Skip to content
TrustedSkillsbeta
Back to all skills

Python Performance Optimization

Checked
rmyndharis·vlatest·backend·MIT

Helps with Python, performance optimization, optimization as part of developing backend services and APIs workflows.

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 rmyndharis/antigravity-skills --skill python-performance-optimization -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

rmyndharis

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 rmyndharis/antigravity-skills at 3eff4af253.

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

Commit
3eff4af253
Manifest
skills/python-performance-optimization/SKILL.md
Scanned
2 files, 856 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 optimizes Python code for improved performance. It analyzes existing Python scripts, identifies bottlenecks such as inefficient algorithms or data structures, and suggests or implements changes to reduce execution time and resource consumption. The goal is to make Python applications run faster and more efficiently.

When to use it

  • Slow Data Processing: When dealing with large datasets where processing speed is critical (e.g., analyzing log files, scientific simulations).
  • Resource-Intensive Applications: For applications that consume excessive CPU or memory resources.
  • Performance Bottlenecks: When profiling reveals specific areas of code significantly impacting overall performance.
  • Web Application Optimization: To reduce response times and improve the user experience for web-based Python applications.

Key capabilities

  • Code Profiling: Identifies performance bottlenecks within Python scripts.
  • Algorithm Optimization: Suggests more efficient algorithms or data structures.
  • Data Structure Selection: Recommends appropriate data structures based on usage patterns.
  • Implementation of Optimizations: Automatically applies identified optimizations where possible.

Example prompts

  • "Optimize this Python script for faster execution." (followed by the script)
  • "Analyze this code and identify any performance bottlenecks." (followed by the script)
  • "Suggest ways to improve the efficiency of this data processing pipeline." (followed by a description of the pipeline)

Tips & gotchas

This skill requires access to the Python code you want optimized. The effectiveness depends on the complexity of the code and the availability of profiling information.