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Swift Mlx Lm

Checked
picomlx·vlatest·frontend·MIT

Rapidly generates and integrates MLX model previews directly into Picomlx frontends for instant experimentation.

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 picomlx/mlx-swift-lm-skill --skill swift-mlx-lm -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

picomlx

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 picomlx/mlx-swift-lm-skill at 2634426382.

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

Commit
2634426382
Manifest
mlx-swift-lm/SKILL.md
Scanned
13 files, 3,623 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, swift-mlx-lm, allows AI agents to execute Swift code using Apple's Metal by Low Level (MLX) framework. It enables complex numerical computations and machine learning tasks directly within the agent’s environment, leveraging the power of Apple Silicon GPUs. The skill provides a bridge between natural language instructions and executable Swift MLX code.

When to use it

  • Complex Calculations: When you need an AI agent to perform intricate mathematical operations or simulations that are beyond standard scripting capabilities.
  • Machine Learning Inference: To run pre-trained machine learning models (e.g., image recognition, natural language processing) on Apple devices with optimized performance using MLX.
  • Data Processing & Analysis: When the agent needs to process and analyze large datasets requiring specialized numerical libraries available within Swift/MLX.
  • Prototyping ML Applications: Quickly test and prototype machine learning applications leveraging the ease of use of a natural language interface.

Key capabilities

  • Swift code execution via MLX
  • Apple Silicon GPU acceleration
  • Integration with existing AI agent workflows
  • Numerical computation support
  • Machine Learning inference

Example prompts

  • "Calculate the dot product of these two vectors: [1, 2, 3] and [4, 5, 6]."
  • "Run this Swift code snippet: let array = [1,2,3]; print(array.map { $0 * 2 })"
  • "Load the ResNet50 model from MLX and classify this image."

Tips & gotchas

  • Requires access to an environment with Swift and MLX installed. The agent will need appropriate permissions to execute code.
  • Be mindful of resource usage, as complex computations can be demanding on system resources.