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Pytorch

Checked
itsmostafa·vlatest·ai-ml·MIT

This skill utilizes PyTorch, a powerful deep learning framework, enabling users to build and train complex neural networks efficiently.

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 itsmostafa/llm-engineering-skills --skill pytorch -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

itsmostafa

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 itsmostafa/llm-engineering-skills at c364a15d51.

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

Commit
c364a15d51
Manifest
skills/pytorch/SKILL.md
Scanned
3 files, 903 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 provides access to PyTorch, a popular open-source machine learning framework. It enables AI agents to perform complex numerical computations, build and train neural networks, and work with tensors efficiently. This is particularly useful for tasks involving image recognition, natural language processing, and other data-intensive applications.

When to use it

  • Image Classification: Train an agent to classify images based on their content (e.g., identifying different types of flowers).
  • Natural Language Processing: Build a model to analyze text sentiment or generate creative writing pieces.
  • Data Analysis: Perform advanced statistical analysis and modeling on large datasets.
  • Scientific Computing: Simulate physical systems or perform complex calculations in fields like physics or chemistry.

Key capabilities

  • Tensor operations
  • Neural network building blocks
  • Automatic differentiation
  • GPU acceleration (if available)

Example prompts

  • "Create a PyTorch model for image classification using a convolutional neural network."
  • "Write a script to load and preprocess the MNIST dataset in PyTorch."
  • “Generate code to calculate the gradient of a function with respect to its inputs, using PyTorch.”

Tips & gotchas

  • Requires familiarity with Python programming.
  • GPU acceleration significantly speeds up training; ensure appropriate hardware is available if needed.