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Python Uv Acceleration

Listed
qiao-925·vlatest·backend

Optimizes Python microservice performance using UV loop integration for faster event handling and reduced latency.

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 qiao-925/qiao-skills --skill python-uv-acceleration --full-depth -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

qiao-925

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 2 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: astral.sh.

    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

    astral.shundeclared

    What it found

    • skill-orchestration/skill-units/language-engineering/python-uv-acceleration/references/common-commands.md:7contacts astral.sh
      curl -LsSf https://astral.sh/uv/install.sh | sh

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • skill-orchestration/skill-units/language-engineering/python-uv-acceleration/references/common-commands.md:7remote script piped into a shell
      curl -LsSf https://astral.sh/uv/install.sh | sh

  • 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.

  • Pinned to a commit: passed

    Scanned qiao-925/qiao-skills at 50a1ee4012.

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

Commit
50a1ee4012
Manifest
skill-orchestration/skill-units/language-engineering/python-uv-acceleration/SKILL.md
Scanned
3 files, 254 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 accelerates Python code execution using uv, a fast and efficient implementation of the Python runtime. It leverages uv's capabilities to significantly reduce latency and improve overall performance for computationally intensive tasks. The skill is designed to be easily integrated into existing Python workflows without requiring major architectural changes.

When to use it

  • High-latency APIs: Reduce response times when interacting with slow external services or databases.
  • Real-time data processing: Speed up the analysis and transformation of streaming data for applications like financial trading or IoT analytics.
  • Machine learning inference: Accelerate model predictions, particularly in resource-constrained environments.
  • CPU-bound tasks: Improve performance for Python scripts that heavily rely on CPU computations.

Key capabilities

  • Integration with existing Python code
  • Reduced latency through uv runtime utilization
  • Improved overall execution speed for computationally intensive operations
  • Easy to install and use

Example prompts

  • "Run this Python script using the uv accelerator."
  • "Optimize this function for faster execution with uv."
  • "Can you profile this code and identify bottlenecks that can be improved by uv?"

Tips & gotchas

The skill requires a compatible version of Python and uv to be installed. While generally straightforward, performance gains may vary depending on the specific workload and system configuration.