Agent skill

Nlm Skill

by iusztinpaul in iusztinpaul/ai-research-os-workshop

Expert guide for the NotebookLM CLI (nlm) and MCP server - interfaces for Google NotebookLM.

MITAuto-check passedKnowledge Management

Install Nlm Skill

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .claude/skills/nlm-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .claude/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .agents/skills/nlm-skill && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .agents/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .cursor/skills/nlm-skill && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .cursor/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/iusztinpaul/ai-research-os-workshop.git --path plugins/ai-research-os/skills/nlm-skill

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .gemini/skills/nlm-skill && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .gemini/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .github/skills/nlm-skill && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .github/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install iusztinpaul/ai-research-os-workshop nlm-skill --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/iusztinpaul/ai-research-os-workshop.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ai-research-os/skills/nlm-skill .opencode/skills/nlm-skill && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nlm-skill" agent skill from https://github.com/iusztinpaul/ai-research-os-workshop/tree/main/plugins/ai-research-os/skills/nlm-skill into .opencode/skills/nlm-skill/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlm-skill", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: nlm-skill
GitHub stars: 179
Used in: 1 other repo
Token cost: ~6.9k tokens
SKILL.md length: 1,377 words
Files: 4 (incl. references)
Skills in repo: 6
Repo updated: Jun 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Expert guide for the NotebookLM CLI (nlm) and MCP server - interfaces for Google NotebookLM.

Works in 12 steps: Authentication → Notebook Management → Source Management → …
Users want to interact with NotebookLM programmatically
SKILL.md covers Tool Detection (CRITICAL -…, Quick Reference, Critical Rules (Read First!) and Workflow Decision Tree, plus 3 more sections
Reaches youtube.com and example1.com

What it does

Nlm Skill is an agent skill from iusztinpaul/ai-research-os-workshop. 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"…

Its SKILL.md is about 6.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/command_reference.md`, `references/troubleshooting.md` and `references/workflows.md`).

It sits in Knowledge Management, covering Source-grounded notebooks, Podcasting and MCP servers. It works with NotebookLM, Model Context Protocol, Google Drive and YouTube. The repository describes itself as: How to turn your Second Brain into a living research memory that your agents maintain. Workshop with slides, video and code. The licence is MIT.

When your agent uses it

Users want to interact with NotebookLM programmatically
Including: creating/managing notebooks
Adding sources (URLs
Generating content (podcasts

Example prompts

“notebooklm”
“notebook lm”
“podcast generation”
“/nlm-skill”

Requirements

Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

Authentication
Notebook Management
Source Management
Research (Source Discovery)
Content Generation (Studio)
Studio (Artifact Management)
Chat Configuration and Notes
Notebook Sharing
Aliases (UUID Shortcuts)
Configuration
Skill Management
Batch Operations

What it can do on your machine

Read from SKILL.md and the folder at commit dc66605. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- youtube.com
- example1.com
- example2.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nlm Skill loads about 6.9k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 1,377 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~153
When it runs · the whole SKILL.md, loaded when a task matches: ~6.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~17k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from iusztinpaul/ai-research-os-workshop at commit dc66605, republished under its MIT licence (© iusztinpaul). 1,377 words, ~6,929 tokens.

Download SKILL.mdSave it as .claude/skills/nlm-skill/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

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.

bash

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:

bash

# 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):

python

# 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

bash

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)

Other tools: source_list_drive, source_describe, source_get_content, source_rename, source_sync_drive (requires confirm=True), source_delete (requires confirm=True).

CLI Commands

bash

# Adding sources
nlm source add <nb-id> --url "https://..."           # Web page
nlm source add <nb-id> --url "https://youtube.com/..." # YouTube video
nlm source add <nb-id> --text "content" --title "X"  # Pasted text
nlm source add <nb-id> --drive <doc-id>              # Drive doc (auto-detect type)
nlm source add <nb-id> --drive <doc-id> --type slides # Explicit type

# Listing and viewing
nlm source list <nb-id>                # Table of sources
nlm source list <nb-id> --drive        # Show Drive sources with freshness
nlm source list <nb-id> --drive -S     # Skip freshness checks (faster)
nlm source get <source-id>             # Source metadata
nlm source describe <source-id>        # AI summary + keywords
nlm source content <source-id>         # Raw text content
nlm source content <source-id> -o file.txt  # Export to file

# Drive sync (for stale sources)
nlm source stale <nb-id>               # List outdated Drive sources
nlm source sync <nb-id> --confirm      # Sync all stale sources
nlm source sync <nb-id> --source-ids <ids> --confirm  # Sync specific

# Rename
nlm source rename <source-id> "New Title" --notebook <nb-id>
nlm rename source <source-id> "New Title" --notebook <nb-id>  # verb-first

# Deletion
nlm source delete <source-id> --confirm

Drive types: doc, slides, sheets, pdf

4. Research (Source Discovery)

Research finds NEW sources from the web or Google Drive.

MCP Tools

Use research_start with:

source: web or drive
mode: fast (~30s) or deep (~5min, web only)

Workflow: research_start → poll research_status → research_import

CLI Commands

bash

# 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.

artifact_type	Key Options
`audio`	`audio_format`: deep_dive/brief/critique/debate, `audio_length`: short/default/long
`video`	`video_format`: explainer/brief, `visual_style`: auto_select/classic/whiteboard/kawaii/anime/watercolor/retro_print/heritage/paper_craft
`report`	`report_format`: Briefing Doc/Study Guide/Blog Post/Create Your Own, `custom_prompt`
`quiz`	`question_count`, `difficulty`: easy/medium/hard
`flashcards`	`difficulty`: easy/medium/hard
`mind_map`	`title`
`slide_deck`	`slide_format`: detailed_deck/presenter_slides, `slide_length`: short/default
`infographic`	`orientation`: landscape/portrait/square, `detail_level`: concise/standard/detailed, `infographic_style`: auto_select/sketch_note/professional/bento_grid/editorial/instructional/bricks/clay/anime/kawaii/scientific
`data_table`	`description` (REQUIRED)

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)

bash

# 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

bash

# 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.

Show full SKILL.md (528 more words)Show less

Renaming Resources

Rename a Source

MCP Tool: source_rename(notebook_id, source_id, new_title)

CLI:

bash

nlm source rename <source-id> "New Title" --notebook <notebook-id>
nlm rename source <source-id> "New Title" --notebook <notebook-id>  # verb-first

Rename a Studio Artifact

MCP Tools

Use studio_status with action="rename", artifact_id, and new_title.

CLI Commands

bash

nlm studio rename <artifact-id> "New Title"
nlm rename studio <artifact-id> "New Title"  # verb-first alternative

Server Info (Version Check)

MCP Tools

Use server_info to get version and check for updates:

python

mcp__notebooklm-mcp__server_info()
# Returns: version, latest_version, update_available, update_command

CLI Commands

bash

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:

bash

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):

bash

nlm chat configure <id> --goal default
nlm chat configure <id> --goal learning_guide
nlm chat configure <id> --goal custom --prompt "Act as a tutor..."
nlm chat configure <id> --response-length longer  # longer, default, shorter

Notes management:

bash

nlm note create <nb-id> "Content" --title "Title"
nlm note list <nb-id>
nlm note update <nb-id> <note-id> --content "New content"
nlm note delete <nb-id> <note-id> --confirm

MCP Tools

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

bash

# 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:

bash

nlm alias set 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:

bash

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

Available Settings:

Key	Default	Description
`output.format`	`table`	Default output format (table, json)
`output.color`	`true`	Enable colored output
`output.short_ids`	`true`	Show shortened IDs
`auth.browser`	`auto`	Preferred browser for login (auto, chrome, arc, brave, edge, chromium, vivaldi, opera)
`auth.default_profile`	`default`	Profile to use when `--profile` not specified

11. Skill Management

Manage the NotebookLM skill installation for various AI assistants:

bash

nlm skill list                              # Show installation status
nlm skill update                            # Update all outdated skills
nlm skill update <tool>                     # Update specific skill (e.g., claude-code)
nlm skill install <tool>                    # Install skill
nlm skill uninstall <tool>                  # Uninstall skill

Verb-first aliases: nlm update skill, nlm list skills, nlm install skill

Output Formats

Most list commands support multiple formats:

Flag	Description
(none)	Rich table (human-readable)
`--json`	JSON output (for parsing)
`--quiet`	IDs only (for piping)
`--title`	"ID: Title" format
`--url`	"ID: URL" format (sources only)
`--full`	All columns/details

12. Batch Operations

Perform the same action across multiple notebooks at once.

MCP Tools

Use batch with action parameter. Select notebooks by notebook_names, tags, or all=True.

python

batch(action="query", query="What are the key findings?", notebook_names="AI Research, Dev Tools")
batch(action="add_source", source_url="https://example.com", tags="ai,research")
batch(action="create", titles="Project A, Project B, Project C")
batch(action="delete", notebook_names="Old Project", confirm=True)
batch(action="studio", artifact_type="audio", tags="research", confirm=True)

CLI Commands

bash

nlm batch query "What are the key takeaways?" --notebooks "id1,id2"
nlm batch query "Summarize" --tags "ai,research"      # Query by tag
nlm batch query "Summarize" --all                      # Query ALL notebooks
nlm batch add-source --url "https://..." --notebooks "id1,id2"
nlm batch create "Project A, Project B, Project C"     # Create multiple
nlm batch delete --notebooks "id1,id2" --confirm       # Delete multiple
nlm batch studio --type audio --tags "research" --confirm  # Generate across notebooks

13. Cross-Notebook Query

Query multiple notebooks and get aggregated answers with per-notebook citations.

MCP Tools

python

cross_notebook_query(query="Compare approaches", notebook_names="Notebook A, Notebook B")
cross_notebook_query(query="Summarize", tags="ai,research")
cross_notebook_query(query="Everything", all=True)

CLI Commands

bash

nlm cross query "What features are discussed?" --notebooks "id1,id2"
nlm cross query "Compare approaches" --tags "ai,research"
nlm cross query "Summarize everything" --all

14. Pipelines

Define and execute multi-step notebook workflows. Three built-in pipelines plus support for custom YAML pipelines.

MCP Tools

python

pipeline(action="list")  # List available pipelines
pipeline(action="run", notebook_id="...", pipeline_name="ingest-and-podcast", input_url="https://...")

CLI Commands

bash

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

Built-in pipelines: ingest-and-podcast, research-and-report, multi-format

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

python

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

bash

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

bash

nlm notebook create "AI Research 2026"   # Capture ID
nlm alias set 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

Pattern 2: Quick Content Ingestion

bash

nlm source add <id> --url "https://example1.com"
nlm source add <id> --url "https://example2.com"
nlm source add <id> --text "My notes..." --title "Notes"
nlm source list <id>

Pattern 3: Study Materials Generation

bash

nlm report create <id> --format "Study Guide" --confirm
nlm quiz create <id> --count 10 --difficulty 3 --focus "Exam prep" --confirm
nlm flashcards create <id> --difficulty medium --focus "Core terms" --confirm

Pattern 4: Drive Document Workflow

bash

nlm source add <id> --drive 1KQH3eW0hMBp7WK... --type slides
# ... time passes, document is edited ...
nlm source stale <id>                    # Check freshness
nlm source sync <id> --confirm           # Sync if stale

Pattern 5: Batch & Cross-Notebook Workflow

bash

# 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"

Error Recovery

Error	Cause	Solution
"Cookies have expired"	Session timeout	`nlm login`
"authentication may have expired"	Session timeout	`nlm login`
"Notebook not found"	Invalid ID	`nlm notebook list`
"Source not found"	Invalid ID	`nlm source list <nb-id>`
"Rate limit exceeded"	Too many calls	Wait 30s, retry
"Research already in progress"	Pending research	Use `--force` or import first
"Import timed out"	Too many sources	Use `--timeout 600` for larger notebooks
"Google API error code 3"	Transient deep research error	Retry in a few minutes, or use `--mode fast`
Browser doesn't launch	Port conflict	Close browser, retry

Rate Limiting

Wait between operations to avoid rate limits:

Source operations: 2 seconds
Content generation: 5 seconds
Research operations: 2 seconds
Query operations: 2 seconds

Advanced Reference

For detailed information, see:

references/command_reference.md: Complete command signatures
references/troubleshooting.md: Detailed error handling
references/workflows.md: End-to-end task sequences

Files

SKILL.md and 3 other files (references) in plugins/ai-research-os/skills/nlm-skill of iusztinpaul/ai-research-os-workshop.

SKILL.md
references/command_reference.md
references/troubleshooting.md
references/workflows.md

Open the folder on GitHubat commit dc66605

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in iusztinpaul/ai-research-os-workshop, which our catalogue first saw on October 7, 2026.

LeoYeAI/openclaw-master-skillsskills/nlm-notebooklm

Compare with similar skills

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Works with

Questions about Nlm Skill

What does Nlm Skill do?

Expert guide for the NotebookLM CLI (nlm) and MCP server - interfaces for Google NotebookLM. Nlm Skill is an agent skill from iusztinpaul/ai-research-os-workshop. Expert guide for the NotebookLM CLI (nlm) and MCP server - interfaces for Google NotebookLM.

When should I use Nlm Skill?

Nlm Skill fits situations like: users want to interact with NotebookLM programmatically; including: creating/managing notebooks; adding sources (URLs; generating content (podcasts.

How do I install Nlm Skill in Claude Code?

Run `npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a claude-code`. Or copy the skill folder (plugins/ai-research-os/skills/nlm-skill in iusztinpaul/ai-research-os-workshop) into .claude/skills/nlm-skill in your project. Claude Code loads it when a task matches its description.

How do I install Nlm Skill in Codex?

Run `npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a codex`. Or copy the skill folder (plugins/ai-research-os/skills/nlm-skill in iusztinpaul/ai-research-os-workshop) into .agents/skills/nlm-skill in your project. Codex loads it when a task matches its description.

Can I use Nlm Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add iusztinpaul/ai-research-os-workshop --skill nlm-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nlm-skill, .gemini/skills/nlm-skill, .github/skills/nlm-skill and .opencode/skills/nlm-skill in your project.

What does Nlm Skill need to run?

SKILL.md names no scripts, command-line tools or credentials: Nlm Skill is instructions for the agent only. Our summary lists: Python 3.

Does Nlm Skill access the network?

SKILL.md names 3 domains. In commands or code: youtube.com, example1.com and example2.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Nlm Skill safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nlm Skill use?

Nlm Skill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nlm Skill use?

About 6.9k tokens (SKILL.md is roughly 28k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.7k tokens, read only when the agent opens those files.

What are the alternatives to Nlm Skill?

Skills that share tags, products or a category with Nlm Skill: NotebookLM CLI Guide (jacob-bd/notebooklm-cli, 256 stars), Notebooklm (alirezarezvani/claude-skills, 28k stars), Multi-Source to NotebookLM Processor (joeseesun/qiaomu-anything-to-notebooklm, 6.2k stars) and Notebooklm CLI (ItamarZand88/CLI-Anything-WEB, 231 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nlm Skill?

iusztinpaul (a GitHub user) maintains it in iusztinpaul/ai-research-os-workshop, which has 179 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on June 27, 2026.

Source: iusztinpaul/ai-research-os-workshop on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Nlm Skill

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

NotebookLM CLI & MCP Expert

Tool Detection (CRITICAL - Read First!)

Quick Reference

Critical Rules (Read First!)

Workflow Decision Tree

Command Categories

1. Authentication

MCP Authentication

2. Notebook Management

MCP Tools

CLI Commands

3. Source Management

MCP Tools

CLI Commands

4. Research (Source Discovery)

MCP Tools

CLI Commands

5. Content Generation (Studio)

MCP Tools (Unified Creation)

CLI Commands

6. Studio (Artifact Management)

MCP Tools

CLI Commands

Renaming Resources

Rename a Source

Rename a Studio Artifact

MCP Tools

CLI Commands

Server Info (Version Check)

MCP Tools

CLI Commands

7. Chat Configuration and Notes

MCP Tools

CLI Commands

8. Notebook Sharing

MCP Tools

CLI Commands

9. Aliases (UUID Shortcuts)

10. Configuration

11. Skill Management

Output Formats

12. Batch Operations

MCP Tools

CLI Commands

13. Cross-Notebook Query

MCP Tools

CLI Commands

14. Pipelines

MCP Tools

CLI Commands

15. Tags & Smart Select

MCP Tools

CLI Commands

Common Patterns

Pattern 1: Research → Podcast Pipeline

Pattern 2: Quick Content Ingestion

Pattern 3: Study Materials Generation

Pattern 4: Drive Document Workflow

Pattern 5: Batch & Cross-Notebook Workflow

Error Recovery

Rate Limiting

Advanced Reference

Files

Used in 1 other repository