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
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

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