antigravity-skills-reference/skills/notebooklm/references/api_reference.md

NotebookLM Skill API Reference

Complete API documentation for all NotebookLM skill modules.

Important: Always Use run.py Wrapper

All commands must use the run.py wrapper to ensure proper environment:

# ✅ CORRECT:
python scripts/run.py [script_name].py [arguments]

# ❌ WRONG:
python scripts/[script_name].py [arguments]  # Will fail without venv!

Core Scripts

ask_question.py

Query NotebookLM with automated browser interaction.

# Basic usage
python scripts/run.py ask_question.py --question "Your question"

# With specific notebook
python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# With direct URL
python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

# Show browser (debugging)
python scripts/run.py ask_question.py --question "..." --show-browser

Parameters:

• --question (required): Question to ask
• --notebook-id: Use notebook from library
• --notebook-url: Use URL directly
• --show-browser: Make browser visible

Returns: Answer text with follow-up prompt appended

notebook_manager.py

Manage notebook library with CRUD operations.

# Smart Add (discover content first)
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]"
# Then add with discovered info
python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Name" \
  --description "Description" \
  --topics "topic1,topic2"

# Direct add (when you know the content)
python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Name" \
  --description "What it contains" \
  --topics "topic1,topic2"

# List notebooks
python scripts/run.py notebook_manager.py list

# Search notebooks
python scripts/run.py notebook_manager.py search --query "keyword"

# Activate 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

# Show statistics
python scripts/run.py notebook_manager.py stats

Commands:

• add: Add notebook (requires --url, --name, --topics)
• list: Show all notebooks
• search: Find notebooks by keyword
• activate: Set default notebook
• remove: Delete from library
• stats: Display library statistics

auth_manager.py

Handle Google authentication and browser state.

# Setup (browser visible for login)
python scripts/run.py auth_manager.py setup

# Check status
python scripts/run.py auth_manager.py status

# Re-authenticate
python scripts/run.py auth_manager.py reauth

# Clear authentication
python scripts/run.py auth_manager.py clear

Commands:

• setup: Initial authentication (browser MUST be visible)
• status: Check if authenticated
• reauth: Clear and re-setup
• clear: Remove all auth data

cleanup_manager.py

Clean skill data with preservation options.

# Preview cleanup
python scripts/run.py cleanup_manager.py

# Execute cleanup
python scripts/run.py cleanup_manager.py --confirm

# Keep library
python scripts/run.py cleanup_manager.py --confirm --preserve-library

# Force without prompt
python scripts/run.py cleanup_manager.py --confirm --force

Options:

• --confirm: Actually perform cleanup
• --preserve-library: Keep notebook library
• --force: Skip confirmation prompt

run.py

Script wrapper that handles environment setup.

# Usage
python scripts/run.py [script_name].py [arguments]

# Examples
python scripts/run.py auth_manager.py status
python scripts/run.py ask_question.py --question "..."

Automatic actions:

1. Creates .venv if missing
2. Installs dependencies
3. Activates environment
4. Executes target script

Python API Usage

Using subprocess with run.py

import subprocess
import json

# Always use run.py wrapper
result = subprocess.run([
    "python", "scripts/run.py", "ask_question.py",
    "--question", "Your question",
    "--notebook-id", "notebook-id"
], capture_output=True, text=True)

answer = result.stdout

Direct imports (after venv exists)

# Only works if venv is already created and activated
from notebook_manager import NotebookLibrary
from auth_manager import AuthManager

library = NotebookLibrary()
notebooks = library.list_notebooks()

auth = AuthManager()
is_auth = auth.is_authenticated()

Data Storage

Location: ~/.claude/skills/notebooklm/data/

data/
├── library.json       # Notebook metadata
├── auth_info.json     # Auth status
└── browser_state/     # Browser cookies
    └── state.json

Security: Protected by .gitignore, never commit.

Environment Variables

Optional .env file configuration:

HEADLESS=false           # Browser visibility
SHOW_BROWSER=false       # Default display
STEALTH_ENABLED=true     # Human behavior
TYPING_WPM_MIN=160       # Typing speed
TYPING_WPM_MAX=240
DEFAULT_NOTEBOOK_ID=     # Default notebook

Error Handling

Common patterns:

# Using run.py prevents most errors
result = subprocess.run([
    "python", "scripts/run.py", "ask_question.py",
    "--question", "Question"
], capture_output=True, text=True)

if result.returncode != 0:
    error = result.stderr
    if "rate limit" in error.lower():
        # Wait or switch accounts
        pass
    elif "not authenticated" in error.lower():
        # Run auth setup
        subprocess.run(["python", "scripts/run.py", "auth_manager.py", "setup"])

Rate Limits

Free Google accounts: 50 queries/day

Solutions:

1. Wait for reset (midnight PST)
2. Switch accounts with reauth
3. Use multiple Google accounts

Advanced Patterns

Parallel Queries

import concurrent.futures
import subprocess

def query(question, notebook_id):
    result = subprocess.run([
        "python", "scripts/run.py", "ask_question.py",
        "--question", question,
        "--notebook-id", notebook_id
    ], capture_output=True, text=True)
    return result.stdout

# Run multiple queries simultaneously
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    futures = [
        executor.submit(query, q, nb)
        for q, nb in zip(questions, notebooks)
    ]
    results = [f.result() for f in futures]

Batch Processing

def batch_research(questions, notebook_id):
    results = []
    for question in questions:
        result = subprocess.run([
            "python", "scripts/run.py", "ask_question.py",
            "--question", question,
            "--notebook-id", notebook_id
        ], capture_output=True, text=True)
        results.append(result.stdout)
        time.sleep(2)  # Avoid rate limits
    return results

Module Classes

NotebookLibrary

• add_notebook(url, name, topics)
• list_notebooks()
• search_notebooks(query)
• get_notebook(notebook_id)
• activate_notebook(notebook_id)
• remove_notebook(notebook_id)

AuthManager

• is_authenticated()
• setup_auth(headless=False)
• get_auth_info()
• clear_auth()
• validate_auth()

BrowserSession (internal)

• Handles browser automation
• Manages stealth behavior
• Not intended for direct use

Best Practices

1. Always use run.py - Ensures environment
2. Check auth first - Before operations
3. Handle rate limits - Implement retries
4. Include context - Questions are independent
5. Clean sessions - Use cleanup_manager