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Java Performance

Checked
pluginagentmarketplace·vlatest·backend

Helps with Java, 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.

  1. 1

    Run this in your project

    terminal
    $ npx skills add pluginagentmarketplace/custom-plugin-java --skill java-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-java at 51aa571c25.

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

Commit
51aa571c25
Manifest
skills/java-performance/SKILL.md
Scanned
7 files, 526 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 assists with optimizing Java Virtual Machine (JVM) performance. It enables an AI agent to tune garbage collection, analyze memory usage, profile CPU hotspots, and benchmark code using JMH. The goal is to improve latency, throughput, and overall efficiency when developing backend services and APIs.

When to use it

  • When you need to tune garbage collection for low latency or high throughput.
  • To identify and address CPU performance bottlenecks through profiling.
  • For analyzing potential memory leaks within your Java application.
  • To benchmark code performance using JMH (Java Microbenchmark Harness).
  • When optimizing container settings related to JVM resource usage.

Key capabilities

  • Garbage Collection Tuning: Provides GC presets for high-throughput, low-latency, memory-constrained, and container-optimized environments.
  • Memory Analysis: Supports heap dump analysis (using tools like MAT - Memory Analyzer Tool) to identify object retention issues.
  • CPU Profiling: Offers commands for thread dumps, flight recording, and using the Async profiler to pinpoint CPU hotspots.
  • Benchmarking: Facilitates code performance benchmarking with JMH.
  • GC Comparison: Provides a comparison of different GC algorithms (G1, ZGC, Shenandoah, Parallel) based on latency, throughput, and heap size requirements.

Example prompts

  • "Can you suggest GC settings for my Java application that prioritize low latency?"
  • "Profile the CPU usage of this Java code snippet."
  • "Generate a heap dump for analysis to identify potential memory leaks."
  • "Benchmark the performance of this method using JMH."

Tips & gotchas

  • Requires familiarity with JVM concepts and tools like jstack, jmap, jstat, and JMH.
  • Heap dumps require additional analysis tools (like MAT) to interpret effectively.
  • Consider container resource limits when optimizing GC settings for containerized environments.