API Testing (English)

中文版： See skill api-testing .

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

When to Use

• User mentions API testing, api-testing

• Need to execute this testing type or produce deliverables per Standard-version

• Trigger examples: "Generate/design/write API test cases 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. Postman + Newman REST API Testing

Complete user management API test example with 10 test cases.

Location: examples/postman-rest-api/

Includes:

• Postman collection file (10 test cases)

• Environment variable configuration

• Newman automation run script

• Detailed README documentation

Quick Start:

cd examples/postman-rest-api npm install -g newman ./newman-run.sh

Test Coverage:

• User CRUD operations (Create, Read, Update, Delete)

• Authentication and authorization testing

• Error handling and boundary conditions

• Response time validation

• Data format validation

See: examples/postman-rest-api/README.md

2. REST Assured (Java)

Coming soon - Java-based REST API testing with REST Assured framework.

Location: examples/rest-assured-java/

3. SuperTest (Node.js)

Coming soon - Node.js API testing with SuperTest framework.

Location: examples/supertest-nodejs/

Best Practices

API Test Design Principles

Test Pyramid

• Unit tests: Test individual API endpoints

• Integration tests: Test interactions between multiple endpoints

• End-to-end tests: Test complete business workflows

Test Data Management

• Use environment variables for different environment configurations

• Use dynamic variables to avoid hardcoding

• Clean up data after tests

Assertion Strategy

• Verify status codes

• Verify response structure (Schema Validation)

• Verify response data

• Verify response time

Error Handling

• Test various error scenarios (4xx, 5xx)

• Verify error message format

• Test boundary conditions

Tool Selection Guide

Tool Use Case Advantages

Postman/Newman REST API testing Easy to use, visual, CI/CD integration

REST Assured Java projects Strong typing, BDD style

Pytest + Requests Python projects Flexible, rich ecosystem

SuperTest Node.js projects Good integration with Express

GraphQL Playground GraphQL API Designed specifically for GraphQL

Common Pitfalls

• ❌ Testing only happy paths → ✅ Cover validation, error handling, auth failures, and edge cases

• ❌ Weak contract checks → ✅ Assert status, schema, business fields, and backward compatibility

• ❌ Not isolating test data → ✅ Use deterministic seed data and clear teardown rules

• ❌ Ignoring environment parity → ✅ Validate base URL, auth config, and dependency versions per environment

Troubleshooting

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

Reference Files

• prompts/api-testing_EN.md — API testing Standard-version prompt

• output-formats.md — Markdown / Excel / CSV / JSON request instructions

• examples/postman-rest-api/ — Complete Postman + Newman example

• quick-start.md — 5-minute quick start guide

Related skills: automation-testing-en, performance-testing-en, security-testing-en, functional-testing-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