notebooklm

A rule set for connecting coding sessions to Google NotebookLM notebooks, including source syncing and creating or querying notebook content.

In plain words
What is it for?
Use it to initialize a project notebook, synchronize sources, ask questions about them, or create NotebookLM artifacts such as podcasts.
Why use it?
It keeps project information in a central notebook so it can be reused and consulted across sessions.

Cursor rule

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/ibaifernandez/notebooklm-skill/notebooklm
Clone the repo
git clone --depth 1 https://github.com/ibaifernandez/notebooklm-skill
Per session 49 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,241 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00049 $0.01241
Opus 5 $0.00024 $0.00620
Sonnet 5 $0.00010 $0.00248
Haiku 4.5 $0.00005 $0.00124

Measured 2d ago against content hash ce65627105ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

adapters/cursor/notebooklm.mdc · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NotebookLM Integration

Programmatic access to Google NotebookLM from Cursor. Supports per-project notebooks, automatic source syncing, and all artifact types.


Setup (run once, on first use)

Check if the CLI is already installed:

notebooklm --version 2>/dev/null || ~/.notebooklm-venv/bin/notebooklm --version 2>/dev/null

If not found, run the installer from the repo root:

chmod +x install.sh && ./install.sh

Or manually:

# Recommended: use uv (fast, no ensurepip issues)
uv venv ~/.notebooklm-venv --python python3.12
uv pip install "notebooklm-py[browser]" --python ~/.notebooklm-venv/bin/python
~/.notebooklm-venv/bin/playwright install chromium
mkdir -p ~/bin
ln -sf ~/.notebooklm-venv/bin/notebooklm ~/bin/notebooklm
export PATH="$HOME/bin:$PATH"

Authentication

Run this script to authenticate with Google. It opens a real browser window — sign in and navigate to notebooklm.google.com, then run the signal command:

cat > /tmp/nlm_login.py << 'PYEOF'
import json, time
from pathlib import Path
from playwright.sync_api import sync_playwright

STORAGE_PATH = Path.home() / ".notebooklm" / "storage_state.json"
PROFILE_PATH = Path.home() / ".notebooklm" / "browser_profile"
SIGNAL_FILE  = Path("/tmp/nlm_save_signal")

SIGNAL_FILE.unlink(missing_ok=True)
STORAGE_PATH.parent.mkdir(parents=True, exist_ok=True)

with sync_playwright() as p:
    browser = p.chromium.launch_persistent_context(
        user_data_dir=str(PROFILE_PATH),
        headless=False,
        args=["--disable-blink-features=AutomationControlled"],
    )
    page = browser.pages[0] if browser.pages else browser.new_page()
    page.goto("https://notebooklm.google.com/")
    print("Sign in, then run: touch /tmp/nlm_save_signal")
    while not SIGNAL_FILE.exists():
        time.sleep(1)
    storage = browser.storage_state()
    STORAGE_PATH.write_text(json.dumps(storage))
    print(f"Saved {len(storage.get('cookies', []))} cookies to {STORAGE_PATH}")
    browser.close()
SIGNAL_FILE.unlink(missing_ok=True)
PYEOF

source ~/.notebooklm-venv/bin/activate && python3 /tmp/nlm_login.py &

Read the full file on GitHub · 176 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 176 lines · 49 tokens per session scan A ce65627105ef

Subscribe to this mod's changes

notebooklm is a cursor rule published in the GitHub repository ibaifernandez/notebooklm-skill (5 stars, last pushed 4mo ago), licensed MIT. It adds 49 tokens to every session and 1,241 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.