Agent-Skills.md

Agent Skills: NotebookLM Research Assistant Skill

Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.

UncategorizedID: b-open-io/prompts/notebooklm

Repository

b-open-io/prompts
b-open-io
62

Install this agent skill to your local

pnpm dlx add-skill https://github.com/b-open-io/prompts/tree/HEAD/skills/notebooklm

Skill Files

Browse the full folder contents for notebooklm.

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skills/notebooklm/SKILL.md

Skill Metadata

Name
notebooklm
Description
This skill should be used when the user wants to query Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Provides browser automation, library management, and persistent auth. Drastically reduced hallucinations through document-only responses.

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

| Script | Purpose | |--------|---------| | auth_manager.py | Authentication setup and status | | notebook_manager.py | Library management (add, list, search, activate, remove) | | ask_question.py | Query interface | | cleanup_manager.py | Data cleanup and maintenance |

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