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notebooklm

Query your Google NotebookLM notebooks directly from Claude for source-grounded, citation-backed answers. Automates browser-based interaction with NotebookLM to retrieve answers exclusively from your uploaded documents, eliminating hallucinations through document-only responses Manages notebook library with metadata tagging, search, and activation; supports smart discovery of notebook content when adding new sources Handles persistent authentication via one-time Google login setup with automatic environment and dependency management through a run.py wrapper Implements follow-up mechanism to ensure complete information retrieval by prompting for additional questions until user needs are fully satisfied Subject to free-tier rate limits (50 queries/day) and requires manual document upload to NotebookLM beforehand

INSTALLATION
npx skills add https://github.com/pleaseprompto/notebooklm-skill --skill notebooklm
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

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
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