notebooklm

A skill for controlling Google NotebookLM through the local notebooklm-py command-line tool. NotebookLM is Google's workspace for asking questions about supplied sources such as web pages, PDFs, YouTube videos, and files.

In plain words
What is it for?
Creating and managing NotebookLM notebooks, importing research sources, asking source-based questions, and generating artifacts such as audio.
Why use it?
It gives an agent a defined way to check setup, choose a notebook, add sources, ask questions, and generate outputs.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/knowingdoing/notebooklm-codex/notebooklm
Any agent
npx skills add knowingdoing/notebooklm-codex --skill notebooklm
Clone the repo
git clone --depth 1 https://github.com/knowingdoing/notebooklm-codex

Made for: Claude Code, Codex.

Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 694 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00117 $0.00694
Opus 5 $0.00059 $0.00347
Sonnet 5 $0.00023 $0.00139
Haiku 4.5 $0.00012 $0.00069

Measured yesterday against content hash 4260bdf669dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

codex-skill/notebooklm/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NotebookLM

Use the local notebooklm CLI when available. If it is missing or not authenticated, help the user install and authenticate it before attempting NotebookLM work.

Detailed setup, command reference, and MCP-server instructions live in references/notebooklm-cli-and-mcp.md. Read that reference when installing, troubleshooting auth, generating artifacts, downloading outputs, or exposing NotebookLM through MCP.

Quick Workflow

  1. Check whether the CLI exists:
    command -v notebooklm
    notebooklm --help
    
  2. Check authentication and context:
    notebooklm auth check
    notebooklm list
    notebooklm status
    
  3. If no notebook is active, list notebooks and use the requested one:
    notebooklm list
    notebooklm use <notebook_id>
    
  4. Add sources before asking or generating:
    notebooklm source add "https://example.com"
    notebooklm source add ./document.pdf
    notebooklm source list
    
  5. Ask questions or generate artifacts:
    notebooklm ask "Summarize the key points"
    notebooklm generate audio "Focus on the practical takeaways"
    notebooklm artifact list
    

Autonomy Rules

Run these without extra confirmation when they directly support the user's request: status checks, auth checks, listing notebooks/sources/artifacts/languages, setting the active notebook, creating notebooks, adding sources, waiting for source/artifact/research completion, read-only chat queries, and read-only conversation history.

Ask before destructive, expensive, or filesystem-writing actions: deleting notebooks or sources, generating long-running artifacts, downloading files, saving an answer as a note, or saving chat history as a note.

Installation

If notebooklm is unavailable, read the setup reference and install notebooklm-py[browser] in ~/.notebooklm-venv with Python 3.10+. Prefer the bundled login script pattern in the reference because NotebookLM auth is browser-based and often needs a persistent Playwright profile.

Read the full file on GitHub · 60 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 60 lines · 117 tokens per session scan A 4260bdf669dc

Subscribe to this mod's changes

notebooklm is a skill published in the GitHub repository knowingdoing/notebooklm-codex (0 stars, last pushed 2mo ago), licensed MIT. It adds 117 tokens to every session and 694 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

trulens-notebook-execution

Execute and display Jupyter notebooks for TruLens demos and quickstarts.

truera/trulens · 21 tokens

eli5

Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.

companion-inc/feynman · 63 tokens

deck-course-module

暖纸背景 + Playfair, 左侧学习目标常驻, 含 MCQ 自测页.

nexu-io/html-anything · 25 tokens

master-yinguang

Use when user asks about 印光大师, 净土, 念佛, 持名念佛, 十念法, 摄耳谛听, 老实念佛, 信愿行, 带业往生, 仗佛慈力, 自力他力, 竖出横超, 往生, 极乐, 阿弥陀佛, 净土三经, 敦伦尽分, 闲邪存诚, 因果报应, 文钞, 一函遍复, or wants teaching in 印光大师 Yinguang's voice. Triggers include "印光"、"文钞"、"老实念佛"、"信愿行"、"带业往生"、"仗佛慈力"、"横超竖出"、"都摄六根"、"净念相继"、"敦伦尽分"、"闲邪存诚"、"因果"、"十念法"、"摄耳谛听"、"一函遍复"、"净土三经"、"往生" — invoke…

xr843/Master-skill · 274 tokens

explore-unknowns

Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…

dzhng/skills · 162 tokens

obsidian-to-clew-import

Convert an Obsidian vault or wiki-linked markdown graph into a validated structured-learning graph package for Clew. Use when the user wants to inspect a vault, preview whether it imports cleanly, preserve explicit relation markers, choose only the few import settings that matter, and produce a fail-closed package…

miuuyy/Clew · 72 tokens