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/notebooklm

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.

From plugin
ay-skills
886 skills
Install
$ npx -y skills add walidboulanouar/Ay-Skills --skill notebooklm --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/notebooklm

Context preview

The summary Claude sees to decide when to auto-load this 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.

SKILL.md

notebooklm.SKILL.md
name: notebooklm
description: 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.

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.

**Source:** https://github.com/PleasePrompto/notebooklm-skill

Install

git clone https://github.com/PleasePrompto/notebooklm-skill ~/.claude/skills/notebooklm

The `run.py` wrapper auto-creates `.venv`, installs dependencies, and sets up Chromium on first run.

When to Use This Skill

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
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 — fails without venv
python scripts/auth_manager.py status

Core Workflow

Step 1: Check Authentication

python scripts/run.py auth_manager.py status

Step 2: Authenticate (One-Time Setup)

# Browser MUST be visible for manual Google login
python scripts/run.py auth_manager.py setup

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

Step 3: Manage Notebook Library

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

# Add notebook — 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, then add)
python scripts/run.py ask_question.py --question "What topics are covered in this notebook?" --notebook-url "[URL]"

# Search, activate, remove
python scripts/run.py notebook_manager.py search --query "keyword"
python scripts/run.py notebook_manager.py activate --id notebook-id
python scripts/run.py notebook_manager.py remove --id notebook-id

Step 4: Ask Questions

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

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

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

# Debug mode
python scripts/run.py ask_question.py --question "..." --show-browser

Follow-Up Mechanism (CRITICAL)

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

Required behavior: 1. STOP — do not immediately respond to user 2. ANALYZE — compare answer to original request 3. IDENTIFY GAPS — determine if more info needed 4. ASK FOLLOW-UP — if gaps exist, query again with context 5. SYNTHESIZE — combine all answers before responding

Script Reference

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

# Notebooks
python scripts/run.py notebook_manager.py add|list|search|activate|remove|stats

# Questions
python scripts/run.py ask_question.py --question "..." [--notebook-id ID] [--notebook-url URL]

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

Data Storage

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

  • `library.json` — notebook metadata
  • `auth_info.json` — auth status
  • `browser_state/` — browser cookies/session

Protected by `.gitignore`. Never commit.

Limitations

  • No session persistence (each question = new browser session)
  • Rate limit: ~50 queries/day on free Google accounts
  • Manual upload required (user adds docs to NotebookLM)
  • Requires Python 3.8+

Troubleshooting

| Problem | Solution | |---------|----------| | ModuleNotFoundError | Use `run.py` wrapper, never call scripts directly | | Auth fails | Browser must be visible: `--show-browser` | | Rate limit hit | Wait or switch Google account | | Browser crashes | `python scripts/run.py cleanup_manager.py --preserve-library` |

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