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

• Open the relevant file in this directory's prompts/ and copy the content below the dashed line.

• Append your requirements and context (business flow, environment, constraints, acceptance criteria).

• 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

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

Test Scenario Design

• Based on real user behavior

• Reasonable think time

• Gradually increase load

• Include warm-up and cool-down phases

Performance Metrics

• Response Time

• Throughput/RPS

• Error Rate

• Concurrent Users

• Resource Usage (CPU, Memory, Network)

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