notebooklm

NotebookLM Skill by RoboNuggets

Connect your AI agent to Google NotebookLM. Add sources automatically, query your notebooks, generate branded slide decks, and keep your session alive without manual re-login.

Built on top of notebooklm-py by Teng Lin.

Prerequisites

1. Install notebooklm-py

pip install "notebooklm-py[browser]"

The [browser] extra installs Playwright, needed for the one-time login. All queries run without a browser after that.

If pip is unavailable or the package is outdated, install from a specific GitHub release tag:

# Get the latest release tag
LATEST_TAG=$(curl -s https://api.github.com/repos/teng-lin/notebooklm-py/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
pip install "git+https://github.com/teng-lin/notebooklm-py@${LATEST_TAG}"

Do NOT install from git+https://github.com/teng-lin/notebooklm-py without a tag — the main branch may contain unstable code.

2. Install Playwright's Chromium browser (one-time)

playwright install chromium

Auth Setup (One-Time)

python scripts/nlm.py login

This opens a browser window. Sign into your Google account, wait until you see the NotebookLM homepage, then press ENTER in the terminal. Your session is saved to ~/.notebooklm/storage_state.json.

After this, your agent can access NotebookLM without any manual login.

Verify it worked

python scripts/nlm.py list

You should see your notebooks listed.

Core Commands

All commands run through python scripts/nlm.py.

Notebooks

# List all notebooks
python scripts/nlm.py list

# Create a new notebook
python scripts/nlm.py create "My Research"

# Describe a notebook (AI summary of contents)
python scripts/nlm.py describe NOTEBOOK_ID

Sources

# Add a URL (articles, YouTube videos, etc.)
python scripts/nlm.py add-source --notebook-id NOTEBOOK_ID --url "https://example.com/article"

# Add a local file (PDF, markdown, txt, docx)
python scripts/nlm.py add-source --notebook-id NOTEBOOK_ID --file "/path/to/doc.pdf"

# Add raw text
python scripts/nlm.py add-source --notebook-id NOTEBOOK_ID --title "My Notes" --text "Content here..."

# List sources in a notebook
python scripts/nlm.py sources --notebook-id NOTEBOOK_ID

Querying

# Ask a question (set active notebook first, or pass --notebook-id)
python scripts/nlm.py ask "What are the main concepts?" --notebook-id NOTEBOOK_ID

Local Library (save notebook metadata)

# Add to local library with a name and description
python scripts/nlm.py library-add --notebook-id NOTEBOOK_ID --name "Claude Docs" --description "All Claude documentation"

# Set as the active (default) notebook
python scripts/nlm.py library-activate claude-docs

# List library
python scripts/nlm.py library-list

Audio & Reports

# Generate deep-dive audio (podcast style)
python scripts/nlm.py generate-audio --notebook-id NOTEBOOK_ID

# Generate a briefing doc
python scripts/nlm.py generate-report --notebook-id NOTEBOOK_ID

# Generate a study guide
python scripts/nlm.py generate-report --notebook-id NOTEBOOK_ID --format study_guide

# Generate a blog post
python scripts/nlm.py generate-report --notebook-id NOTEBOOK_ID --format blog_post

Artifacts

# List all generated artifacts (audio, reports, etc.)
python scripts/nlm.py artifacts --notebook-id NOTEBOOK_ID

Slide Generation

Before generating slides — ask the user for their brand colors

Agent instruction: Before running any slide generation, always ask the user:

"What are your brand colors? I need:

Primary color (main accent — e.g. #FF6B2C)

Secondary color (supporting — e.g. #FFBE2E)

Background color (e.g. #000000 for black, #1A1A2E for dark navy)

Text color (e.g. #F5F5F5 for off-white)

If you don't have hex codes, describe them and I'll pick the closest match."

Once you have the colors, substitute them into the slide prompt template below.

Slide Prompt Template (Blackboard Style)

Use this as a Focus Prompt inside NotebookLM (Studio → Focus). The agent fills in the placeholders before sending.

Create a [NUMBER]-slide presenter deck for "[VIDEO TITLE]."

Design: Dark blackboard on [BACKGROUND_COLOR]. TITLES: bold slab serif (Rockwell or Roboto Slab), stamped/typeset, color [PRIMARY_COLOR]. Body text: handwritten chalk-style, color [TEXT_COLOR]. Each slide features one key glossy 3D object that floats above the chalk background. Chalk dashed connector lines link 3D elements to surrounding sketch labels. Every slide: dashed border in [PRIMARY_COLOR] + [CORNER_ICON] icon top-right corner. [PRIMARY_COLOR] PRIMARY, [SECONDARY_COLOR] SECONDARY, [TEXT_COLOR] text, [BACKGROUND_COLOR] background.

Slide 1: [SLIDE_1_DESCRIPTION]
Slide 2: [SLIDE_2_DESCRIPTION]
...

CRITICAL: Every slide — slab serif TITLE in [PRIMARY_COLOR] at top, dashed [PRIMARY_COLOR] border, [CORNER_ICON] icon top-right. Chalk body text. [BACKGROUND_COLOR] background. One 3D object per slide. [PRIMARY_COLOR] PRIMARY.

Placeholders to fill:

Placeholder	What to fill
`[NUMBER]`	How many slides
`[VIDEO TITLE]`	The topic/title of the content
`[PRIMARY_COLOR]`	User's primary brand color (hex)
`[SECONDARY_COLOR]`	User's secondary brand color (hex)
`[BACKGROUND_COLOR]`	Background color (hex)
`[TEXT_COLOR]`	Text color (hex)
`[CORNER_ICON]`	Icon that appears on every slide (e.g. lightning bolt, star, logo description)
`[SLIDE_N_DESCRIPTION]`	Per-slide: what the slide covers, what 3D object appears, what labels surround it

Per-slide description format

Each slide description should be ~150 characters. Be specific:

Slide 2: [TOPIC]. 3D [OBJECT] floating [POSITION]. Chalk labels: "[LABEL 1]," "[LABEL 2]," "[LABEL 3]." [PRIMARY_COLOR] dashed arrow pointing to [KEY ELEMENT].

3D object suggestions by topic:

Data / knowledge → glowing books or filing cabinet
Actions / automation → terminal / monitor
Time / scheduling → clock or calendar
Mobile / notifications → smartphone
AI / thinking → brain model
Cloud / remote → glowing cloud
Steps / process → numbered podium

How to submit the focus prompt

Once filled in, run:

python scripts/nlm.py generate-report --notebook-id NOTEBOOK_ID --format custom --prompt "YOUR FILLED PROMPT HERE"

Or paste it directly into NotebookLM → Studio → Focus prompt.

Note: NotebookLM has a ~5,000 character limit on focus prompts. If your prompt is too long, shorten the per-slide descriptions.

Keeping Auth Alive (Optional)

Google session cookies expire after 7–30 days. There are two approaches:

Option A — Simple (re-login when it breaks)

When you get an auth error, just run:

python scripts/nlm.py login

Takes 30 seconds. Fine for occasional use.

Option B — Headless Auto-Refresh (recommended for agent setups)

scripts/refresh_auth.py silently refreshes your cookies using the persistent browser profile — no browser window, no manual steps. Run it every few days before cookies expire.

python scripts/refresh_auth.py          # Refresh cookies
python scripts/refresh_auth.py --check  # Just check if they're valid

To automate this, add a scheduled task or cron job:

# Cron example — refresh every 3 days at 5:30am
30 5 */3 * * python /path/to/scripts/refresh_auth.py

For Claude Code agent setups, add this to your agent's cron-registry.json:

{
  "id": "notebooklm-auth-refresh",
  "name": "NotebookLM cookie refresh (every 3 days)",
  "cron": "30 5 */3 * *",
  "prompt": "Run: python /path/to/scripts/refresh_auth.py — if it fails, alert the user to run notebooklm login manually.",
  "enabled": true
}

How it works: Your first login saves a full browser profile to ~/.notebooklm/browser_profile/. Google keeps this session alive much longer than raw cookies because the browser handles token rotation automatically. The refresh script uses this profile headlessly to export fresh cookies — no human interaction needed.

Troubleshooting

Auth error / "Authentication expired" Run python scripts/refresh_auth.py first. If that fails, run python scripts/nlm.py login to do a full re-login.

nlm command not found in terminal Use python scripts/nlm.py instead. This is a Windows PATH issue with certain Python installations (common with Windows Store Python). The scripts work fine when called with python directly.

"No notebook specified" Either pass --notebook-id YOUR_ID or set an active notebook first:

python scripts/nlm.py library-activate your-notebook-name

Slide generation times out NotebookLM can take 3–6 minutes to generate custom reports. If the command times out, check your artifacts list — the generation likely completed on NotebookLM's side:

python scripts/nlm.py artifacts --notebook-id NOTEBOOK_ID

Playwright / Chromium not found Run playwright install chromium to install the browser.

Windows: "UnicodeEncodeError" on output Set PYTHONUTF8=1 in your environment, or run $env:PYTHONUTF8="1" in PowerShell before running the script.

Credits

notebooklm-py by Teng Lin — the library that makes this possible
This skill by RoboNuggets — CLI wrapper, headless refresh, slide generation workflow