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Python Performance Optimization

Checked
nickcrew·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 nickcrew/claude-cortex --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

nickcrew

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 nickcrew/claude-ctx-plugin at bb47af79ad.

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

Commit
bb47af79ad
Manifest
skills/python-performance-optimization/SKILL.md
Scanned
6 files, 706 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 and suggests changes to reduce execution time and resource consumption, focusing on areas like algorithm efficiency and memory usage. The tool aims to provide practical recommendations that developers can implement directly into their codebase.

When to use it

  • Slow Data Processing: When a Python script takes an unacceptably long time to process large datasets.
  • Resource Intensive Applications: For applications consuming excessive CPU or memory resources.
  • Performance Bottlenecks: To identify and address specific performance bottlenecks in existing code.
  • Code Refactoring: During code refactoring efforts where improved performance is a key goal.

Key capabilities

  • Python code analysis
  • Algorithm optimization suggestions
  • Memory usage reduction recommendations
  • Performance bottleneck identification

Example prompts

  • "Analyze this Python script for performance improvements: [paste code here]"
  • "How can I optimize the memory usage of this function? [paste code here]"
  • "Identify any potential bottlenecks in this data processing pipeline. [describe pipeline]"

Tips & gotchas

The skill's effectiveness depends on providing clear and concise Python code snippets for analysis. While it offers suggestions, thorough testing is crucial after implementing any optimization changes to ensure stability and prevent regressions.