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load-test-scenario-builder负载测试场景构建器

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

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2,032

周安装

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下载量

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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

3

许可证

MIT

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:load-test-scenario-builder(负载测试场景构建器)
来源仓库:https://github.com/patricio0312rev/skills
仓库路径:skills/load-test-scenario-builder
安装命令:
npx skills add https://github.com/patricio0312rev/skills --skill load-test-scenario-builder
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/patricio0312rev/skills --skill load-test-scenario-builder

简介

用于辅助测试设计、自动化测试、用例整理和回归验证。

  • 适用于研究检索类任务,可结合来源仓库和原始 README 核验具体用法。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和安装路径。
  • 使用时需确认项目测试框架和运行命令,避免为了通过测试而修改真实逻辑。
  • load-test-scenario-builder 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Load Test Scenario Builder

Validate system capacity with realistic load tests.

Load Test Scenarios

interface LoadTestScenario {
  name: string;
  description: string;
  virtualUsers: number;
  duration: string;
  rampUp: string;
  successCriteria: {
    p95Latency: number;
    errorRate: number;
    throughput: number;
  };
}

const scenarios: LoadTestScenario[] = [
  {
    name: "Baseline Load",
    description: "Normal traffic pattern",
    virtualUsers: 100,
    duration: "10m",
    rampUp: "2m",
    successCriteria: {
      p95Latency: 500, // ms
      errorRate: 0.01, // 1%
      throughput: 1000, // req/s
    },
  },
  {
    name: "Peak Load",
    description: "Black Friday traffic",
    virtualUsers: 1000,
    duration: "30m",
    rampUp: "5m",
    successCriteria: {
      p95Latency: 2000,
      errorRate: 0.05,
      throughput: 5000,
    },
  },
  {
    name: "Stress Test",
    description: "Find breaking point",
    virtualUsers: 5000,
    duration: "20m",
    rampUp: "10m",
    successCriteria: {
      p95Latency: 5000,
      errorRate: 0.1,
      throughput: 10000,
    },
  },
];

K6 Load Test Script

// load-tests/checkout-flow.js
import http from "k6/http";
import { check, sleep } from "k6";
import { Rate } from "k6/metrics";

const errorRate = new Rate("errors");

export let options = {
  stages: [
    { duration: "2m", target: 100 }, // Ramp up
    { duration: "10m", target: 100 }, // Stay at 100
    { duration: "2m", target: 0 }, // Ramp down
  ],
  thresholds: {
    http_req_duration: ["p(95)<500"], // 95% under 500ms
    errors: ["rate<0.01"], // Error rate <1%
  },
};

export default function () {
  // 1. Browse products
  let browseRes = http.get("https://api.example.com/products");
  check(browseRes, {
    "browse status 200": (r) => r.status === 200,
  }) || errorRate.add(1);
  sleep(1);

  // 2. Add to cart
  let addCartRes = http.post(
    "https://api.example.com/cart",
    JSON.stringify({
      productId: "123",
      quantity: 1,
    }),
    {
      headers: { "Content-Type": "application/json" },
    }
  );
  check(addCartRes, {
    "add cart status 201": (r) => r.status === 201,
  }) || errorRate.add(1);
  sleep(2);

  // 3. Checkout
  let checkoutRes = http.post(
    "https://api.example.com/checkout",
    JSON.stringify({
      paymentMethod: "card",
    }),
    {
      headers: { "Content-Type": "application/json" },
    }
  );
  check(checkoutRes, {
    "checkout status 200": (r) => r.status === 200,
    "checkout success": (r) => r.json("status") === "success",
  }) || errorRate.add(1);
  sleep(3);
}

Traffic Models

// Realistic traffic patterns
export const trafficModels = {
  // Steady state
  steadyState: {
    stages: [{ duration: "30m", target: 500 }],
  },

  // Gradual ramp
  gradualRamp: {
    stages: [
      { duration: "5m", target: 100 },
      { duration: "5m", target: 300 },
      { duration: "5m", target: 500 },
      { duration: "10m", target: 500 },
      { duration: "5m", target: 0 },
    ],
  },

  // Spike test
  spikeTest: {
    stages: [
      { duration: "2m", target: 100 },
      { duration: "1m", target: 2000 }, // Sudden spike
      { duration: "2m", target: 100 },
    ],
  },

  // Soak test (endurance)
  soakTest: {
    stages: [
      { duration: "5m", target: 500 },
      { duration: "4h", target: 500 }, // Long duration
      { duration: "5m", target: 0 },
    ],
  },
};

Success Thresholds

export const thresholds = {
  // Latency
  http_req_duration: [
    "p(50)<200", // 50% under 200ms
    "p(95)<500", // 95% under 500ms
    "p(99)<1000", // 99% under 1s
  ],

  // Error rate
  http_req_failed: ["rate<0.01"], // <1% errors

  // Throughput
  http_reqs: ["rate>1000"], // >1000 req/s

  // Custom metrics
  checkout_duration: ["p(95)<2000"],
  checkout_success_rate: ["rate>0.95"],
};

Running Load Tests

#!/bin/bash
# scripts/run-load-tests.sh

echo "Running load tests..."

# Baseline test
k6 run --vus 100 --duration 10m load-tests/checkout-flow.js

# Peak load test
k6 run --vus 1000 --duration 30m load-tests/checkout-flow.js

# Stress test (find breaking point)
k6 run --vus 5000 --duration 20m load-tests/stress-test.js

# Generate report
k6 run --out json=results.json load-tests/checkout-flow.js
k6 run --out influxdb=http://localhost:8086 load-tests/checkout-flow.js

Result Analysis

interface LoadTestResult {
  scenario: string;
  timestamp: Date;
  metrics: {
    p50Latency: number;
    p95Latency: number;
    p99Latency: number;
    errorRate: number;
    throughput: number;
    maxVUs: number;
  };
  passed: boolean;
  notes: string[];
}

function analyzeResults(results: LoadTestResult) {
  console.log(\`Load Test: \${results.scenario}\`);
  console.log(\`Status: \${results.passed ? '✅ PASS' : '❌ FAIL'}\`);
  console.log(\`p95 Latency: \${results.metrics.p95Latency}ms\`);
  console.log(\`Error Rate: \${(results.metrics.errorRate * 100).toFixed(2)}%\`);
  console.log(\`Throughput: \${results.metrics.throughput} req/s\`);

  if (!results.passed) {
    console.log('Failed criteria:');
    results.notes.forEach(note => console.log(\`  - \${note}\`));
  }
}

Output Checklist

  • Scenarios defined
  • k6 scripts created
  • Traffic models configured
  • Success criteria set
  • CI integration
  • Results analysis ENDFILE

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

28.27%
按下载量换算184

Gemini CLI

22.32%
按下载量换算145

Antigravity

18.83%
按下载量换算123

windsurf

12.52%
按下载量换算82

github-copilot

8.51%
按下载量换算55

Codex

3.81%
按下载量换算25

安全审计

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可疑

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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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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