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"

Background Operation (Efficiency First)

By default, all scripts run in headless mode (in the background). You will not see a browser window unless you explicitly add the --show-browser flag. This ensures maximum efficiency and zero interruption to your work.

⚠️ 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:

1. Creates .venv if needed
2. Installs all dependencies
3. Activates environment
4. 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"

# SYNC LIBRARY (Highly Recommended if library looks incomplete)
# Automatically discovers all notebooks from your Google account
python scripts/run.py sync_library.py

# 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

1. Check library: python scripts/run.py notebook_manager.py list
2. 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

Step 5: Upload Sources to a Notebook

Upload files (PDF, images, docs, audio) or add YouTube links directly to a notebook's sources via browser automation.

# Upload a YouTube link to the active notebook
python scripts/run.py upload_source.py --youtube "https://www.youtube.com/watch?v=VIDEO_ID"

# Upload a YouTube link to a specific notebook
python scripts/run.py upload_source.py --youtube "https://www.youtube.com/watch?v=VIDEO_ID" --notebook-id ID

# Upload local file(s) - BATCH SUPPORTED (V1.5.2)
python scripts/run.py upload_source.py --file "C:\path\to\doc1.pdf" --file "C:\path\to\doc2.pptx"

# Upload with visible browser for debugging
python scripts/run.py upload_source.py --file "..." --show-browser

How it works:

1. Opens the notebook in an authenticated browser
2. Clicks the "Sources" tab, then "+ Add sources"
3. For URLs: pastes into the search bar and submits
4. For files: uses the file drop zone / file input
5. Waits for processing to complete

Limitations & Performance:

• Reliable Verification: Rigorously waits until server-side indexing (Syncing) is complete for EVERY file.
• Batch Processing: Supports multiple files in a single command; each is verified before starting the next.
• Event-Driven: Uses Playwright triggers rather than static waits, increasing speed by ~60%.
• Chinese Support: Fully compatible with Chinese Windows environments and filenames.

Follow-Up Mechanism (CRITICAL)

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

Required Claude Behavior:

1. STOP - Do not immediately respond to user
2. ANALYZE - Compare answer to user's original request
3. IDENTIFY GAPS - Determine if more information needed
4. ASK FOLLOW-UP - If gaps exist, immediately ask:

   python scripts/run.py ask_question.py --question "Follow-up with context..."
   

5. REPEAT - Continue until information is complete
6. 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

1. Always use run.py - Handles environment automatically
2. Check auth first - Before any operations
3. Follow-up questions - Don't stop at first answer
4. Browser visible for auth - Required for manual login
5. Include context - Each question is independent
6. 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