Skip to content
TrustedSkillsbeta
Back to all skills

Python Notebooks Async

Checked
ahgraber·vlatest·backend·CC0-1.0

Asynchronously executes Python notebooks provided as input, returning results for automated workflows and data processing.

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 ahgraber/skills --skill python-notebooks-async -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

ahgraber

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 ahgraber/skills at 5fa2be4df6.

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

Commit
5fa2be4df6
Manifest
skills/python-notebooks-async/SKILL.md
Scanned
2 files, 163 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

This skill allows an AI agent to execute Python notebooks asynchronously. It can run code cells, display output (including text, images, and interactive widgets), and handle errors within a notebook environment. This enables complex data processing, model training, and visualization workflows directly within the AI agent's capabilities.

When to use it

  • Data Analysis: When you need an agent to perform exploratory data analysis or generate reports from datasets.
  • Model Training/Evaluation: To automate machine learning tasks like training models on specific datasets and evaluating their performance.
  • Interactive Visualization: For creating dynamic visualizations and dashboards based on data processed by the agent.
  • Complex Calculations: When a task requires multiple steps or dependencies that are best organized within a notebook structure.

Key capabilities

  • Asynchronous Notebook Execution
  • Code Cell Execution
  • Output Display (text, images, interactive widgets)
  • Error Handling

Example prompts

  • "Run the notebook located at /path/to/my_analysis.ipynb and show me the results."
  • "Execute the train_model.ipynb notebook using these parameters: learning_rate=0.01, batch_size=32."
  • "Can you run this notebook and save the generated plot as a PNG?"

Tips & gotchas

  • Ensure the AI agent has access to the necessary Python environment and dependencies required by the notebooks.
  • Large or computationally intensive notebooks may take significant time to execute.