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Notebooklm Studio

Community
bighardperson
notebooklm-studio

Import sources (URLs, YouTube, files, text) into Google NotebookLM and generate artifacts: podcasts, videos, reports, quizzes, flashcards, mind maps, slide decks, infographics, data tables. Use when users want to study from web content, create learning materials from URLs or documents, generate quizzes from articles, or produce study aids.

Overview

Publisherbighardperson
Repositorycomputer-science-skills-collection
Skill namenotebooklm-studio
Stars
66
Forks
10
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bighardperson on GitHub. Read the source before you install it.

Installation

Install the Notebooklm Studio AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bighardperson/computer-science-skills-collection/tree/main/skills/NotebookLM%20Studio
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Notebooklm Studio in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Notebooklm Studio on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Notebooklm Studio is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

NotebookLM Studio

Import sources into NotebookLM, generate user-selected artifacts via CLI, download results locally.

Inputs

Collect from user message (ask only for missing fields):

  • Sources: URLs, YouTube links, text notes, or file attachments (PDF, Word, audio, image, Google Drive link)
  • Artifacts: User selects from 9 types (no default — always ask):
    • audio (podcast), video, report, quiz, flashcards, mind-map, slide-deck, infographic, data-table
  • Language (optional, default: zh_Hant): applied via notebooklm language set
  • Artifact options: format, style, length, difficulty, etc.
  • Custom instructions (optional): passed as description to generate commands

Workflow

Steps are sequential gates — do NOT skip or combine steps.

  1. Auth precheck — Verify the session is valid:

    notebooklm auth check --test --json
  2. Parse input & configure artifacts —

    • 1a. Select artifacts
    • 1b. Discuss options (ASK / OFFER / SILENT priority levels)
  3. Derive slug — Generate a short kebab-case slug for the notebook name and output directory.

  4. Create notebook —

    notebooklm create "<slug> <YYYYMMDD>"
    notebooklm use <notebook_id>
    mkdir -p ./output/<slug>
  5. Set language — notebooklm language set <confirmed_language>

  6. Add sources — For each source: notebooklm source add "<url_or_filepath>"

  7. Generate artifacts — Two-tier strategy:

    • Tier 1 (Immediate): mind-map, report, quiz, flashcards, data-table, infographic — use --wait
    • Tier 2 (Deferred): slide-deck, video, audio — use --json, capture task_id for polling
  8. Download Tier 1 — Each artifact into ./output/<slug>/

  9. Report + Deliver Tier 1 — Present completed artifacts to user

  10. Poll + Deliver Tier 2 — Wait for deferred artifacts, download and deliver as each completes

Artifact Types

TypeTierTypical Time
mind-map1Instant
report11-2 min
quiz11-2 min
flashcards11-2 min
data-table11-2 min
infographic12-5 min
slide-deck25-15 min
video210-30 min
audio (podcast)210-30 min

Requirements

  • notebooklm CLI (notebooklm-py)
  • ffmpeg (for audio compression)
  • playwright (for browser automation)

Error Handling

  • Auth errors: Caught by step 0 precheck. Re-login if expired.
  • Tier 1 failure: Retry up to 2 times, then include failure note in delivery.
  • Tier 2 failure: Notify user per-artifact. Tier 1 already delivered.
  • Timeout recovery: Never re-generate on timeout. Re-check status, re-wait if still processing.

Delivery Template

  1. Selection rationale (≤3 bullets)
  2. Artifact list with paths/status
  3. Key takeaways (3-5 bullets)
  4. Failures + fallback note (if any)
  5. One discussion question

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Notebooklm Studio AI skill do?

Import sources (URLs, YouTube, files, text) into Google NotebookLM and generate artifacts: podcasts, videos, reports, quizzes, flashcards, mind maps, slide decks, infographics, data tables. Use when users want to study from web content, create learning materials from URLs or documents, generate quizzes from articles, or produce study aids.

Why use Notebooklm Studio on TypingMind?

Because you install it once and use it with any model. Notebooklm Studio is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Notebooklm Studio in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bighardperson/computer-science-skills-collection/tree/main/skills/NotebookLM%20Studio. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Notebooklm Studio?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Notebooklm Studio?

As many as you like. As long as a model supports skills, you can use Notebooklm Studio with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Notebooklm Studio AI skill free?

It is published on GitHub by bighardperson. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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