notebooklm
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.
Best use case
notebooklm is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
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?
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.
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.
Related Guides
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
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
nextjs-best-practices
Next.js App Router principles. Server Components, data fetching, routing patterns.
network-101
Configure and test common network services (HTTP, HTTPS, SNMP, SMB) for penetration testing lab environments. Enable hands-on practice with service enumeration, log analysis, and security testing against properly configured target systems.
neon-postgres
Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration
nanobanana-ppt-skills
AI-powered PPT generation with document analysis and styled images
multi-agent-patterns
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.
monorepo-management
Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.
monetization
Estrategia e implementacao de monetizacao para produtos digitais - Stripe, subscriptions, pricing experiments, freemium, upgrade flows, churn prevention, revenue optimization e modelos de negocio SaaS.
modern-javascript-patterns
Comprehensive guide for mastering modern JavaScript (ES6+) features, functional programming patterns, and best practices for writing clean, maintainable, and performant code.
microservices-patterns
Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.
mcp-builder
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.
makepad-skills
Makepad UI development skills for Rust apps: setup, patterns, shaders, packaging, and troubleshooting.
m365-agents-py
Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth.