Notebooklm

NotebookLM Research Assistant Skill

Full NotebookLM automation: Query notebooks AND upload local files/notes to NotebookLM.

🎯 Core Capabilities

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"
• Wants to upload local files/notes to NotebookLM
• Asks to create a new NotebookLM notebook
• Uses phrases like "upload to NotebookLM", "add my files to notebook", "sync my notes"

⚡ Quick Reference: Upload Commands

# Create new notebook
python scripts/run.py upload_sources.py create --name "My Project Docs"

# Upload local files to notebook
python scripts/run.py upload_sources.py upload --files "/path/to/doc.pdf" --create-notebook "New Notebook"
python scripts/run.py upload_sources.py upload --files "/path/to/doc.pdf" --notebook-url "https://notebooklm.google.com/notebook/..."

# Batch upload from directory
python scripts/run.py upload_sources.py upload-dir --directory "/path/to/docs" --extensions "pdf,md,txt" --create-notebook "Documentation"

# Add URLs
python scripts/run.py upload_sources.py add-urls --urls "https://example.com" --notebook-url "..."

# Add text content
python scripts/run.py upload_sources.py add-text --text "Your content..." --notebook-url "..."
python scripts/run.py upload_sources.py add-text --file "/path/to/content.txt" --notebook-url "..."

⚠️ 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

Source Upload (upload_sources.py)

NEW! Upload local notes, files, URLs, or text content to NotebookLM notebooks.

Create New Notebook

python scripts/run.py upload_sources.py create --name "My Project Docs"

Upload Local Files

# Upload to existing notebook
python scripts/run.py upload_sources.py upload --files "/path/to/doc.pdf,/path/to/notes.md" --notebook-url "https://notebooklm.google.com/notebook/..."

# Upload to notebook from library
python scripts/run.py upload_sources.py upload --files "/path/to/doc.pdf" --notebook-id my-notebook-id

# Create new notebook and upload
python scripts/run.py upload_sources.py upload --files "/path/to/doc.pdf" --create-notebook "New Project"

Upload Directory (Batch)

# Upload all supported files from directory
python scripts/run.py upload_sources.py upload-dir --directory "/path/to/docs" --notebook-id ID

# Filter by extension
python scripts/run.py upload_sources.py upload-dir --directory "/path/to/docs" --extensions "pdf,md,txt" --create-notebook "Documentation"

Add URLs (Websites/YouTube)

python scripts/run.py upload_sources.py add-urls --urls "https://example.com,https://youtube.com/watch?v=..." --notebook-id ID

Add Text Content

# From command line
python scripts/run.py upload_sources.py add-text --text "Your long text content here..." --notebook-id ID

# From file
python scripts/run.py upload_sources.py add-text --file "/path/to/content.txt" --notebook-id ID

Supported file formats: PDF, TXT, MD, DOCX, DOC

Limits:

• Max 50 sources per notebook
• Max 500,000 words per source
• Use --show-browser for debugging

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
    ↓
┌─────────────────────────────────────────────────────────────┐
│                    WHAT DOES USER WANT?                      │
├─────────────────────────────────────────────────────────────┤
│                           │                                   │
│    📤 UPLOAD FILES        │    ❓ ASK QUESTIONS               │
│                           │                                   │
│    ↓                      │    ↓                              │
│    Create/Select notebook │    Check/Add notebook             │
│    ↓                      │    ↓                              │
│    upload_sources.py      │    ask_question.py                │
│    upload/upload-dir/     │    --question "..."               │
│    add-urls/add-text      │    ↓                              │
│                           │    Follow-ups until complete      │
│                           │    ↓                              │
│                           │    Synthesize and respond         │
└─────────────────────────────────────────────────────────────┘

Upload Flow (New!)

User wants to upload local files
    ↓
Check auth → python scripts/run.py auth_manager.py status
    ↓
Create new notebook (optional) → python scripts/run.py upload_sources.py create --name "..."
    ↓
Upload files → python scripts/run.py upload_sources.py upload --files "..." --notebook-url/--notebook-id
    ↓
Confirm upload success
    ↓
(Optional) Add notebook to library → python scripts/run.py notebook_manager.py add --url URL

Troubleshooting

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)
• Max 50 sources per notebook, 500,000 words per source
• Browser overhead (few seconds per operation)

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