Never commit.

Limitations

• No session persistence (each question = new browser session)
• Rate limit: ~50 queries/day on free Google accounts
• Manual upload required (user adds docs to NotebookLM)
• Requires Python 3.8+

Troubleshooting