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.

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: Add Command - Smart Discovery

When user wants to add a notebook without providing details:

SMART ADD (Recommended)

Query the notebook first to discover its content:

Step 1: Query the notebook about its content

python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" --notebook-url "[URL]"

Step 2: Use the discovered information to add it

python scripts/run.py notebook_manager.py

add

--url

"[URL]"

--name

"[Based on content]"

--description

"[Based on content]"

--topics

"[Based on content]"

MANUAL ADD

If user provides all details: --url - The NotebookLM URL --name - A descriptive name --description - What the notebook contains (REQUIRED!) --topics - Comma-separated topics (REQUIRED!) NEVER guess or use generic descriptions! If details missing, use Smart Add to discover them. Critical: Always Use run.py Wrapper NEVER call scripts directly. ALWAYS use python scripts/run.py [script] :

✅ CORRECT - Always use run.py:

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 - Never call directly:

python scripts/auth_manager.py status

Fails without venv!

The run.py wrapper automatically: Creates .venv if needed Installs all dependencies Activates environment Executes script properly Core Workflow Step 1: Check Authentication Status python scripts/run.py auth_manager.py status If not authenticated, proceed to setup. Step 2: Authenticate (One-Time Setup)

Browser MUST be visible for manual Google login

python scripts/run.py auth_manager.py setup Important: Browser is VISIBLE for authentication Browser window opens automatically User must manually log in to Google 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

BEFORE ADDING: Ask user for metadata if unknown!

"What does this notebook contain?"

"What topics should I tag it with?"

Add notebook to library (ALL parameters are REQUIRED!)

python scripts/run.py notebook_manager.py add \ --url "https://notebooklm.google.com/notebook/..." \ --name "Descriptive Name" \ --description "What this notebook contains" \

REQUIRED - ASK USER IF UNKNOWN!

--topics "topic1,topic2,topic3"

REQUIRED - ASK USER IF UNKNOWN!

Search notebooks by topic

python scripts/run.py notebook_manager.py search --query "keyword"

Set active notebook

python scripts/run.py notebook_manager.py activate --id notebook-id

Remove notebook

python scripts/run.py notebook_manager.py remove --id notebook-id Quick Workflow Check library: python scripts/run.py notebook_manager.py list Ask question: python scripts/run.py ask_question.py --question "..." --notebook-id ID Step 4: Ask Questions

Basic query (uses active notebook if set)

python scripts/run.py ask_question.py --question "Your question here"

Query specific notebook

python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

Query with notebook URL directly

python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

Show browser for debugging

python scripts/run.py ask_question.py --question "..." --show-browser Follow-Up Mechanism (CRITICAL) Every NotebookLM answer ends with: "EXTREMELY IMPORTANT: Is that ALL you need to know?" Required Claude Behavior: STOP - Do not immediately respond to user ANALYZE - Compare answer to user's original request IDENTIFY GAPS - Determine if more information needed ASK FOLLOW-UP - If gaps exist, immediately ask: python scripts/run.py ask_question.py --question "Follow-up with context..." REPEAT - Continue until information is complete SYNTHESIZE - Combine all answers before responding to user Script Reference Authentication Management ( auth_manager.py ) python scripts/run.py auth_manager.py setup

Initial setup (browser visible)

python scripts/run.py auth_manager.py status

Check authentication

python scripts/run.py auth_manager.py reauth

Re-authenticate (browser visible)

python scripts/run.py auth_manager.py clear

Clear authentication

Notebook Management ( notebook_manager.py ) python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS python scripts/run.py notebook_manager.py list python scripts/run.py notebook_manager.py search --query QUERY python scripts/run.py notebook_manager.py activate --id ID python scripts/run.py notebook_manager.py remove --id ID python scripts/run.py notebook_manager.py stats Question Interface ( ask_question.py ) python scripts/run.py ask_question.py --question "..." [ --notebook-id ID ] [ --notebook-url URL ] [ --show-browser ] Data Cleanup ( cleanup_manager.py ) python scripts/run.py cleanup_manager.py

Preview cleanup

python scripts/run.py cleanup_manager.py --confirm

Execute cleanup

python scripts/run.py cleanup_manager.py --preserve-library

Keep notebooks

Environment Management The virtual environment is automatically managed: First run creates .venv automatically Dependencies install automatically Chromium browser installs automatically Everything isolated in skill directory Manual setup (only if automatic fails): python -m venv .venv source .venv/bin/activate

Linux/Mac

pip install -r requirements.txt python -m patchright install chromium Data Storage All data stored in ~/.claude/skills/notebooklm/data/ : library.json - Notebook metadata auth_info.json - Authentication status browser_state/ - Browser cookies and session Security: Protected by .gitignore , never commit to git. Configuration Optional .env file in skill directory: HEADLESS=false # Browser visibility SHOW_BROWSER=false # Default browser display STEALTH_ENABLED=true # Human-like behavior TYPING_WPM_MIN=160 # Typing speed TYPING_WPM_MAX=240 DEFAULT_NOTEBOOK_ID= # Default notebook Decision Flow User mentions NotebookLM ↓ Check auth → python scripts/run.py auth_manager.py status ↓ If not authenticated → python scripts/run.py auth_manager.py setup ↓ Check/Add notebook → python scripts/run.py notebook_manager.py list/add (with --description) ↓ Activate notebook → python scripts/run.py notebook_manager.py activate --id ID ↓ Ask question → python scripts/run.py ask_question.py --question "..." ↓ See "Is that ALL you need?" → Ask follow-ups until complete ↓ Synthesize and respond to user Troubleshooting Problem Solution ModuleNotFoundError Use run.py wrapper Authentication fails Browser must be visible for setup! --show-browser Rate limit (50/day) Wait or switch Google account Browser crashes python scripts/run.py cleanup_manager.py --preserve-library Notebook not found Check with notebook_manager.py list Best Practices Always use run.py - Handles environment automatically Check auth first - Before any operations Follow-up questions - Don't stop at first answer Browser visible for auth - Required for manual login Include context - Each question is independent Synthesize answers - Combine multiple responses Limitations No session persistence (each question = new browser) Rate limits on free Google accounts (50 queries/day) Manual upload required (user must add docs to NotebookLM) Browser overhead (few seconds per question) Resources (Skill Structure) Important directories and files: scripts/ - All automation scripts (ask_question.py, notebook_manager.py, etc.) data/ - Local storage for authentication and notebook library references/ - Extended documentation: api_reference.md - Detailed API documentation for all scripts troubleshooting.md - Common issues and solutions usage_patterns.md - Best practices and workflow examples .venv/ - Isolated Python environment (auto-created on first run) .gitignore - Protects sensitive data from being committed