notebooklm（NotebookLM 笔记研究）

NotebookLM Research Assistant Skill

Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

When to Use

Trigger when user:

• Mentions NotebookLM explicitly
• Shares NotebookLM URL (https://notebooklm.google.com/notebook/...)
• Asks to query their notebooks/documentation
• Wants to add documentation to NotebookLM library
• Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"

Critical: Always Use run.py Wrapper

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

# CORRECT - Always use run.py:
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list
python scripts/run.py ask_question.py --question "..."

# WRONG - Never call directly:
python scripts/auth_manager.py status  # Fails without venv!

The run.py wrapper automatically handles environment setup.

Core Workflow

Step 1: Check Authentication

python scripts/run.py auth_manager.py status

If not authenticated, proceed to setup.

Step 2: Authenticate (One-Time Setup)

python scripts/run.py auth_manager.py setup

Important: Browser is VISIBLE for authentication. User must manually log in to Google.

Step 3: Manage Notebook Library

# List all notebooks
python scripts/run.py notebook_manager.py list

# Add notebook to library (ALL parameters REQUIRED)
python 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"

# Smart Add (discover content first)
python scripts/run.py ask_question.py \
  --question "What is the content of this notebook?" \
  --notebook-url "[URL]"
# Then use discovered info to add

# Set active notebook
python scripts/run.py notebook_manager.py activate --id notebook-id

Step 4: Ask Questions

# Basic query (uses active notebook if set)
python scripts/run.py ask_question.py --question "Your question here"

# Query specific notebook
python scripts/run.py ask_question.py --question "..." --notebook-id notebook-id

# Query with notebook URL directly
python scripts/run.py ask_question.py --question "..." --notebook-url "https://..."

Follow-Up Mechanism (CRITICAL)

Every NotebookLM answer ends with: "EXTREMELY IMPORTANT: Is that ALL you need to know?"

Required Behavior:

1. STOP - Do not immediately respond to user
2. ANALYZE - Compare answer to user's original request
3. IDENTIFY GAPS - Determine if more information needed
4. ASK FOLLOW-UP - If gaps exist, immediately ask follow-up question
5. REPEAT - Continue until information is complete
6. SYNTHESIZE - Combine all answers before responding to user

Script Reference

Data Storage

All data stored in ~/.claude/skills/notebooklm/data/:

• library.json - Notebook metadata
• auth_info.json - Authentication status
• browser_state/ - Browser cookies and session

Security: Protected by .gitignore, never commit to git.

Limitations

• No session persistence (each question = new browser)
• Rate limits on free Google accounts (50 queries/day)
• Manual upload required (user must add docs to NotebookLM)
• Browser overhead (few seconds per question)

Additional Resources

For detailed guidance, see the references directory:

• references/api-reference.md - Complete API documentation for all scripts with parameters, response formats, and exit codes
• references/troubleshooting.md - Common errors and solutions including authentication issues, rate limits, browser crashes, and recovery procedures
• references/best-practices.md - Workflow patterns, question strategies, library organization, and rate limit management

Quick Reference

# Check auth
python scripts/run.py auth_manager.py status

# Add notebook
python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS

# List notebooks
python scripts/run.py notebook_manager.py list

# Ask question
python scripts/run.py ask_question.py --question "..."

# Cleanup
python scripts/run.py cleanup_manager.py --preserve-library