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Agent Skill · 前端设计

java-profiling(Java profiling 测试)

用于辅助 Java 项目开发、面向对象设计、Spring 生态、Maven 或 Gradle 依赖和后端工程实践。它适合让 Agent 分析类结构、设计接口、整理服务分层、生成测试或检查常见代码坏味道。使用时需要结合项目已有架构、包结构和依赖版本,不应只按通用教程改代码;涉及数据库、事务、并发或框架配置时,应先确认运行环境和回归测试范围。

java-profiling 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 5,213。

总安装 717周安装 29GitHub Stars 12下载 225核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:java-profiling(Java profiling 测试)
来源仓库:https://github.com/claude-dev-suite/claude-dev-suite
仓库路径:skills/java-profiling
安装命令:
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill java-profiling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill java-profiling

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

支持 Java 应用运行时性能剖析与资源监控。集成多种 Profiler 工具的使用方法和技术要点。帮助识别 CPU 密集、内存泄漏等典型问题。高频采样可能影响线上服务稳定性需谨慎使用。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

java-profiling 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/java-profiling

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill java…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

java-profiling 怎么安装?

在终端执行 npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill java-profiling。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

java-profiling 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

java-profiling 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

java-profiling 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

java-profiling 用的人多吗?

公开数据是累计安装 717 次,近一周安装 29 次,下载 225 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

java-profiling 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
java-profiling

Java profiling 测试 · 本页

71712操作浏览器未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Java/JVM Performance Profiling

When NOT to Use This Skill

  • Node.js/JavaScript profiling - Use the nodejs-profiling skill for V8 profiler and heap analysis
  • Python profiling - Use the python-profiling skill for cProfile and tracemalloc
  • Application-level optimization - This is for JVM-level profiling, not algorithm optimization
  • Database query tuning - Use database-specific profiling tools
  • Frontend performance - Use browser DevTools for client-side profiling
Deep Knowledge: Use mcp__documentation__fetch_docs with technology: java for comprehensive JFR configuration, GC tuning, and JVM diagnostics.

Java Flight Recorder (JFR)

Starting JFR

# Start recording with application
java -XX:+FlightRecorder \
     -XX:StartFlightRecording=duration=60s,filename=recording.jfr \
     -jar app.jar

# Start recording on running JVM
jcmd <pid> JFR.start duration=60s filename=recording.jfr

# Continuous recording (always-on)
java -XX:+FlightRecorder \
     -XX:FlightRecorderOptions=stackdepth=256 \
     -XX:StartFlightRecording=disk=true,maxsize=500m,maxage=1d \
     -jar app.jar

# Dump current recording
jcmd <pid> JFR.dump filename=dump.jfr

JFR Configuration

# custom.jfc
<?xml version="1.0" encoding="UTF-8"?>
<configuration version="2.0">
  <event name="jdk.CPULoad">
    <setting name="enabled">true</setting>
    <setting name="period">1 s</setting>
  </event>
  <event name="jdk.GCHeapSummary">
    <setting name="enabled">true</setting>
  </event>
  <event name="jdk.ObjectAllocationInNewTLAB">
    <setting name="enabled">true</setting>
    <setting name="stackTrace">true</setting>
  </event>
</configuration>

Analyzing JFR Files

# Print JFR summary
jfr summary recording.jfr

# Print specific events
jfr print --events jdk.CPULoad recording.jfr
jfr print --events jdk.ExecutionSample --json recording.jfr

# Export to JSON
jfr print --json recording.jfr > recording.json

jcmd Diagnostics

Process Information

# List all Java processes
jcmd

# VM info
jcmd <pid> VM.version
jcmd <pid> VM.flags
jcmd <pid> VM.system_properties
jcmd <pid> VM.command_line

# Thread dump
jcmd <pid> Thread.print

# Heap info
jcmd <pid> GC.heap_info

# Class histogram
jcmd <pid> GC.class_histogram

Memory Analysis

# Native memory tracking (requires -XX:NativeMemoryTracking=summary)
jcmd <pid> VM.native_memory summary

# Heap dump
jcmd <pid> GC.heap_dump /path/to/dump.hprof

# Force GC
jcmd <pid> GC.run

GC Tuning

GC Selection

# G1GC (default in JDK 9+, recommended for heap > 4GB)
java -XX:+UseG1GC -jar app.jar

# ZGC (low latency, JDK 15+)
java -XX:+UseZGC -jar app.jar

# Shenandoah (low latency, OpenJDK)
java -XX:+UseShenandoahGC -jar app.jar

# Parallel GC (throughput)
java -XX:+UseParallelGC -jar app.jar

GC Logging

# JDK 9+ unified logging
java -Xlog:gc*:file=gc.log:time,uptime,level,tags:filecount=5,filesize=10m \
     -jar app.jar

# Common GC flags
java -XX:+PrintGCDetails \
     -XX:+PrintGCDateStamps \
     -XX:+PrintTenuringDistribution \
     -Xloggc:gc.log \
     -jar app.jar

G1GC Tuning

java -XX:+UseG1GC \
     -XX:MaxGCPauseMillis=200 \        # Target pause time
     -XX:G1HeapRegionSize=16m \        # Region size
     -XX:InitiatingHeapOccupancyPercent=45 \  # Start marking at 45%
     -XX:G1ReservePercent=10 \         # Reserve for promotions
     -XX:ConcGCThreads=4 \             # Concurrent GC threads
     -XX:ParallelGCThreads=8 \         # Parallel GC threads
     -jar app.jar

Memory Optimization

Heap Sizing

# Set heap size
java -Xms4g -Xmx4g -jar app.jar  # Fixed heap (recommended for production)

# Metaspace sizing
java -XX:MetaspaceSize=256m -XX:MaxMetaspaceSize=512m -jar app.jar

# Direct memory
java -XX:MaxDirectMemorySize=256m -jar app.jar

Memory Leak Detection

// Common leak patterns

// ❌ Bad: Static collections that grow
private static List<Object> cache = new ArrayList<>();
public void process(Object obj) {
    cache.add(obj);  // Never removed
}

// ✅ Good: Bounded cache with eviction
private static final Cache<String, Object> cache = Caffeine.newBuilder()
    .maximumSize(10_000)
    .expireAfterWrite(Duration.ofMinutes(10))
    .build();

// ❌ Bad: Unclosed resources
public void readFile(String path) {
    InputStream is = new FileInputStream(path);
    // Missing is.close()
}

// ✅ Good: Try-with-resources
public void readFile(String path) {
    try (InputStream is = new FileInputStream(path)) {
        // Process
    }
}

// ❌ Bad: ThreadLocal not cleaned
private static ThreadLocal<Connection> connHolder = new ThreadLocal<>();
public void process() {
    connHolder.set(getConnection());
    // Missing connHolder.remove()
}

// ✅ Good: Always clean ThreadLocal
public void process() {
    try {
        connHolder.set(getConnection());
        // Process
    } finally {
        connHolder.remove();
    }
}

CPU Profiling

async-profiler (recommended)

# Profile CPU
./profiler.sh -d 30 -f profile.html <pid>

# Profile allocations
./profiler.sh -d 30 -e alloc -f alloc.html <pid>

# Profile locks
./profiler.sh -d 30 -e lock -f lock.html <pid>

# Flame graph output
./profiler.sh -d 30 -f flamegraph.html -o flamegraph <pid>

JMC (Java Mission Control)

# Open JFR recording in JMC
jmc recording.jfr

Common Bottleneck Patterns

Synchronization Issues

// ❌ Bad: Coarse-grained locking
public synchronized void process(String key, Object value) {
    cache.put(key, value);
    compute(value);
}

// ✅ Good: Fine-grained locking
private final ConcurrentHashMap<String, Object> cache = new ConcurrentHashMap<>();
public void process(String key, Object value) {
    cache.put(key, value);  // Lock-free for different keys
    compute(value);
}

// ✅ Good: Read-write lock
private final ReadWriteLock lock = new ReentrantReadWriteLock();
public Object get(String key) {
    lock.readLock().lock();
    try { return cache.get(key); }
    finally { lock.readLock().unlock(); }
}

String Operations

// ❌ Bad: String concatenation in loop
String result = "";
for (String s : list) {
    result += s;  // Creates new String each iteration
}

// ✅ Good: StringBuilder
StringBuilder sb = new StringBuilder();
for (String s : list) {
    sb.append(s);
}
String result = sb.toString();

// ✅ Good: String.join for simple cases
String result = String.join("", list);

Collection Optimization

// ❌ Bad: ArrayList when size known
List<String> list = new ArrayList<>();
for (int i = 0; i < 10000; i++) {
    list.add(getData(i));  // Multiple resizes
}

// ✅ Good: Pre-size collections
List<String> list = new ArrayList<>(10000);

// ✅ Good: Use primitive collections for performance
// Use Eclipse Collections, Trove, or fastutil
IntList list = new IntArrayList(10000);

Boxing/Unboxing

// ❌ Bad: Autoboxing in hot path
public long sum(List<Long> numbers) {
    long sum = 0;
    for (Long n : numbers) {
        sum += n;  // Unboxing each iteration
    }
    return sum;
}

// ✅ Good: Use primitive streams
public long sum(List<Long> numbers) {
    return numbers.stream().mapToLong(Long::longValue).sum();
}

// ✅ Good: Primitive arrays when possible
public long sum(long[] numbers) {
    return Arrays.stream(numbers).sum();
}

JIT Optimization

Warm-up

// Warm-up critical paths before measuring
public static void main(String[] args) {
    // Warm-up phase
    for (int i = 0; i < 10_000; i++) {
        criticalMethod(i);
    }

    // Measurement phase
    long start = System.nanoTime();
    for (int i = 0; i < 100_000; i++) {
        criticalMethod(i);
    }
    long duration = System.nanoTime() - start;
}

JIT Logging

# Print JIT compilation
java -XX:+PrintCompilation -jar app.jar

# Print inlining decisions
java -XX:+UnlockDiagnosticVMOptions -XX:+PrintInlining -jar app.jar

# Disable specific optimizations for debugging
java -XX:+UnlockDiagnosticVMOptions -XX:DisableIntrinsic=_hashCode -jar app.jar

Profiling Checklist

CheckToolCommand
CPU hotspotsJFRjcmd <pid> JFR.start
Memory usagejcmdjcmd <pid> GC.heap_info
GC behaviorGC logs-Xlog:gc*
Thread contentionJFRjdk.JavaMonitorWait events
Memory leaksHeap dumpjcmd <pid> GC.heap_dump
Class loadingjcmdjcmd <pid> VM.classloaders

Production Flags

java \
  -server \
  -Xms4g -Xmx4g \
  -XX:+UseG1GC \
  -XX:MaxGCPauseMillis=200 \
  -XX:+UseStringDeduplication \
  -XX:+AlwaysPreTouch \
  -XX:+DisableExplicitGC \
  -XX:+HeapDumpOnOutOfMemoryError \
  -XX:HeapDumpPath=/var/log/heap.hprof \
  -Xlog:gc*:file=/var/log/gc.log:time,uptime:filecount=5,filesize=10m \
  -jar app.jar

Anti-Patterns

Anti-PatternWhy It's WrongCorrect Approach
System.out.println() in hot pathsExtremely slow, synchronous I/OUse SLF4J with async appenders
Creating many short-lived objectsGC pressure, allocation overheadReuse objects, use object pools
synchronized on hot pathsThread contention, poor scalabilityUse ConcurrentHashMap, ReentrantLock, or lock-free structures
String concatenation with + in loopsCreates many intermediate stringsUse StringBuilder
Autoboxing in loopsCreates wrapper objectsUse primitive types
Not sizing collectionsFrequent resizing, memory churnPre-size with new ArrayList<>(expectedSize)
finalize() for cleanupUnpredictable, deprecatedUse try-with-resources or explicit cleanup
Ignoring GC logsMiss performance degradationAlways enable GC logging in production
One-size-fits-all heapWrong GC pauses for workloadTune heap based on app behavior
Not using connection poolingConnection creation overheadUse HikariCP or similar

Quick Troubleshooting

IssueDiagnosisSolution
Long GC pausesHeap too large or wrong GCUse ZGC/Shenandoah or tune G1GC pause targets
OutOfMemoryError: Java heap spaceMemory leak or undersized heapAnalyze heap dump, increase -Xmx if needed
OutOfMemoryError: MetaspaceToo many classes loadedIncrease -XX:MaxMetaspaceSize or fix classloader leak
High CPU usageHot loop, inefficient algorithmCPU profile with JFR or async-profiler
Thread contentionLock competitionThread dump analysis, reduce lock scope
Slow startupClass loading, initializationUse AppCDS, lazy initialization
Memory leakUnclosed resources, static collectionsHeap dump comparison, find growing objects
GC overhead limit exceededGC taking > 98% of timeFix memory leak or increase heap
Full GC too frequentOld gen filling upTune heap ratio, fix object tenure issues
Application unresponsiveDeadlock or long GCThread dump to find deadlock, GC logs

Related Skills

平台分布

Codex

35.17%
按下载量换算79

Claude

31.37%
按下载量换算71

Cursor

20.24%
按下载量换算46

Gemini CLI

9.77%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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