Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for source-grounded research context. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for source-grounded research context. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
Blog NotebookLM: Source-Grounded Research from Your Documents
Query Google NotebookLM notebooks directly from Claude Code for citation-backed
answers from Gemini. Each question opens a headless browser session, retrieves
the answer from your uploaded documents, and closes. Responses are
source-grounded model answers, not proof of truth: uploaded documents may be
primary or secondary, and the answer can still omit context.
Answers provide usable provenance only when the returned citation identifies a
verifiable underlying source. Record a stable source URL and a publication,
study-period, or retrieval date when that detail affects verification or
interpretation. Use the underlying source title as the inline citation. Do not
cite the private NotebookLM URL as the bibliography entry for public content.
Quick Reference
Command
What it does
/blog notebooklm ask <question>
Query a notebook for source-grounded answers
/blog notebooklm discover <url>
Smart-discover notebook content before cataloging
/blog notebooklm library list
List all notebooks in library
/blog notebooklm library add <url>
Add a notebook to library
/blog notebooklm library search <query>
Search notebooks by keyword
/blog notebooklm library remove <id>
Remove a notebook from library
/blog notebooklm setup
One-time Google authentication (browser visible)
/blog notebooklm status
Check authentication status
/blog notebooklm cleanup
Clean browser state (preserves library)
Prerequisites
Google account with NotebookLM access
Python 3.11+ (venv managed automatically by run.py)
Google Chrome (installed automatically on first run via Patchright)
One-time authentication setup (interactive Google login in visible browser)
Use the run.py Wrapper
Call scripts only through the run.py wrapper: python3 scripts/run.py [script]:
# CORRECT:
python3 scripts/run.py auth_manager.py status
python3 scripts/run.py ask_question.py --question "..."# Do not call files under scripts/ directly. The wrapper owns venv setup.
The run.py wrapper automatically creates .venv, installs dependencies,
sets up Chrome, and executes the target script.
Auth Check (Gate Pattern)
Before any query operation, check authentication:
python3 scripts/run.py auth_manager.py status
If authenticated: proceed with the query
If not authenticated: inform user and guide to setup:
"NotebookLM requires Google login. Run /blog notebooklm setup to authenticate."
When called internally (from blog-write or blog-researcher): return silently
with no error if not authenticated. Never block the writing workflow.
Setup Workflow
For /blog notebooklm setup:
# Opens a visible browser for manual Google login (one-time)
python3 scripts/run.py auth_manager.py setup
Tell the user: "A browser window will open. Please log in to your Google account."
Authentication persists via browser profile + cookie injection (hybrid approach).
Other auth commands:
python3 scripts/run.py auth_manager.py status # Check auth
python3 scripts/run.py auth_manager.py reauth # Re-authenticate
python3 scripts/run.py auth_manager.py clear # Clear all auth data
Query Workflow
For /blog notebooklm ask <question>:
Step 1: Check Auth
Run auth check (see gate pattern above). If not authenticated, guide to setup.
Step 2: Resolve Notebook
Determine which notebook to query:
If --notebook-url provided: validate it is a NotebookLM notebook URL, then use it
If --notebook-id provided: look up in library
If neither: use active notebook from library
If no active notebook: show library and ask user to select
Step 3: Ask the Question
# Basic query (uses active notebook)
python3 scripts/run.py ask_question.py --question "Your question here"# Query specific notebook by ID
python3 scripts/run.py ask_question.py --question "..." --notebook-id notebook-id
# Query by URL directly
python3 scripts/run.py ask_question.py --question "..." --notebook-url "https://..."# JSON output (for internal/programmatic use)
python3 scripts/run.py ask_question.py --question "..." --json
# Show browser for debugging
python3 scripts/run.py ask_question.py --question "..." --show-browser
Step 4: Analyze and Follow Up
Every response ends with a follow-up prompt. Required behavior:
STOP: do not immediately respond to the user
ANALYZE: compare the answer to the user's original request
IDENTIFY GAPS: determine if more information is needed
ASK FOLLOW-UP: if gaps exist, immediately ask a follow-up question
REPEAT: continue until information is complete
SYNTHESIZE: combine all answers before responding to the user
Smart Discovery Workflow
For /blog notebooklm discover <url>:
When adding a notebook without knowing its content, query it first:
# Step 1: Discover content
python3 scripts/run.py ask_question.py \
--question "What is the content of this notebook? What topics are covered? Provide a complete overview briefly and concisely" \
--notebook-url "<URL>"# Step 2: Add with discovered metadata
python3 scripts/run.py notebook_manager.py add \
--url "<URL>" \
--name "<Based on content>" \
--description "<Based on content>" \
--topics "<Extracted topics>"
Do not guess descriptions; discover or ask the user.
Library Management
# List all notebooks
python3 scripts/run.py notebook_manager.py list
# Add notebook (all params required -- discover or ask user!)
python3 scripts/run.py notebook_manager.py add \
--url "https://notebooklm.google.com/notebook/..." \
--name "Descriptive Name" \
--description "What this notebook contains" \
--topics "topic1,topic2,topic3"# Search by keyword
python3 scripts/run.py notebook_manager.py search --query "keyword"# Set active notebook
python3 scripts/run.py notebook_manager.py activate --id notebook-id
# Remove notebook
python3 scripts/run.py notebook_manager.py remove --id notebook-id
# Library statistics
python3 scripts/run.py notebook_manager.py stats
Internal API (for blog-write / blog-researcher)
When invoked as a Task subagent from blog-write or blog-researcher:
Input (provided by calling skill):
question: Research question relevant to the blog topic
notebook_id or notebook_url: Which notebook to query
Check auth status: if not authenticated, return empty result silently
Query the notebook with the research question
Parse and return structured response
Output (returned to calling skill):
### NotebookLM Research-**Source:** [Notebook name]
-**Question:** [What was asked]
-**Answer:** [Source-grounded response from user's documents]
-**Underlying Source:** [Public source URL or document identifier]
-**Underlying Source Date:** [Publication date or retrieval date]
-**Source Quality:** [Tier 1-3 after classifying the underlying document]
Graceful fallback: If auth is missing or query fails, return immediately
with no error. The calling workflow continues with WebSearch-based research.
Never block blog-write or blog-rewrite because NotebookLM is unavailable.
Data Storage
All data stored inside the skill directory:
data/library.json: Notebook metadata and library
data/auth_info.json: Authentication status
data/browser_state/: Chrome profile with cookies
Security: All data directories are gitignored. Never commit auth or browser state.
Browser lifecycle and authenticated-context isolation are centralized in
scripts/browser_session.py. Command scripts must use that helper instead of
opening an additional persistent profile or copying cookies into another file.
Error Handling
Error
Resolution
Not authenticated
Run /blog notebooklm setup
ModuleNotFoundError
Always use run.py wrapper
Browser crash
cleanup_manager.py --confirm --preserve-library, then re-auth
Rate limit (50/day)
Wait until midnight PST or switch Google account
Notebook not found
Check with notebook_manager.py list
Query timeout (120s)
Retry with simpler question or --show-browser to debug
MCP unavailable (internal)
Return silently: writing workflow uses WebSearch
Limitations
No session persistence (each question = new browser session)
Rate limits on free Google accounts (50 queries/day)
Manual upload required (user must add docs to NotebookLM web UI)
Browser overhead (few seconds per question for launch + teardown)
Local Claude Code only (not available in web UI)
Reference Documentation
Load on-demand: do NOT load all at startup:
references/commands.md: Full CLI commands, parameters, and workflow patterns
