Performance Engineer

Name: Performance Engineer
Author: dokhacgiakhoa

Listed

dokhacgiakhoa·vlatest·other

Optimizes application performance through automated analysis, bottleneck identification, and targeted code suggestions.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Run this in your project

terminal

$ npx skills add dokhacgiakhoa/agent-skills-4-vibe-coding-cli --skill performance-engineer -a claude-code

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

dokhacgiakhoa

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 1 Oct 2026

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
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 dokhacgiakhoa/antigravity-ide at c4a059bbf7.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: c4a059bbf7
Manifest: .agent/skills/performance-engineer/SKILL.md
Scanned: 0 files
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 allows AI agents to perform performance engineering tasks. It can analyze code for bottlenecks, suggest optimizations, and help improve the overall efficiency of applications. The agent is capable of identifying areas where resource usage (CPU, memory) is excessive and recommending changes to mitigate these issues.

When to use it

Code Optimization: When you need an AI agent to review a codebase and identify performance bottlenecks.
Resource Usage Analysis: To understand why an application is consuming excessive CPU or memory resources.
Performance Tuning: When seeking recommendations for improving the speed and responsiveness of a software system.
Scalability Planning: To predict how changes will impact performance as load increases.

Key capabilities

Code bottleneck analysis
Resource usage identification (CPU, Memory)
Optimization suggestions
Performance tuning recommendations

Example prompts

"Analyze this Python script for performance bottlenecks and suggest improvements."
"Why is my application using so much memory? Can you identify the cause?"
"How can I optimize this database query to improve response time?"

Tips & gotchas

The agent's effectiveness depends on providing clear code snippets or descriptions of the system being analyzed. It may require additional context about the environment and expected performance characteristics for optimal results.