blog-notebooklm

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 exclusively from your uploaded documents, and closes. Responses are Tier 1 quality (user's own primary sources): zero hallucination risk. Answers satisfy the FLOW evidence triple: use the returned source title as the inline citation and the notebook URL plus retrieval date as the bibliography entry. This is the highest-confidence path to meeting the "verified source" bar that FLOW requires before any statistic goes public.

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)

Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS use python3 scripts/run.py [script]:

# CORRECT:
python3 scripts/run.py auth_manager.py status
python3 scripts/run.py ask_question.py --question "..."

# WRONG -- fails without venv:
python3 scripts/auth_manager.py status

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: use directly
• 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:

1. STOP: do not immediately respond to the user
2. ANALYZE: compare the answer to the user's original request
3. IDENTIFY GAPS: determine if more information is needed
4. ASK FOLLOW-UP: if gaps exist, immediately ask a follow-up question
5. REPEAT: continue until information is complete
6. 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>"

NEVER guess or use generic descriptions. Always 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
• context: "internal" (signals graceful fallback mode)

Process:

1. Check auth status: if not authenticated, return empty result silently
2. Query the notebook with the research question
3. 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]
- **Source Quality:** Tier 1 (user-uploaded primary documents)

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:

• scripts/data/library.json: Notebook metadata and library
• scripts/data/auth_info.json: Authentication status
• scripts/data/browser_state/: Chrome profile with cookies

Security: All data directories are gitignored. Never commit auth or browser state.

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
• references/troubleshooting.md: Error solutions, recovery procedures, debugging