NotebookLM Skill

by pleaseprompto

Let Claude Code chat directly with NotebookLM for source-grounded answers based exclusively on your uploaded documents npx skills add https://github.com/PleasePrompto/notebooklm-skill --skill notebooklm Download ZIPGitHub7.3k

NotebookLM Web Importer

Import web pages and YouTube videos to NotebookLM with one click. Trusted by 200,000+ users. Install Chrome Extension

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.

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

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

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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