Notebooklm

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
python3 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
python3 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 python3 scripts/run.py [script]:

# ✅ CORRECT - Always use run.py:
python3 scripts/run.py auth_manager.py status
python3 scripts/run.py notebook_manager.py list
python3 scripts/run.py ask_question.py --question "..."

# ❌ WRONG - Never call directly:
python3 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

python3 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
python3 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
python3 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!)
python3 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
python3 scripts/run.py notebook_manager.py search --query "keyword"

# Set active notebook
python3 scripts/run.py notebook_manager.py activate --id notebook-id

# Remove notebook
python3 scripts/run.py notebook_manager.py remove --id notebook-id

Quick Workflow

1. Check library: python3 scripts/run.py notebook_manager.py list
2. Ask question: python3 scripts/run.py ask_question.py --question "..." --notebook-id ID

Step 4: Ask Questions

# Basic query (uses active notebook if set)
python3 scripts/run.py ask_question.py --question "Your question here"

# Query specific notebook
python3 scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# Query with notebook URL directly
python3 scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

# Show browser for debugging
python3 scripts/run.py ask_question.py --question "..." --show-browser

Step 5: Cross-Notebook Analysis (Multiple Notebooks)

When user wants to query multiple notebooks with the same question (e.g., for project hierarchies with sub-projects + overall project):

# Query ALL notebooks in library with the same question
python3 scripts/run.py ask_multiple.py --question "What are the main risks?" --all-notebooks

# Query SPECIFIC notebooks by ID
python3 scripts/run.py ask_multiple.py --question "GPU status?" --notebook-ids "id1,id2,id3"

# Show browser for debugging
python3 scripts/run.py ask_multiple.py --question "..." --all-notebooks --show-browser

When to use Cross-Notebook Analysis:

• User has project hierarchy (sub-projects + overall project notebooks)
• User wants comparative analysis across different documentation sets
• User asks to "check all notebooks", "ask all my docs", etc.
• User wants comprehensive view across entire knowledge base

Output format:

• Displays answers from each notebook separately
• Includes success/failure status for each
• Provides unified view for synthesis
• Same follow-up mechanism applies

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:

   python3 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)

python3 scripts/run.py auth_manager.py setup    # Initial setup (browser visible)
python3 scripts/run.py auth_manager.py status   # Check authentication
python3 scripts/run.py auth_manager.py reauth   # Re-authenticate (browser visible)
python3 scripts/run.py auth_manager.py clear    # Clear authentication

Notebook Management (notebook_manager.py)

python3 scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS
python3 scripts/run.py notebook_manager.py list
python3 scripts/run.py notebook_manager.py search --query QUERY
python3 scripts/run.py notebook_manager.py activate --id ID
python3 scripts/run.py notebook_manager.py remove --id ID
python3 scripts/run.py notebook_manager.py stats

Question Interface (ask_question.py)

python3 scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]

Cross-Notebook Analysis (ask_multiple.py)

# Query all notebooks
python3 scripts/run.py ask_multiple.py --question "..." --all-notebooks

# Query specific notebooks by ID (comma-separated)
python3 scripts/run.py ask_multiple.py --question "..." --notebook-ids "id1,id2,id3"

# Show browser
python3 scripts/run.py ask_multiple.py --question "..." --all-notebooks --show-browser

Data Cleanup (cleanup_manager.py)

python3 scripts/run.py cleanup_manager.py                    # Preview cleanup
python3 scripts/run.py cleanup_manager.py --confirm          # Execute cleanup
python3 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 .agent/skills/business/notebooklm/data/ (relative to project root):

• 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 → python3 scripts/run.py auth_manager.py status
    ↓
If not authenticated → python3 scripts/run.py auth_manager.py setup
    ↓
Check/Add notebook → python3 scripts/run.py notebook_manager.py list/add (with --description)
    ↓
Activate notebook → python3 scripts/run.py notebook_manager.py activate --id ID
    ↓
Ask question → python3 scripts/run.py ask_question.py --question "..."
    ↓
See "Is that ALL you need?" → Ask follow-ups until complete
    ↓
Synthesize and respond to user

Troubleshooting

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
7. Cross-notebook for hierarchies - Use ask_multiple.py for project structures with sub-projects
8. Specific vs. all notebooks - Use --notebook-ids for targeted analysis, --all-notebooks for comprehensive view

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 - Query single notebook
  • ask_multiple.py - Cross-notebook analysis
  • notebook_manager.py - Library management
  • auth_manager.py - Authentication
  • cleanup_manager.py - Data cleanup
• 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