Agent skill
notebooklm
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.
Install this agent skill to your Project
npx add-skill https://github.com/leegonzales/AISkills/tree/main/NotebookLMSkill/notebooklm
SKILL.md
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:
- STOP - Don't immediately respond
- ANALYZE - Compare answer to user's request
- IDENTIFY GAPS - Determine missing information
- ASK FOLLOW-UP - If gaps exist, ask immediately:
bash
python scripts/run.py ask_question.py --question "Follow-up with context..." - REPEAT - Continue until information complete
- 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.
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