Expert guide for the NotebookLM CLI (`nlm`) and MCP server - interfaces for Google NotebookLM. Use this skill when users want to interact with NotebookLM programmatically, including: creating/managing notebooks, adding sources (URLs, YouTube, text, Google Drive), generating content (podcasts, reports, quizzes, flashcards, mind maps, slides, infographics, videos, data tables), conducting research, chatting with sources, or automating NotebookLM workflows. Triggers on mentions of "nlm", "notebooklm", "notebook lm", "podcast generation", "audio overview", or any NotebookLM-related automation task.
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name
nlm-skill
description
Expert guide for the NotebookLM CLI (`nlm`) and MCP server - interfaces for Google NotebookLM. Use this skill when users want to interact with NotebookLM programmatically, including: creating/managing notebooks, adding sources (URLs, YouTube, text, Google Drive), generating content (podcasts, reports, quizzes, flashcards, mind maps, slides, infographics, videos, data tables), conducting research, chatting with sources, or automating NotebookLM workflows. Triggers on mentions of "nlm", "notebooklm", "notebook lm", "podcast generation", "audio overview", or any NotebookLM-related automation task.
version
0.5.13
NotebookLM CLI & MCP Expert
This skill provides comprehensive guidance for using NotebookLM via both the nlm CLI and MCP tools.
Tool Detection (CRITICAL - Read First!)
ALWAYS check which tools are available before proceeding:
Check for MCP tools: Look for tools starting with mcp__notebooklm-mcp__* or mcp_notebooklm_*
If BOTH MCP tools AND CLI are available: ASK the user which they prefer to use before proceeding
If only MCP tools are available: Use them directly (refer to tool docstrings for parameters)
If only CLI is available: Use nlm CLI commands via Bash
Decision Logic:
has_mcp_tools = check_available_tools() # Look for mcp__notebooklm-mcp__* or mcp_notebooklm_*
has_cli = check_bash_available() # Can run nlm commands
if has_mcp_tools and has_cli:
# ASK USER: "I can use either MCP tools or the nlm CLI. Which do you prefer?"
user_preference = ask_user()
else if has_mcp_tools:
# Use MCP tools directly
mcp__notebooklm-mcp__notebook_list()
else:
# Use CLI via Bash
bash("nlm notebook list")
This skill documents BOTH approaches. Choose the appropriate one based on tool availability and user preference.
Quick Reference
Run nlm --ai to get comprehensive AI-optimized documentation - this provides a complete view of all CLI capabilities.
nlm --help# List all commands
nlm <command> --help# Help for specific command
nlm --ai # Full AI-optimized documentation (RECOMMENDED)
nlm --version # Check installed version
Critical Rules (Read First!)
Always authenticate first: Run nlm login before any operations
Sessions expire in ~20 minutes: Re-run nlm login if commands start failing
⚠️ ALWAYS ASK USER BEFORE DELETE: Before executing ANY delete command, ask the user for explicit confirmation. Deletions are irreversible. Show what will be deleted and warn about permanent data loss.
--confirm is REQUIRED: All generation and delete commands need --confirm or -y (CLI) or confirm=True (MCP)
Research requires --notebook-id: The flag is mandatory, not positional
Capture IDs from output: Create/start commands return IDs needed for subsequent operations
Use aliases: Simplify long UUIDs with nlm alias set <name> <uuid>
Check aliases before creating: Run nlm alias list before creating a new alias to avoid conflicts with existing names.
DO NOT launch REPL: Never use nlm chat start - it opens an interactive REPL that AI tools cannot control. Use nlm notebook query for one-shot Q&A instead.
Choose output format wisely: Default output (no flags) is compact and token-efficient—use it for status checks. Use --quiet to capture IDs for piping. Only use --json when you need to parse specific fields programmatically.
Use --help when unsure: Run nlm <command> --help to see available options and flags for any command.
Workflow Decision Tree
Use this to determine the right sequence of commands:
User wants to...
│
├─► Work with NotebookLM for the first time
│ └─► nlm login → nlm notebook create "Title"
│
├─► Add content to a notebook
│ ├─► From a URL/webpage → nlm source add <nb-id> --url "https://..."
│ ├─► From YouTube → nlm source add <nb-id> --url "https://youtube.com/..."
│ ├─► From pasted text → nlm source add <nb-id> --text "content" --title "Title"
│ ├─► From Google Drive → nlm source add <nb-id> --drive <doc-id> --type doc
│ └─► Discover new sources → nlm research start "query" --notebook-id <nb-id>
│
├─► Generate content from sources
│ ├─► Podcast/Audio → nlm audio create <nb-id> --confirm
│ ├─► Written summary → nlm report create <nb-id> --confirm
│ ├─► Study materials → nlm quiz/flashcards create <nb-id> --confirm
│ ├─► Visual content → nlm mindmap/slides/infographic create <nb-id> --confirm
│ ├─► Video → nlm video create <nb-id> --confirm
│ └─► Extract data → nlm data-table create <nb-id> "description" --confirm
│
├─► Ask questions about sources
│ └─► nlm notebook query <nb-id> "question"
│ (Use --conversation-id for follow-ups)
│ ⚠️ Do NOT use `nlm chat start` - it's a REPL for humans only
│
├─► Check generation status
│ └─► nlm studio status <nb-id>
│
└─► Manage/cleanup
├─► List notebooks → nlm notebook list
├─► List sources → nlm source list <nb-id>
├─► Delete source → nlm source delete <source-id> --confirm
└─► Delete notebook → nlm notebook delete <nb-id> --confirm
Command Categories
1. Authentication
MCP Authentication
If using MCP tools and encountering authentication errors:
# Run the CLI authentication (works for both CLI and MCP)
nlm login
# Then reload tokens in MCP
mcp__notebooklm-mcp__refresh_auth()
Or manually save cookies via MCP (fallback):
# Extract cookies from Chrome DevTools and save
mcp__notebooklm-mcp__save_auth_tokens(cookies="<cookie_header>")
#### CLI Authentication
```bash
nlm login # Launch browser, extract cookies (primary method)
nlm login --check # Validate current session
nlm login --profile work # Use named profile for multiple accounts
nlm login --provider openclaw --cdp-url http://127.0.0.1:18800 # External CDP provider
nlm login switch <profile> # Switch the default profile
nlm login profile list # List all profiles with email addresses
nlm login profile delete <name> # Delete a profile
nlm login profile rename <old> <new> # Rename a profile
Multi-Profile Support: Each profile gets its own isolated browser session (supports Chrome, Arc, Brave, Edge, Chromium, and more), so you can be logged into multiple Google accounts simultaneously.
Session lifetime: ~20 minutes. Re-authenticate when commands fail with auth errors.
Switching MCP Accounts: The MCP server always uses the active default profile. If you need to switch which Google account the MCP server is communicating with, you MUST use the CLI: run nlm login switch <name>. Your next MCP tool call will instantly use the new account.
Note: Both MCP and CLI share the same authentication backend, so authenticating with one works for both.
2. Notebook Management
MCP Tools
Use tools: notebook_list, notebook_create, notebook_get, notebook_describe, notebook_query, notebook_rename, notebook_delete. All accept notebook_id parameter. Delete requires confirm=True.
CLI Commands
nlm notebook list # List all notebooks
nlm notebook list --json # JSON output for parsing
nlm notebook list --quiet # IDs only (for scripting)
nlm notebook create "Title"# Create notebook, returns ID
nlm notebook get <id> # Get notebook details
nlm notebook describe <id> # AI-generated summary + suggested topics
nlm notebook query <id> "question"# One-shot Q&A with sources
nlm notebook rename <id> "New Title"# Rename notebook
nlm notebook delete <id> --confirm # PERMANENT deletion
3. Source Management
MCP Tools
Use source_add with these source_type values:
url - Web page or YouTube URL (url param)
text - Pasted content (text + title params)
file - Local file upload (file_path param)
drive - Google Drive doc (document_id + doc_type params)
# Start research (--notebook-id is REQUIRED)
nlm research start "query" --notebook-id <id> # Fast web (~30s)
nlm research start "query" --notebook-id <id> --mode deep # Deep web (~5min)
nlm research start "query" --notebook-id <id> --source drive # Drive search# Check progress
nlm research status <nb-id> # Poll until done (5min max)
nlm research status <nb-id> --max-wait 0 # Single check, no waiting
nlm research status <nb-id> --task-id <tid> # Check specific task
nlm research status <nb-id> --full # Full details# Import discovered sources
nlm research import <nb-id> <task-id> # Import all
nlm research import <nb-id> <task-id> --indices 0,2,5 # Import specific
nlm research import <nb-id> <task-id> --timeout 600 # Custom timeout (default: 300s)
Modes: fast (~30s, ~10 sources) | deep (~5min, ~40+ sources, web only)
5. Content Generation (Studio)
MCP Tools (Unified Creation)
Use studio_create with artifact_type and type-specific options. All require confirm=True.
Common options: source_ids, language (BCP-47 code), focus_prompt
Revise Slides: Use studio_revise to revise individual slides in an existing slide deck.
Requires artifact_id (from studio_status) and slide_instructions
Creates a NEW artifact — the original is not modified
Slide numbers are 1-based (slide 1 = first slide)
Poll studio_status after calling to check when the new deck is ready
CLI Commands
All generation commands share these flags:
--confirm or -y: REQUIRED to execute
--source-ids <id1,id2>: Limit to specific sources
--language <code>: BCP-47 code (en, es, fr, de, ja)
# Audio (Podcast)
nlm audio create <id> --confirm
nlm audio create <id> --format deep_dive --length default --confirm
nlm audio create <id> --format brief --focus "key topic" --confirm
# Formats: deep_dive, brief, critique, debate# Lengths: short, default, long# Report
nlm report create <id> --confirm
nlm report create <id> --format "Study Guide" --confirm
nlm report create <id> --format "Create Your Own" --prompt "Custom..." --confirm
# Formats: "Briefing Doc", "Study Guide", "Blog Post", "Create Your Own"# Quiz
nlm quiz create <id> --confirm
nlm quiz create <id> --count 5 --difficulty 3 --confirm
nlm quiz create <id> --count 10 --difficulty 3 --focus "Focus on key concepts" --confirm
# Count: number of questions (default: 2)# Difficulty: 1-5 (1=easy, 5=hard)# Focus: optional text to guide quiz generation# Flashcards
nlm flashcards create <id> --confirm
nlm flashcards create <id> --difficulty hard --confirm
nlm flashcards create <id> --difficulty medium --focus "Focus on definitions" --confirm
# Difficulty: easy, medium, hard# Focus: optional text to guide flashcard generation# Mind Map
nlm mindmap create <id> --confirm
nlm mindmap create <id> --title "Topic Overview" --confirm
nlm mindmap list <id> # List existing mind maps# Slides
nlm slides create <id> --confirm
nlm slides create <id> --format presenter --length short --confirm
# Formats: detailed, presenter | Lengths: short, default
nlm slides revise <artifact-id> --slide '1 Make the title larger' --confirm
# Creates a NEW deck with revisions. Original unchanged.# Infographic
nlm infographic create <id> --confirm
nlm infographic create <id> --orientation portrait --detail detailed --style professional --confirm
# Orientations: landscape, portrait, square# Detail: concise, standard, detailed# Styles: auto_select, sketch_note, professional, bento_grid, editorial, instructional, bricks, clay, anime, kawaii, scientific# Video
nlm video create <id> --confirm
nlm video create <id> --format brief --style whiteboard --confirm
# Formats: explainer, brief# Styles: auto_select, classic, whiteboard, kawaii, anime, watercolor, retro_print, heritage, paper_craft# Data Table
nlm data-table create <id> "Extract all dates and events" --confirm
# DESCRIPTION is required as second argument
6. Studio (Artifact Management)
MCP Tools
Use studio_status to check progress (or rename with action="rename"). Use download_artifact with artifact_type and output_path. Use export_artifact with export_type: docs/sheets. Delete with studio_delete (requires confirm=True).
CLI Commands
# Check status
nlm studio status <nb-id> # List all artifacts
nlm studio status <nb-id> --full # Show full details (including custom prompts)
nlm studio status <nb-id> --json # JSON output# Download artifacts
nlm download audio <nb-id> --output podcast.mp3
nlm download video <nb-id> --output video.mp4
nlm download report <nb-id> --output report.md
nlm download slide-deck <nb-id> --output slides.pdf # PDF (default)
nlm download slide-deck <nb-id> --output slides.pptx --format pptx # PPTX
nlm download quiz <nb-id> --output quiz.json --format json
# Export to Google Docs/Sheets
nlm export sheets <nb-id> <artifact-id> --title "My Data Table"
nlm export docs <nb-id> <artifact-id> --title "My Report"# Delete artifact
nlm studio delete <nb-id> <artifact-id> --confirm
Status values: completed (✓), in_progress (●), failed (✗)
Prompt Extraction: The studio_status tool returns a custom_instructions field for each artifact. This contains the original focus prompt or custom instructions used to generate that artifact (e.g., the prompt for a "Create Your Own" report, or the focus topic for an Audio Overview). This is useful for retrieving the exact prompt that generated a successful artifact.
nlm --version # Shows version and update availability
7. Chat Configuration and Notes
MCP Tools
Use chat_configure with goal: default/learning_guide/custom. Use note with action: create/list/update/delete. Delete requires confirm=True.
CLI Commands
⚠️ AI TOOLS: DO NOT USE nlm chat start - It launches an interactive REPL that cannot be controlled programmatically. Use nlm notebook query for one-shot Q&A instead.
For human users at a terminal:
nlm chat start <nb-id> # Launch interactive REPL
REPL Commands:
/sources - List available sources
/clear - Reset conversation context
/help - Show commands
/exit - Exit REPL
Configure chat behavior (works for both REPL and query):
Use notebook_share_status to check, notebook_share_public to enable/disable public link, notebook_share_invite with email and role: viewer/editor.
CLI Commands
# Check sharing status
nlm share status <nb-id>
# Enable/disable public link
nlm share public <nb-id> # Enable
nlm share public <nb-id> --off # Disable# Invite collaborator
nlm share invite <nb-id> user@example.com
nlm share invite <nb-id> user@example.com --role editor
9. Aliases (UUID Shortcuts)
Simplify long UUIDs:
nlm aliasset myproject abc123-def456... # Create alias (auto-detects type)
nlm alias get myproject # Resolve to UUID
nlm alias list # List all aliases
nlm alias delete myproject # Remove alias# Use aliases anywhere
nlm notebook get myproject
nlm source list myproject
nlm audio create myproject --confirm
10. Configuration
CLI-only commands for managing settings:
nlm config show # Show current config
nlm config get <key> # Get specific setting
nlm config set <key> <value> # Update setting
nlm config set output.format json # Change default output# For switching profiles, prefer the simpler command:
nlm login switch work # Switch default profile
Define and execute multi-step notebook workflows. Three built-in pipelines plus support for custom YAML pipelines.
MCP Tools
pipeline(action="list") # List available pipelines
pipeline(action="run", notebook_id="...", pipeline_name="ingest-and-podcast", input_url="https://...")
CLI Commands
nlm pipeline list # List available pipelines
nlm pipeline run <notebook> ingest-and-podcast --url "https://..."
nlm pipeline run <notebook> research-and-report --url "https://..."
nlm pipeline run <notebook> multi-format # Audio + report + flashcards
Create custom pipelines: add YAML files to ~/.notebooklm-mcp-cli/pipelines/
15. Tags & Smart Select
Tag notebooks for organization and use tags to target batch operations.
MCP Tools
tag(action="add", notebook_id="...", tags="ai,research,llm")
tag(action="remove", notebook_id="...", tags="ai")
tag(action="list") # List all tagged notebooks
tag(action="select", query="ai research") # Find notebooks by tag match
CLI Commands
nlm tag add <notebook> --tags "ai,research,llm"# Add tags
nlm tag add <notebook> --tags "ai" --title "My Notebook"# With display title
nlm tag remove <notebook> --tags "ai"# Remove tags
nlm tag list # List all tagged notebooks
nlm tag select"ai research"# Find notebooks by tag match
Common Patterns
Pattern 1: Research → Podcast Pipeline
nlm notebook create "AI Research 2026"# Capture ID
nlm aliasset ai <notebook-id>
nlm research start "agentic AI trends" --notebook-id ai --mode deep
nlm research status ai --max-wait 300 # Wait up to 5 min
nlm research import ai <task-id> # Import all sources
nlm audio create ai --format deep_dive --confirm
nlm studio status ai # Check generation progress
nlm source add <id> --drive 1KQH3eW0hMBp7WK... --type slides
# ... time passes, document is edited ...
nlm source stale <id> # Check freshness
nlm sourcesync <id> --confirm # Sync if stale
Pattern 5: Batch & Cross-Notebook Workflow
# Tag notebooks for organization
nlm tag add <id1> --tags "ai,research"
nlm tag add <id2> --tags "ai,product"# Query across tagged notebooks
nlm cross query "What are the main conclusions?" --tags "ai"# Batch generate podcasts for all tagged notebooks
nlm batch studio --type audio --tags "ai" --confirm
# Run a pipeline on a single notebook
nlm pipeline run <id> ingest-and-podcast --url "https://example.com"
