<!-- NavAIgate NotebookLM Skill — navaigate.dev -->

NavAIgate NotebookLM Integration

Part of the NavAIgate AI toolkit. This skill connects Claude Code to Google NotebookLM, giving you persistent memory, free content generation, and deep research capabilities.

Complete programmatic access to Google NotebookLM—including capabilities not exposed in the web UI. Create notebooks, add sources (URLs, YouTube, PDFs, audio, video, images), chat with content, generate all artifact types, and download results in multiple formats.

Step 0: Setup (Run Automatically on First Use)

When this skill is triggered and notebooklm is not yet installed or authenticated, complete setup first.

Pre-flight: Check Python Version

notebooklm-py requires Python 3.10+. Check the available version before installing:

python3 --version

If Python is below 3.10 (e.g. 3.9.x which is the macOS default), install a compatible version:

macOS (Homebrew):

brew install python@3.12

Then use /opt/homebrew/bin/python3.12 (Apple Silicon) or /usr/local/bin/python3.12 (Intel) for the venv below.

Linux (apt):

sudo apt update && sudo apt install -y python3.12 python3.12-venv

Install the CLI

Always use a virtual environment to avoid "externally-managed-environment" errors and PATH issues.

Determine which Python to use — if the system python3 is 3.10+, use it directly. Otherwise use the one you just installed (e.g. python3.12):

# Set PYTHON to the correct binary (adjust if needed)
PYTHON=$(command -v python3.12 2>/dev/null || command -v python3.11 2>/dev/null || command -v python3.10 2>/dev/null || command -v python3)

# Verify it's 3.10+
$PYTHON -c "import sys; assert sys.version_info >= (3,10), f'Python {sys.version} is too old — need 3.10+'; print(f'Using Python {sys.version}')"

# Create venv and install
$PYTHON -m venv ~/.notebooklm-venv
source ~/.notebooklm-venv/bin/activate
pip install "notebooklm-py[browser]"
playwright install chromium

Then symlink so it's always on PATH:

mkdir -p ~/bin
ln -sf ~/.notebooklm-venv/bin/notebooklm ~/bin/notebooklm
export PATH="$HOME/bin:$PATH"

Verify the CLI works:

notebooklm --help

Authenticate

IMPORTANT: The built-in notebooklm login command requires interactive terminal input (pressing Enter after sign-in). Claude Code's bash tool does NOT support interactive input, so notebooklm login will fail — the browser opens and closes instantly. Instead, use this custom login script.

Tell the user:

I'm going to open a browser window — just sign into your Google account and navigate to notebooklm.google.com. Take your time, I'll wait for you to confirm before closing it.

Then write and run this login script:

cat > /tmp/nlm_login.py << 'PYEOF'
import json, os, 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)

print("Opening browser for Google login...")
print("Sign in to Google and navigate to notebooklm.google.com")

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("Browser is open. Waiting for save signal...")
    while not SIGNAL_FILE.exists():
        time.sleep(1)

    print("Save signal received! Capturing session...")
    storage = browser.storage_state()
    with open(STORAGE_PATH, "w") as f:
        json.dump(storage, f)

    cookie_names = [c["name"] for c in storage.get("cookies", [])]
    print(f"Saved {len(cookie_names)} cookies: {cookie_names}")
    browser.close()

SIGNAL_FILE.unlink(missing_ok=True)
print(f"Authentication saved to: {STORAGE_PATH}")
PYEOF

# Run the login script in the background
source ~/.notebooklm-venv/bin/activate
python3 /tmp/nlm_login.py > /tmp/nlm_login_output.txt 2>&1 &
echo "Login started (PID=$!). Browser should open in a few seconds..."

Wait ~10 seconds for the browser to open, then ask the user if they can see the browser and are signed in.

Once the user confirms they are on the NotebookLM homepage, save the session:

touch /tmp/nlm_save_signal
sleep 8
cat /tmp/nlm_login_output.txt

Then verify authentication:

export PATH="$HOME/bin:$PATH"
notebooklm auth check
notebooklm list

If auth passes (SID cookie present), confirm to the user that NotebookLM is set up and ready. Clean up the temp script:

rm -f /tmp/nlm_login.py /tmp/nlm_login_output.txt /tmp/nlm_save_signal

If auth fails (SID cookie missing), the user may not have fully signed in. Delete the browser profile and retry:

rm -rf ~/.notebooklm/browser_profile ~/.notebooklm/storage_state.json

Then run the login script again from the top.

---

Adding NotebookLM to Co-work

When the user asks to "add this to Co-work", "use this in Co-work", or "make this work in Co-work":

Step 1: Check auth exists

cat ~/.notebooklm/storage_state.json > /dev/null 2>&1

If it doesn't exist, run the install and authenticate steps above first.

Step 2: Generate the Co-work skill file

Read the contents of this skill file you are currently using. Then read the contents of ~/.notebooklm/storage_state.json.

IMPORTANT — Strip cookies before embedding: Co-work is sandboxed and can't read local files, so cookies must be inlined. But the full storage_state.json contains duplicate cookies across multiple Google domains (.google.ae, .google.co.uk, .youtube.com, etc.) that are NOT needed. Strip them to save ~55% of tokens.

Run this to generate the minimal auth JSON:

python3 << 'PYEOF'
import json

with open("$HOME/.notebooklm/storage_state.json") as f:
    data = json.load(f)

# Only these domains are needed for NotebookLM auth
essential_domains = {".google.com", "notebooklm.google.com", "accounts.google.com"}
# Skip analytics/tracking cookies
skip_names = {"_gcl_au", "_ga", "_ga_W0LDH41ZCB", "OTZ", "ACCOUNT_CHOOSER"}

stripped = {
    "cookies": [c for c in data["cookies"] if c["domain"] in essential_domains and c["name"] not in skip_names],
    "origins": [o for o in data.get("origins", []) if "notebooklm" in o.get("origin", "")]
}
print(json.dumps(stripped, separators=(',',':')))
PYEOF

Create a MODIFIED copy of this skill file where you replace the entire "Step 0: Setup" section and "Adding NotebookLM to Co-work" section with this:

## Auto-Authentication (Co-work)

This skill runs in a sandboxed Co-work environment. Before running ANY notebooklm command, run these setup commands:

\`\`\`bash
pip install notebooklm-py 2>/dev/null
export NOTEBOOKLM_AUTH_JSON='<STRIPPED MINIMAL AUTH JSON FROM ABOVE>'
\`\`\`

Then verify:
\`\`\`bash
notebooklm auth check
notebooklm list
\`\`\`

If auth fails, the cookies may have expired. Ask the user to re-run `notebooklm login` in Claude Code and regenerate this file.

Step 3: Save and instruct

Save the file to the user's Desktop as NotebookLMSkill-Cowork.md.

Tell the user:

Done! I've saved the Co-work skill to your Desktop as NotebookLMSkill-Cowork.md. Upload it as a skill in Claude Co-work and you'll have full NotebookLM access there too.

I've stripped the cookies down to only the 21 essential ones (~1,400 tokens instead of ~3,100). When they expire, just come back to Claude Code and say "regenerate my Co-work NotebookLM skill" and I'll make a fresh one.

---

When This Skill Activates

Explicit: User says "/navaigate-notebooklm", "use notebooklm", "install notebooklm", or mentions the tool by name

Intent detection: Recognize requests like:

• "Create a podcast about [topic]"
• "Summarize these URLs/documents"
• "Generate a quiz from my research"
• "Turn this into an audio overview"
• "Create flashcards for studying"
• "Generate a video explainer"
• "Make an infographic"
• "Create a mind map of the concepts"
• "Download the quiz as markdown"
• "Add these sources to NotebookLM"
• "Add this to Co-work" / "Make this work in Co-work"

Autonomy Rules

Run automatically (no confirmation):

• notebooklm status - check context
• notebooklm auth check - diagnose auth issues
• notebooklm list - list notebooks
• notebooklm source list - list sources
• notebooklm artifact list - list artifacts
• notebooklm language list - list supported languages
• notebooklm language get - get current language
• notebooklm language set - set language (global setting)
• notebooklm artifact wait - wait for artifact completion
• notebooklm source wait - wait for source processing
• notebooklm research status - check research status
• notebooklm research wait - wait for research
• notebooklm use <id> - set context
• notebooklm create - create notebook
• notebooklm ask "..." - chat queries (without --save-as-note)
• notebooklm history - display conversation history (read-only)
• notebooklm source add - add sources

Ask before running:

• notebooklm delete - destructive
• notebooklm generate * - long-running, may fail
• notebooklm download * - writes to filesystem
• notebooklm ask "..." --save-as-note - writes a note
• notebooklm history --save - writes a note

Quick Reference

Generation Types

All generate commands support:

• -s, --source to use specific source(s) instead of all sources
• --language to set output language (defaults to 'en')
• --json for machine-readable output
• --retry N to automatically retry on rate limits

Common Workflows

Research to Podcast

1. notebooklm create "Research: [topic]"
2. notebooklm source add for each URL/document
3. Wait for sources: notebooklm source list --json until all status=READY
4. notebooklm generate audio "Focus on [specific angle]"
5. Check notebooklm artifact list for status
6. notebooklm download audio ./podcast.mp3 when complete

Document Analysis

1. notebooklm create "Analysis: [project]"
2. notebooklm source add ./doc.pdf (or URLs)
3. notebooklm ask "Summarize the key points"
4. Continue chatting as needed

Output Formats (--json)

// notebooklm list --json
{"notebooks": [{"id": "...", "title": "...", "created_at": "..."}]}

// notebooklm source list --json
{"sources": [{"id": "...", "title": "...", "status": "ready|processing|error"}]}

// notebooklm artifact list --json
{"artifacts": [{"id": "...", "title": "...", "type": "Audio Overview", "status": "in_progress|pending|completed|unknown"}]}

Error Handling

Known Limitations

• Audio, video, quiz, flashcard, infographic, and slide deck generation may fail due to Google rate limits
• Generation times: audio 10-20 min, video 15-45 min, quiz/flashcards 5-15 min
• This is an unofficial API — Google can change things without warning