notebooklm
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/guanyang/open-agent-hub --skill notebooklm- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads notebooklm automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
.venvif 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
.venvautomatically - 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 metadataauth_info.json- Authentication statusbrowser_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
| Problem | Solution |
|---|---|
| ModuleNotFoundError | Use run.py wrapper |
| Authentication fails | Browser must be visible for setup! --show-browser |
| Rate limit (50/day) | Wait or switch Google account |
| Browser crashes | python scripts/run.py cleanup_manager.py --preserve-library |
| Notebook not found | Check with notebook_manager.py list |
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 libraryreferences/- Extended documentation:api_reference.md- Detailed API documentation for all scriptstroubleshooting.md- Common issues and solutionsusage_patterns.md- Best practices and workflow examples
.venv/- Isolated Python environment (auto-created on first run).gitignore- Protects sensitive data from being committed
FAQ
What does the notebooklm skill do?
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.
How do I install notebooklm?
Run npx skills add https://github.com/guanyang/open-agent-hub --skill notebooklm in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/notebooklm/ (personal) or .claude/skills/notebooklm/ (project).
Which AI agents can use notebooklm?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is notebooklm safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains notebooklm?
It is published by guanyang in guanyang/open-agent-hub. It has 292 recorded installs.
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