nblm

NotebookLM Quick Commands

Query Google NotebookLM for source-grounded, citation-backed answers.

Environment

All dependencies and authentication are handled automatically by run.py:

• First run creates .venv and installs Python/Node.js dependencies
• If Google auth is missing or expired, a browser window opens automatically
• No manual pre-flight steps required

Usage

/nblm <command> [args]

Commands

Notebook Management

Source Management

Upload options:

• --use-active - Upload to the currently active notebook
• --create-new - Create a new notebook named after the file/folder
• --notebook-id <id> - Upload to a specific notebook
• --dry-run - Show sync plan without executing (folder sync)
• --rebuild - Force rebuild tracking file (folder sync)

Important: When user runs upload without specifying a target, ASK them first:

"Would you like to upload to the active notebook, or create a new notebook?" Then pass the appropriate flag (--use-active or --create-new).

Chat & Audio/Media

Command Routing

Based on $ARGUMENTS, execute the appropriate command:

$IF($ARGUMENTS, Parse the command from: "$ARGUMENTS"

login → python scripts/run.py auth_manager.py setup --service google

accounts → python scripts/run.py auth_manager.py accounts list

accounts add → python scripts/run.py auth_manager.py accounts add

accounts switch → python scripts/run.py auth_manager.py accounts switch "<id>"

accounts remove → python scripts/run.py auth_manager.py accounts remove "<id>"

accounts use → python scripts/run.py auth_manager.py accounts use "<id>"

accounts clear → python scripts/run.py auth_manager.py accounts clear

status → Run both:

• python scripts/run.py auth_manager.py status
• python scripts/run.py notebook_manager.py list

local → python scripts/run.py notebook_manager.py list

remote → python scripts/run.py nblm_cli.py notebooks

create → python scripts/run.py nblm_cli.py create "<name>"

delete [--id ID] → python scripts/run.py nblm_cli.py delete <args>

rename [--id ID] → python scripts/run.py nblm_cli.py rename "<name>" <args>

summary [--id ID] → python scripts/run.py nblm_cli.py summary <args>

describe [--id ID] → python scripts/run.py nblm_cli.py describe <args>

add → Smart add workflow (auto-detects URL vs notebook ID)

activate → python scripts/run.py notebook_manager.py activate --id "<id>"

sources [--id ID] → python scripts/run.py nblm_cli.py sources <args>

upload → First ASK user: "Upload to active notebook or create new?" Then: - Active: python scripts/run.py source_manager.py add --file "<file>" --use-active - New: python scripts/run.py source_manager.py add --file "<file>" --create-new

upload → Sync a folder: - First ASK user: "Sync to active notebook, create new, or specify notebook?" - Active: python scripts/run.py source_manager.py sync "<folder>" --use-active - New: python scripts/run.py source_manager.py sync "<folder>" --create-new - Specific: python scripts/run.py source_manager.py sync "<folder>" --notebook-id ID - Dry-run: python scripts/run.py source_manager.py sync "<folder>" --dry-run - Rebuild: python scripts/run.py source_manager.py sync "<folder>" --rebuild

upload-zlib → First ASK user: "Upload to active notebook or create new?" Then: - Active: python scripts/run.py source_manager.py add --url "<url>" --use-active - New: python scripts/run.py source_manager.py add --url "<url>" --create-new

upload-url → python scripts/run.py nblm_cli.py upload-url "<url>"

upload-youtube → python scripts/run.py nblm_cli.py upload-youtube "<url>"

upload-text → python scripts/run.py nblm_cli.py upload-text "<title>" <args>

source-text → python scripts/run.py nblm_cli.py source-text "<id>"

source-guide → python scripts/run.py nblm_cli.py source-guide "<id>"

source-rename → python scripts/run.py nblm_cli.py source-rename "<id>" "<name>"

source-refresh → python scripts/run.py nblm_cli.py source-refresh "<id>"

source-delete → python scripts/run.py nblm_cli.py source-delete "<id>"

ask → python scripts/run.py nblm_cli.py ask "<question>"

podcast → python scripts/run.py artifact_manager.py generate --format DEEP_DIVE <args>

podcast-status → python scripts/run.py artifact_manager.py status --task-id "<task-id>"

podcast-download [output-path] → python scripts/run.py artifact_manager.py download "<output-path>"

briefing → python scripts/run.py artifact_manager.py generate --format BRIEF <args>

debate → python scripts/run.py artifact_manager.py generate --format DEBATE <args>

slides → python scripts/run.py artifact_manager.py generate-slides <args>

slides-download [output-path] → python scripts/run.py artifact_manager.py download "<output-path>" --type slide-deck

infographic → python scripts/run.py artifact_manager.py generate-infographic <args>

infographic-download [output-path] → python scripts/run.py artifact_manager.py download "<output-path>" --type infographic

media-list [--type TYPE] → python scripts/run.py artifact_manager.py list <args>

media-delete → python scripts/run.py artifact_manager.py delete "<id>"

If command not recognized, show usage help.,

Show available commands with /nblm (no arguments) )

Podcast Options

/nblm podcast --length DEFAULT --wait --output ./podcast.mp3
/nblm podcast --instructions "Focus on the key findings"
/nblm briefing --wait --output ./summary.mp3
/nblm debate --instructions "Compare the two approaches"

Slide Deck Options

/nblm slides --format DETAILED_DECK --wait --output ./presentation.pdf
/nblm slides --instructions "Focus on key diagrams" --format PRESENTER_SLIDES

Infographic Options

/nblm infographic --orientation LANDSCAPE --wait --output ./visual.png
/nblm infographic --instructions "Highlight comparison" --detail-level DETAILED

Media Generation

Examples

Download & Manage

Extended Documentation

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

The add command now automatically discovers metadata from the notebook:

What Smart Add does:

1. Fetches notebook title from NotebookLM API
2. Queries the notebook content to generate description and topics
3. Adds to local library with discovered metadata

Supported input formats:

• Notebook ID: 5fd9f36b-8000-401d-a7a0-7aa3f7832644
• Full URL: https://notebooklm.google.com/notebook/5fd9f36b-8000-401d-a7a0-7aa3f7832644

NEVER manually specify --name, --description, or --topics unless the user explicitly provides them.

Critical: Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS use python scripts/run.py [script]:

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

If not authenticated, proceed to setup.

Step 2: Authenticate (One-Time 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

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

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:

   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

Z-Library Integration

Triggers

• User provides Z-Library URL (zlib.li, z-lib.org, zh.zlib.li)
• User says "download this book to NotebookLM"
• User says "add this book from Z-Library"

Setup (One-Time)

# Authenticate with Z-Library
python scripts/run.py auth_manager.py setup --service zlibrary

Commands

# Add book from Z-Library
python scripts/run.py source_manager.py add --url "https://zh.zlib.li/book/..."
 
# Check Z-Library auth status
python scripts/run.py auth_manager.py status --service zlibrary

Script Reference

Authentication Management (auth_manager.py)

python scripts/run.py auth_manager.py setup                    # Default: Google
python scripts/run.py auth_manager.py setup --service google
python scripts/run.py auth_manager.py setup --service zlibrary
python scripts/run.py auth_manager.py status                   # Show all services
python scripts/run.py auth_manager.py status --service zlibrary
python scripts/run.py auth_manager.py clear --service zlibrary # Clear auth
 
# Multi-Account Management (Google)
python scripts/run.py auth_manager.py accounts list             # List all accounts
python scripts/run.py auth_manager.py accounts add              # Add new account
python scripts/run.py auth_manager.py accounts switch 1         # Switch by index
python scripts/run.py auth_manager.py accounts switch user@gmail.com  # Switch by email
python scripts/run.py auth_manager.py accounts remove 2         # Remove account

Notebook Management (notebook_manager.py)

python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS
# OR use notebook ID directly:
python scripts/run.py notebook_manager.py add --notebook-id ID --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]

Source Manager (source_manager.py)

# Upload to active notebook
python scripts/run.py source_manager.py add --file "/path/to/book.pdf" --use-active
 
# Create new notebook for upload
python scripts/run.py source_manager.py add --file "/path/to/book.pdf" --create-new
 
# Upload to specific notebook
python scripts/run.py source_manager.py add --file "/path/to/book.pdf" --notebook-id NOTEBOOK_ID
 
# Z-Library download and upload
python scripts/run.py source_manager.py add --url "https://zh.zlib.li/book/..." --use-active
python scripts/run.py source_manager.py add --url "https://zh.zlib.li/book/..." --create-new
 
# Sync a folder (new!)
python scripts/run.py source_manager.py sync "/path/to/docs" --use-active
python scripts/run.py source_manager.py sync "/path/to/docs" --create-new
python scripts/run.py source_manager.py sync "/path/to/docs" --notebook-id NOTEBOOK_ID
 
# Sync options (new!)
python scripts/run.py source_manager.py sync "/path/to/docs" --dry-run    # Preview only
python scripts/run.py source_manager.py sync "/path/to/docs" --rebuild   # Force re-hash all files

Folder Sync:

• Scans folder for supported types: PDF, TXT, MD, DOCX, HTML, EPUB
• Tracks sync state internally (no per-folder tracking file to manage)
• Sync strategy: add new, update modified (delete + re-upload), skip unchanged
• Multi-account aware (tracks which Google account was used) Note: One of --use-active, --create-new, or --notebook-id is REQUIRED. Uploads wait for NotebookLM processing and print progress as Ready: N/T. Press Ctrl+C to stop waiting. Local file uploads use browser automation and require Google authentication. If browser automation is unavailable, set NOTEBOOKLM_UPLOAD_MODE=text to upload extracted text instead (PDFs require pypdf).

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

Watchdog Status (auth_manager.py)

python scripts/run.py auth_manager.py watchdog-status

Environment Management

The virtual environment is automatically managed:

• First run creates .venv automatically
• Dependencies install automatically
• Node.js dependencies install automatically
• agent-browser daemon starts on demand and keeps browser state in memory
• daemon stops after 10 minutes of inactivity (any agent-browser command resets the timer)
• set AGENT_BROWSER_OWNER_PID to auto-stop when the agent process exits
• scripts/run.py sets AGENT_BROWSER_OWNER_PID to its parent PID by default
• 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
npm install
npm run install-browsers

Data Storage

All data stored in ~/.claude/skills/notebooklm/data/:

• library.json - Notebook metadata (with account associations)
• auth/google/ - Multi-account Google auth
  • index.json - Account index (active account, list)
  • <n>-<email>.json - Per-account credentials
• auth/zlibrary.json - Z-Library auth state
• agent_browser/session_id - Current daemon session ID
• agent_browser/last_activity.json - Last activity timestamp for idle shutdown
• agent_browser/watchdog.pid - Idle watchdog process ID

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

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 per account; use multiple accounts to increase)
• 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 committed