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....
Best use case
notebooklm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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....
Teams using notebooklm should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/notebooklm/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How notebooklm Compares
| Feature / Agent | notebooklm | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this 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....
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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:
```bash
# 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]`:**
```bash
# ✅ 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
```bash
python scripts/run.py auth_manager.py status
```
If not authenticated, proceed to setup.
### Step 2: Authenticate (One-Time Setup)
```bash
# 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
```bash
# 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
```bash
# 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:
```bash
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`)
```bash
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`)
```bash
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`)
```bash
python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL] [--show-browser]
```
### Data Cleanup (`cleanup_manager.py`)
```bash
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):
```bash
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:
```env
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
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)
- 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 committedRelated Skills
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