Token导航 LogoToken导航TokenDH.com
前端设计操作浏览器github未标认证来源可访问许可证需确认审计通过

java-profilingJava profiling 测试

Agent Skill

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。

总安装

717

周安装

29

GitHub Stars

12

下载量

225
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills