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BytesAgain β€Ί Skills β€Ί Notebooklm
πŸ¦€ ClawHub

Notebooklm

by @guccidgi

Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.

Versionv0.1.0
Downloads8,850
Stars⭐ 16
#clawhub
TERMINAL
clawhub install notebooklm-skill

πŸ“– About This Skill

name: notebooklm description: Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.

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]:

    # βœ… 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"
    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

    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 β†’ python scripts/run.py auth_manager.py status
    ↓
If not authenticated β†’ python scripts/run.py auth_manager.py setup
    ↓
Check/Add notebook β†’ python scripts/run.py notebook_manager.py list/add (with --description)
    ↓
Activate notebook β†’ python scripts/run.py notebook_manager.py activate --id ID
    ↓
Ask question β†’ python scripts/run.py ask_question.py --question "..."
    ↓
See "Is that ALL you need?" β†’ 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

    • βš™οΈ 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

    πŸ“‹ Tips & 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