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Cuda

Checked
technillogue·vlatest·ai-ml

Cuda generates concise, creative prompts for various AI models, boosting your content creation workflow and sparking innovative ideas quickly.

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 technillogue/ptx-isa-markdown --skill cuda -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

technillogue

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 technillogue/ptx-isa-markdown at 64cfba5fca.

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

Commit
64cfba5fca
Manifest
cuda_skill/SKILL.md
Scanned
60 files, 1,425 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

The CUDA skill enables AI agents to interface with NVIDIA’s parallel computing platform, allowing them to leverage GPU acceleration for high-performance tasks. It provides access to the PTX ISA (Parallel Thread Execution) and supports compiling code optimized for specific GPU architectures.

When to use it

  • Accelerating computationally intensive machine learning model training or inference workloads.
  • Running scientific simulations that require massive parallel processing power.
  • Deploying deep learning applications on NVIDIA hardware for real-time performance.
  • Executing custom kernels written in PTX or CUDA C++ within an agent environment.

Key capabilities

  • Direct integration with NVIDIA GPU hardware resources.
  • Support for PTX ISA compilation and execution.
  • Optimization for various NVIDIA GPU architectures.
  • Execution of parallel thread-based computations.

Example prompts

  • "Compile this CUDA kernel using the PTX ISA to run on my local GPU."
  • "Optimize this deep learning training loop by offloading heavy matrix operations to CUDA."
  • "Execute a parallel simulation task utilizing available NVIDIA hardware acceleration."

Tips & gotchas

Ensure your environment has compatible NVIDIA drivers and a supported GPU before attempting installation. Performance gains depend heavily on the specific architecture of the target hardware and the efficiency of the compiled PTX code.