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Python Performance

Checked
pluginagentmarketplace·vlatest·backend

Helps with Python, performance 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.

Installs under a different name. Its SKILL.md names it Python Performance, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add pluginagentmarketplace/custom-plugin-python --skill "Python Performance" -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

pluginagentmarketplace

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 pluginagentmarketplace/custom-plugin-python at 4415517654.

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

Commit
4415517654
Manifest
skills/python-performance/SKILL.md
Scanned
4 files, 405 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 Python Performance skill enables AI agents to analyze, profile, and optimize Python code for speed and efficiency. It identifies bottlenecks in execution flow and suggests specific refactoring strategies to improve runtime performance without altering core logic.

When to use it

  • Optimizing legacy Python scripts that are running slower than expected in production environments.
  • Reducing memory footprint and CPU usage for long-running data processing pipelines.
  • Preparing codebases for deployment on resource-constrained hardware or cloud instances.
  • Refactoring inefficient loops, list comprehensions, or algorithmic structures before scaling.

Key capabilities

  • Automated static analysis of Python source code to detect performance anti-patterns.
  • Profiling execution paths to pinpoint specific lines causing latency spikes.
  • Generating optimized code snippets that replace inefficient constructs with faster alternatives.
  • Providing actionable recommendations on library usage and algorithmic complexity reduction.

Example prompts

  • "Analyze this Python function for performance bottlenecks and suggest optimizations."
  • "Profile the execution time of this data processing script and identify memory leaks."
  • "Refactor this inefficient loop to use vectorized operations or built-in functions for speed."

Tips & gotchas

Ensure your codebase is fully instrumented with profiling tools before relying solely on automated analysis. While the skill suggests optimizations, always validate performance gains in your specific runtime environment, as theoretical improvements may vary based on hardware and data size.