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.mdat 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.shTest 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