YAML
---
name: generating-test-data
description: Generates test data and placeholder content for development and testing purposes. Use when you need sample data, mock content, or testing scenarios.
---

Quick Start

Python
# Generate test user data
users = [
    {"id": i, "name": f"TestUser{i}", "email": f"test{i}@example.com"} 
    for i in range(1, 11)
]

Workflow

1. Identify data type - Determine what kind of test data is needed
2. Set parameters - Define volume, format, and constraints
3. Generate base structure - Create the fundamental data pattern
4. Add variation - Introduce realistic diversity to avoid uniform data
5. Validate output - Ensure generated data meets requirements

Progress:

• Define test data requirements
• Choose generation method
• Create sample dataset
• Review for realistic variation
• Export in required format

Examples

Example 1: Input: "Need 5 test products" Output:

JSON
[
  {"id": 1, "name": "TestProduct A", "price": 29.99, "stock": 15},
  {"id": 2, "name": "TestProduct B", "price": 45.50, "stock": 8},
  {"id": 3, "name": "TestProduct C", "price": 12.99, "stock": 23}
]

Example 2: Input: "Generate test API responses" Output:

JSON
{"status": "success", "data": {"message": "Test operation completed"}, "timestamp": "2024-01-15T10:30:00Z"}

Best Practices

• Use consistent naming patterns (TestUser1, TestUser2)
• Include edge cases (empty strings, null values, boundary conditions)
• Make data realistic enough for meaningful testing
• Use standard test domains (example.com, test.local)
• Include both valid and invalid test cases

Common Pitfalls

• Don't use real personal data for testing
• Avoid completely random data that lacks realistic patterns
• Don't generate too much data that slows down tests
• Avoid hardcoded test data that becomes stale