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

2. Test Scenario Design

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

3. Performance Metrics

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

4. Threshold Setting

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

Tool Selection Guide

Tool	Use Case	Advantages
K6	Modern performance testing	Scriptable, easy to use, cloud-native
JMeter	Traditional performance testing	Feature-rich, GUI, many plugins
Gatling	Scala/Java projects	High performance, beautiful reports
Locust	Python projects	Easy to learn, distributed
Artillery	Node.js projects	Simple 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