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NotebookLM Research Assistant

Free

Query Google NotebookLM for precise, citation-backed answers.

by davila730.2k stars on davila7/claude-code-templates
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Updated Aug 10, 2026
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Free · Opens the source repo

What NotebookLM Research Assistant does

The NotebookLM Research Assistant skill enables users to interact seamlessly with Google NotebookLM, allowing them to query documentation and receive answers grounded in their specific sources. This skill is particularly useful for developers and researchers who rely on extensive documentation stored in NotebookLM, as it ensures that responses are backed by the actual content of their notebooks. By utilizing Gemini's capabilities, users can expect reduced hallucinations and more reliable information retrieval.

Upon activation, the skill opens a new browser session for each query, ensuring that answers are drawn exclusively from the user's uploaded documents. This approach not only enhances the accuracy of the responses but also maintains the integrity of the information being accessed. Users can manage their notebook library effectively, adding new notebooks, listing existing ones, and searching for specific topics, all through a straightforward command-line interface.

The skill also includes a follow-up mechanism that prompts users to clarify their queries if the initial answer does not fully address their needs. This iterative approach helps ensure that users receive comprehensive and relevant information. With automated environment management, users can focus on their queries without worrying about setting up dependencies or managing virtual environments manually.

This skill is designed for anyone who regularly uses Google NotebookLM for documentation purposes, including developers, researchers, and technical writers. It streamlines the process of accessing and utilizing documentation, making it easier to find precise answers quickly and efficiently.

When to use it

Use this skill when you need to query your Google NotebookLM notebooks for specific information or when you want to manage your documentation library directly from Claude Code.

When not to use it

This skill may not be suitable if you do not use Google NotebookLM or if you require information from external sources not stored in your notebooks.

What you can build with it

Querying Documentation

Use the skill to ask specific questions about your documentation stored in Google NotebookLM.

Managing Notebook Library

Add, list, and search your notebooks to keep your documentation organized and easily accessible.

Follow-Up Questions

Utilize the follow-up mechanism to ensure you receive comprehensive answers to your queries.

How to install NotebookLM Research Assistant

View source

1. Install with the skills CLI

npx skills add davila7/claude-code-templates/notebooklm --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by davila7

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:

  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 → 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

ProblemSolution
ModuleNotFoundErrorUse run.py wrapper
Authentication failsBrowser must be visible for setup! --show-browser
Rate limit (50/day)Wait or switch Google account
Browser crashespython scripts/run.py cleanup_manager.py --preserve-library
Notebook not foundCheck 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

Frequently asked questions about NotebookLM Research Assistant

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