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Notebooklm

Listed
pleaseprompto·vlatest·writing·MIT

NotebookLM generates detailed, step-by-step notes from your prompts, streamlining learning and research by creating organized summaries.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pleaseprompto/notebooklm-skill --skill notebooklm -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

pleaseprompto

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: notebooklm.google.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    github.comsource hostnotebooklm.google.comundeclared

    What it found

    • scripts/auth_manager.py:114contacts notebooklm.google.com
      page.goto("https://notebooklm.google.com", wait_until="domcontentloaded")
    • scripts/auth_manager.py:260contacts notebooklm.google.com
      page.goto("https://notebooklm.google.com", wait_until="domcontentloaded", timeout=30000)

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned pleaseprompto/notebooklm-skill at c80722d3f2.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
c80722d3f2
Manifest
SKILL.md
Scanned
19 files, 3,857 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The notebooklm skill enables AI agents to interact with and manipulate Jupyter Notebooks, including executing code cells, generating markdown, and managing notebook structure. It supports dynamic content generation within notebooks and can be used for educational purposes or data analysis workflows.

When to use it

  • When you need an AI agent to run or modify Python scripts in a Jupyter Notebook environment.
  • For creating interactive tutorials or demonstrations that involve live code execution.
  • To automate the process of generating reports or documentation directly within a notebook interface.
  • When integrating AI-generated insights into data analysis workflows using notebooks.

Key capabilities

  • Execute code cells within Jupyter Notebooks
  • Generate markdown and text content dynamically
  • Manage notebook structure (e.g., adding/removing cells)
  • Support for interactive data visualization

Example prompts

  • "Run the Python script in this Jupyter Notebook to analyze the dataset."
  • "Generate a new markdown cell explaining the results of the last code execution."
  • "Add a new code cell that plots a histogram of the 'age' column from the DataFrame."

Tips & gotchas

  • Ensure that the AI agent has access to a running Jupyter kernel for executing code.
  • Be cautious with notebook modifications—always save backups before making significant changes.