Install
$ agentstack add skill-medy-gribkov-arcana-notebooklm-research ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
NotebookLM Research Automation
Turn Claude Code into a research agent by automating Google NotebookLM. NotebookLM has no public API. This skill uses Playwright to control a real Chrome browser via CDP (Chrome DevTools Protocol). All commands output JSON to stdout.
Prerequisites
One-time setup (2 minutes):
pip install playwright && playwright install chromium
python scripts/setup_chrome.py
# Log in to Google in the Chrome window that opens
python scripts/notebooklm_client.py status # Verify: "authenticated": true
See references/authentication.md for detailed setup and troubleshooting.
Session Management
BAD: Launching Chromium directly from Playwright. Gets detected as a bot, cannot authenticate with Google.
browser = await playwright.chromium.launch()
page = await browser.new_page()
await page.goto("https://notebooklm.google.com") # Blocked or login fails
GOOD: Connecting to a real Chrome instance via CDP with a persistent profile.
browser = await playwright.chromium.connect_over_cdp("http://localhost:9222")
context = browser.contexts[0] # Reuse authenticated session
page = context.pages[0] # Already on NotebookLM
Interaction Speed
BAD: Instant typing and clicks. Triggers anti-bot detection, actions may be ignored.
await page.fill("textarea", "full text instantly")
await page.click("button")
GOOD: Human-like delays. Random 25-75ms per character, 100-300ms pre-click pause.
for char in question:
await element.type(char, delay=random.uniform(25, 75))
await asyncio.sleep(random.uniform(0.1, 0.3))
await page.click("button")
Command Reference
Always verify connection first with status. All commands return JSON.
| Command | Example | Purpose | |---------|---------|---------| | status | python scripts/notebooklm_client.py status | Check Chrome + auth | | list | python scripts/notebooklm_client.py list | List all notebooks | | create | python scripts/notebooklm_client.py create "Topic" | Create notebook | | add-source | ...add-source --notebook "Topic" --url "https://..." | Add URL source | | add-source | ...add-source --notebook "Topic" --file "/path/to.pdf" | Add file source | | add-source | ...add-source --notebook "Topic" --youtube "https://..." | Add YouTube source | | add-source | ...add-source --notebook "Topic" --text "raw content" | Add text source | | query | ...query --notebook "Topic" --question "Key findings?" | Query with citations | | generate | ...generate --notebook "Topic" --type slides | Generate artifact | | generate | ...generate --notebook "Topic" --type infographic --instructions "Focus on stats" | Generate with instructions |
Artifact types: slides, infographic, quiz, flashcards, report, table, mindmap, video
Research Workflow
Follow this sequence for reliable results:
- Check connection:
statuscommand. If not connected, runsetup_chrome.py. - Create notebook:
create "Research Topic Name" - Add sources: Run
add-sourcefor each URL, file, YouTube link, or text block. Add 2-3+ sources for best results. - Wait for processing: Sources need 10-60 seconds to index. The script waits automatically, but for large PDFs allow extra time.
- Query sources:
query --notebook "Topic" --question "Your question". Returns answer with citations. - Generate deliverables:
generate --notebook "Topic" --type slides. Output saved to./notebooklm-output/.
Output Format
Success:
{"status": "success", "answer": "...", "citations": ["Source 1, p.3", "Source 2, sec.4"]}
Error:
{"status": "error", "error": "Chrome not reachable", "suggestion": "Run: python setup_chrome.py"}
Generated files are saved to ./notebooklm-output/ (override with NOTEBOOKLM_OUTPUT_DIR env var).
Troubleshooting
| Problem | Fix | |---------|-----| | "Cannot connect to Chrome" | Run python scripts/setup_chrome.py | | "Not authenticated" | Log in to Google in the Chrome window | | Source upload hangs | Large file. Wait 2 minutes, then check NotebookLM UI | | Generation fails | Retry once. If persistent, check NotebookLM UI for errors | | Selectors not matching | NotebookLM UI may have changed. Update selectors in notebooklm_client.py |
See references/deliverables.md for artifact types, generation times, and examples. See references/authentication.md for Chrome setup, session management, and security.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: medy-gribkov
- Source: medy-gribkov/arcana
- License: Apache-2.0
- Homepage: https://www.npmjs.com/package/@sporesec/arcana
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.