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Performance Engineering

Checked
travisjneuman·vlatest·other·MIT

Optimizes code and infrastructure for peak efficiency and reduced resource consumption using data-driven insights.

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 travisjneuman/.claude --skill performance-engineering -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

travisjneuman

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 travisjneuman/.claude at 050878886e.

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

Scanned
1 file, 500 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 provides performance engineering expertise to analyze and optimize systems. It can identify bottlenecks, suggest improvements for latency and throughput, and provide guidance on scaling strategies. The skill aims to improve system efficiency and responsiveness based on established principles of performance engineering.

When to use it

  • Troubleshooting slow applications: When users report sluggishness or long loading times in an application.
  • Preparing for increased load: Before a marketing campaign or anticipated surge in user activity.
  • Optimizing database queries: To reduce query execution time and improve overall data access performance.
  • Evaluating architectural changes: Assessing the potential impact of new technologies or design patterns on system performance.

Key capabilities

  • Bottleneck identification
  • Latency reduction strategies
  • Throughput optimization
  • Scaling recommendations

Example prompts

  • "Analyze this application's performance and identify any bottlenecks."
  • "Suggest ways to improve the latency of database query X."
  • "What are some scaling strategies for a system experiencing high load?"

Tips & gotchas

The skill’s effectiveness relies on providing sufficient context about the system being analyzed. Be prepared to share relevant metrics, architecture diagrams, or code snippets for more accurate and actionable recommendations.