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Skill Optimization

Checked
shinpr·vlatest·other·MIT

Automatically analyzes task workflows to suggest improvements and optimize resource allocation for enhanced efficiency.

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 shinpr/ai-coding-project-boilerplate --skill skill-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

shinpr

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 shinpr/ai-coding-project-boilerplate at 0efcb8f120.

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

Scanned
9 files, 1,044 lines
Copies in repo
3 — all of them scanned
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 code for improved performance and efficiency. It analyzes existing code, identifies bottlenecks, and suggests or implements changes to reduce execution time and resource consumption. The optimization can include refactoring algorithms, improving data structures, and reducing unnecessary computations.

When to use it

  • Slow scripts: Use when a script takes an unexpectedly long time to complete.
  • Resource-intensive tasks: Apply to code that consumes excessive memory or CPU power.
  • Performance bottlenecks: Identify and resolve specific areas of code slowing down overall execution.
  • Large datasets: Optimize processing pipelines for handling large volumes of data efficiently.

Key capabilities

  • Code analysis
  • Bottleneck identification
  • Algorithm refactoring suggestions
  • Data structure optimization
  • Resource consumption reduction

Example prompts

  • "Optimize this Python script for faster execution."
  • "Analyze this code and suggest improvements to reduce memory usage."
  • "Refactor this algorithm to improve its time complexity."

Tips & gotchas

The effectiveness of the skill depends on the quality and structure of the input code. Complex or poorly written code may require more significant refactoring efforts.