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performance-testing-en性能测试 en

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。

总安装

259

周安装

11

GitHub Stars

39

下载量

91
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:performance-testing-en(性能测试 en)
来源仓库:https://github.com/naodeng/awesome-qa-skills
仓库路径:skills/performance-testing-en
安装命令:
npx skills add https://github.com/naodeng/awesome-qa-skills --skill performance-testing-en
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/naodeng/awesome-qa-skills --skill performance-testing-en

简介

用于辅助测试设计、自动化测试、用例整理和回归验证。

  • 适合编写单元测试、端到端测试或根据失败日志定位问题。
  • 使用时需确认项目测试框架、运行命令和夹具数据,避免误改逻辑。
  • 安装命令:npx skills add https://github.com/naodeng/awesome-qa-skills --skill performance-testing-en。
  • 涉及浏览器或外部服务时,应区分本地模拟与生产环境。

SKILL.md

Performance Testing (English)

中文版: See skill performance-testing.

Prompt: this directory's prompts/performance-testing_EN.md.

When to Use

  • User mentions performance testing, performance-testing
  • Need to execute this testing type or produce deliverables per Standard-version
  • Trigger examples: "Generate/design/write performance test plan for the following"

Output Format Options

Markdown by default. For Excel / CSV / JSON, add at the end of your request; see output-formats.md.

How to Use

  1. Open the relevant file in this directory's prompts/ and copy the content below the dashed line.
  2. Append your requirements and context (business flow, environment, constraints, acceptance criteria).
  3. If you need non-Markdown output, append the request sentence from output-formats.md at the end.

Code Examples

1. K6 Load Testing

Complete K6 performance testing example including load, stress, spike, and API testing.

Location: ../performance-testing/examples/k6-load-testing/

Includes:

  • Load test script
  • Stress test script
  • Spike test script
  • API performance test script
  • Automated run scripts
  • Detailed README documentation

Quick Start:

cd examples/k6-load-testing
chmod +x run-tests.sh
./run-tests.sh load

Test Coverage:

  • Load testing (simulate normal business volume)
  • Stress testing (find performance limits)
  • Spike testing (sudden traffic)
  • API performance testing (REST API)
  • Custom metrics and thresholds

See: examples/k6-load-testing/README.md

Best Practices

Performance Test Design Principles

  1. Test Type Selection

- Load testing: Verify system performance under expected load - Stress testing: Find system performance limits - Spike testing: Test sudden traffic handling capability - Soak testing: Verify long-term stability

  1. Test Scenario Design

- Based on real user behavior - Reasonable think time - Gradually increase load - Include warm-up and cool-down phases

  1. Performance Metrics

- Response Time - Throughput/RPS - Error Rate - Concurrent Users - Resource Usage (CPU, Memory, Network)

  1. Threshold Setting

- Define based on business requirements - Use percentiles (p95, p99) - Set reasonable error rates - Monitor trend changes

Tool Selection Guide

ToolUse CaseAdvantages
K6Modern performance testingScriptable, easy to use, cloud-native
JMeterTraditional performance testingFeature-rich, GUI, many plugins
GatlingScala/Java projectsHigh performance, beautiful reports
LocustPython projectsEasy to learn, distributed
ArtilleryNode.js projectsSimple configuration, CI/CD friendly

Common Pitfalls

  • ❌ Using unrealistic traffic models → ✅ Build scenarios from production-like behavior and workload mix
  • ❌ Looking only at average latency → ✅ Track p95/p99, error rate, throughput, and saturation together
  • ❌ Skipping baseline and warm-up phases → ✅ Establish baseline, warm-up, then apply staged load
  • ❌ Ignoring bottleneck evidence → ✅ Correlate app metrics with CPU, memory, I/O, and downstream services

Troubleshooting

Detailed troubleshooting steps were moved to references/troubleshooting.md. Load it on demand to keep the main skill concise.

Reference Files

  • prompts/performance-testing_EN.md — Performance testing Standard-version prompt
  • output-formats.md — Markdown / Excel / CSV / JSON request instructions
  • examples/k6-load-testing/ — Complete K6 example
  • quick-start.md — 5-minute quick start guide

Related skills: api-testing-en, automation-testing-en, test-strategy-en, test-reporting-en.

Target Audience

  • QA engineers and developers executing this testing domain in real projects
  • Team leads who need structured, reproducible testing outputs
  • AI users who need fast, format-ready deliverables for execution and reporting

Not Recommended For

  • Pure production incident response without test scope/context
  • Decisions requiring legal/compliance sign-off without expert review
  • Requests lacking minimum inputs (scope, environment, expected behavior)

Critical Success Factors

  • Provide clear scope, environment, and acceptance criteria before generation
  • Validate generated outputs against real system constraints before execution
  • Keep artifacts traceable (requirements -> test points -> defects -> decisions)

Output Templates and Parsing Scripts

  • Template directory: output-templates/

- template-word.md (Word-friendly structure) - template-excel.tsv (Excel paste-ready) - template-xmind.md (XMind-friendly outline) - template-json.json - template-csv.csv - template-markdown.md

  • Parser scripts directory: scripts/

- Parse (generic): parse_output_formats.py - Parse (per-format): parse_word.py, parse_excel.py, parse_xmind.py, parse_json.py, parse_csv.py, parse_markdown.py - Convert (generic): convert_output_formats.py - Convert (per-format): convert_to_word.py, convert_to_excel.py, convert_to_xmind.py, convert_to_json.py, convert_to_csv.py, convert_to_markdown.py - Batch convert: batch_convert_templates.py (outputs into artifacts/)

Examples:

python3 scripts/parse_json.py output-templates/template-json.json
python3 scripts/parse_markdown.py output-templates/template-markdown.md
python3 scripts/convert_to_json.py output-templates/template-markdown.md
python3 scripts/convert_output_formats.py output-templates/template-json.json --to csv
python3 scripts/batch_convert_templates.py --skip-same

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

36.2%
按下载量换算33

Claude

30.56%
按下载量换算28

Cursor

16.71%
按下载量换算15

Gemini CLI

7.87%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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

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