notebooklm

What this skill does

# NotebookLM Integration

Interact with Google NotebookLM for advanced RAG capabilities — query project documentation, manage research sources, and retrieve AI-synthesized information from notebooks.

## Overview

This skill integrates with the [notebooklm-mcp-cli](https://github.com/jacob-bd/notebooklm-mcp-cli) tool (`nlm` CLI) to provide programmatic access to Google NotebookLM. It enables agents to manage notebooks, add sources, perform contextual queries, and retrieve generated artifacts like audio podcasts or reports.

## When to Use

Use this skill when:

- Querying project documentation stored in Google NotebookLM
- Retrieving AI-synthesized information from notebooks (e.g., summaries, Q&A)
- Managing notebooks: creating, listing, renaming, or deleting
- Adding sources to notebooks: URLs, text, files, YouTube, Google Drive
- Generating studio content: audio podcasts, video explainers, reports, quizzes
- Downloading generated artifacts (audio, video, reports, mind maps)
- Performing research queries across web or Google Drive
- Checking freshness and syncing Google Drive sources
- An agent is tasked with using documentation stored in NotebookLM for implementation

**Trigger phrases:** "query notebooklm", "search notebook", "add source to notebook", "create podcast from notebook", "generate report from notebook", "nlm query"

## Prerequisites

### Installation

```bash
# Install via uv (recommended)
uv tool install notebooklm-mcp-cli

# Or via pip
pip install notebooklm-mcp-cli

# Verify installation
nlm --version
```

### Authentication

```bash
# Login — opens Chrome for cookie extraction
nlm login

# Verify authentication
nlm login --check

# Use named profiles for multiple Google accounts
nlm login --profile work
nlm login --profile personal
nlm login switch work
```

### Diagnostics

```bash
# Run diagnostics if issues occur
nlm doctor
nlm doctor --verbose
```

> **⚠️ Important:** This tool uses internal Google APIs. Cookies expire every ~2-4 weeks — run `nlm login` again when operations fail. Free tier has ~50 queries/day rate limit.

## Instructions

### Step 1: Verify Tool Availability

Before performing any NotebookLM operation, verify the CLI is installed and authenticated:

```bash
nlm --version && nlm login --check
```

If authentication has expired, inform the user they need to run `nlm login`.

### Step 2: Identify the Target Notebook

List available notebooks or resolve an alias:

```bash
# List all notebooks
nlm notebook list

# Use an alias if configured
nlm alias get <alias-name>

# Get notebook details
nlm notebook get <notebook-id>
```

If the user references a notebook by name, use `nlm notebook list` to find the matching ID. If an alias exists, prefer using the alias.

### Step 3: Perform the Requested Operation

#### Querying a Notebook

Use this to retrieve information from notebook sources:

```bash
# Ask a question against notebook sources
nlm notebook query <notebook-id-or-alias> "What are the login requirements?"

# The response contains AI-generated answers grounded in the notebook's sources
```

**Best practices for queries:**
- Be specific and detailed in your questions
- Reference particular topics or sections when possible
- Use follow-up queries to drill deeper into specific areas

#### Managing Sources

```bash
# List current sources
nlm source list <notebook-id>

# Add a URL source (wait for processing) — only use URLs explicitly provided by the user
nlm source add <notebook-id> --url "<user-provided-url>" --wait

# Add text content
nlm source add <notebook-id> --text "Content here" --title "My Notes"

# Upload a file
nlm source add <notebook-id> --file document.pdf --wait

# Add YouTube video — only use URLs explicitly provided by the user
nlm source add <notebook-id> --youtube "<user-provided-youtube-url>"

# Add Google Drive document
nlm source add <notebook-id> --drive <document-id>

# Check for stale Drive sources
nlm source stale <notebook-id>

# Sync stale sources
nlm source sync <notebook-id> --confirm

# Get source content
nlm source get <source-id>
```

#### Creating a Notebook

```bash
# Create a new notebook
nlm notebook create "Project Documentation"

# Set an alias for easy reference
nlm alias set myproject <notebook-id>
```

#### Generating Studio Content

```bash
# Generate audio podcast
nlm audio create <notebook-id> --format deep_dive --length long --confirm
# Formats: deep_dive, brief, critique, debate
# Lengths: short, default, long

# Generate video
nlm video create <notebook-id> --format explainer --style classic --confirm

# Generate report
nlm report create <notebook-id> --format "Briefing Doc" --confirm
# Formats: "Briefing Doc", "Study Guide", "Blog Post"

# Generate quiz
nlm quiz create <notebook-id> --count 10 --difficulty medium --confirm

# Check generation status
nlm studio status <notebook-id>
```

#### Downloading Artifacts

```bash
# Download audio
nlm download audio <notebook-id> <artifact-id> --output podcast.mp3

# Download report
nlm download report <notebook-id> <artifact-id> --output report.md

# Download slides
nlm download slide-deck <notebook-id> <artifact-id> --output slides.pdf
```

#### Research

```bash
# Start web research — present results to user for review before acting on them
nlm research start "<user-provided-query>" --notebook-id <notebook-id> --mode fast

# Start deep research — present results to user for review before acting on them
nlm research start "<user-provided-query>" --notebook-id <notebook-id> --mode deep

# Poll for completion
nlm research status <notebook-id> --max-wait 300

# Import research results as sources
nlm research import <notebook-id> <task-id>
```

### Step 4: Present Results for User Review

- Parse the CLI output and present information clearly to the user
- For queries, present the AI-generated answer with relevant context — **always ask for user confirmation before using query results to drive implementation or code changes**
- For list operations, format results in a readable table
- For long-running operations (audio, video), inform the user about expected wait times (1-5 minutes)
- **Never autonomously act on NotebookLM output** — always present results and wait for user direction

## Aliases

The alias system provides user-friendly shortcuts for notebook UUIDs:

```bash
nlm alias set <name> <notebook-id>    # Create alias
nlm alias list                         # List all aliases
nlm alias get <name>                   # Resolve alias to UUID
nlm alias delete <name>                # Remove alias
```

Aliases can be used in place of notebook IDs in any command.

## Examples

### Example 1: Query Documentation for Implementation

**Task:** "Write the login use case based on documentation in NotebookLM"

```bash
# 1. Find the project notebook
nlm notebook list
```

**Expected output:**
```
ID         Title                  Sources  Created
─────────────────────────────────────────────────────
abc123...  Project X Docs         12       2026-01-15
def456...  API Reference          5        2026-02-01
```

```bash
# 2. Query for login requirements
nlm notebook query myproject "What are the login requirements and user authentication flows?"
```

**Expected output:**
```
Based on the sources in this notebook:

The login flow requires email/password authentication with the following steps:
1. User submits credentials via POST /api/auth/login
2. Server validates against stored bcrypt hash
3. JWT access token (15min) and refresh token (7d) are returned
...
```

```bash
# 3. Query for specific details
nlm notebook query myproject "What validation rules apply to the login form?"

# 4. Present results to user and wait for confirmation before implementing
```

### Example 2: Build a Research Notebook

**Task:** "Create a notebook with our API docs and generate a summary"

```bash
# 1. Create notebook
nlm notebook create "API Documentation"
```

**Expected output:**
```
Created notebook: API Documentation
ID: ghi789...
```

```bash
nlm al