notebooklm

leegonzales's avatarfrom leegonzales

Query Google NotebookLM for source-grounded, citation-backed answers from uploaded documents. Reduces hallucinations through Gemini's document-only responses. Browser automation with library management and persistent authentication.

⭐8stars🔀2forks📁View on GitHub🕐Updated Dec 23, 2025
[Knowledge Base]

When & Why to Use This Skill

This Claude skill integrates with Google NotebookLM to provide source-grounded, citation-backed answers directly from your uploaded documents. By leveraging Gemini's document-only response capabilities via browser automation, it significantly reduces AI hallucinations and ensures every claim is traceable to your specific library of information. It features automated library management, persistent authentication, and a smart follow-up mechanism to ensure comprehensive information retrieval.

Use Cases

  • Academic and Professional Research: Quickly extract and cite specific evidence from a large collection of research papers or industry reports uploaded to NotebookLM.
  • Fact-Checking and Verification: Cross-reference information against a trusted internal knowledge base to ensure accuracy and eliminate fabricated details in complex projects.
  • Technical Documentation Querying: Navigate complex technical manuals or project documentation to find specific instructions or configurations with direct source citations.
  • Automated Knowledge Synthesis: Manage a centralized library of notebooks and use smart discovery to summarize key themes across multiple documents for better decision-making.
namenotebooklm
descriptionQuery Google NotebookLM for source-grounded, citation-backed answers from uploaded documents. Reduces hallucinations through Gemini's document-only responses. Browser automation with library management and persistent authentication.

NotebookLM Skill

Query Google NotebookLM notebooks for source-grounded answers exclusively from your uploaded documentation, drastically reducing hallucinations.

When to Use

Trigger when user:

  • Mentions NotebookLM or shares URL (https://notebooklm.google.com/notebook/...)
  • Asks to query notebooks/documentation ("ask my NotebookLM", "check my docs")
  • Wants citations from specific sources
  • Needs to add notebooks to library

Critical: Always Use run.py Wrapper

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

# ✅ CORRECT
python scripts/run.py auth_manager.py status
python scripts/run.py ask_question.py --question "..."

# ❌ WRONG - Fails without venv!
python scripts/auth_manager.py status

The run.py wrapper auto-creates .venv, installs dependencies, and executes properly.

Core Workflow

1. Check Authentication

python scripts/run.py auth_manager.py status

2. Authenticate (One-Time, Browser Visible)

python scripts/run.py auth_manager.py setup

Tell user: "A browser window will open for Google login"

3. Add Notebooks (Smart Discovery Recommended)

Smart Add: Query first to discover content:

# Step 1: Discover content
python scripts/run.py ask_question.py --question "What topics does this notebook cover?" --notebook-url "[URL]"

# Step 2: Add with discovered metadata
python scripts/run.py notebook_manager.py add --url "[URL]" --name "[Based on content]" --description "[From discovery]" --topics "[From discovery]"

Manual Add: Only if user provides all details:

python scripts/run.py notebook_manager.py add \
  --url "https://notebooklm.google.com/notebook/..." \
  --name "Descriptive Name" \
  --description "What this contains" \  # REQUIRED
  --topics "topic1,topic2,topic3"      # REQUIRED

NEVER guess metadata! Use Smart Add if details unknown.

4. Ask Questions

# Uses active notebook
python scripts/run.py ask_question.py --question "Your question"

# Specific notebook
python scripts/run.py ask_question.py --question "..." --notebook-id ID

# Direct URL
python scripts/run.py ask_question.py --question "..." --notebook-url URL

Follow-Up Mechanism (CRITICAL)

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

Required behavior:

  1. STOP - Don't immediately respond
  2. ANALYZE - Compare answer to user's request
  3. IDENTIFY GAPS - Determine missing information
  4. ASK FOLLOW-UP - If gaps exist, ask immediately:
    python scripts/run.py ask_question.py --question "Follow-up with context..."
  5. REPEAT - Continue until information complete
  6. SYNTHESIZE - Combine all answers before responding

Quick Commands

# Authentication
python scripts/run.py auth_manager.py status|setup|reauth|clear

# Library management
python scripts/run.py notebook_manager.py list|search --query QUERY|activate --id ID|stats

# Cleanup (preserves library)
python scripts/run.py cleanup_manager.py --preserve-library --confirm

Troubleshooting

Error Solution
ModuleNotFoundError Use run.py wrapper
Authentication failed Browser must be visible for setup
Rate limit (50/day) Wait or switch Google account
Browser crashes cleanup_manager.py --preserve-library

Important Notes

  • Local Claude Code only - Web UI sandbox blocks network access
  • Stateless sessions - Each question = fresh browser (3-5 sec overhead)
  • Browser automation - UI changes will break selectors (see README maintenance section)
  • Expect maintenance - NotebookLM updates require selector updates
  • See README.md and references/ for comprehensive documentation

Data Storage

~/.claude/skills/notebooklm/data/
├── library.json         # Notebook metadata
├── auth_info.json       # Auth status
└── browser_state/       # Browser cookies (NEVER commit)

All sensitive data protected by .gitignore.