seed-data-generator

Seed Data Generator Skill

Generate realistic test data for database development, testing, and demos.

Instructions

You are a test data generation expert. When invoked:

1. Analyze Schema:

   • Identify tables and relationships
   • Understand column types and constraints
   • Detect foreign key dependencies
   • Recognize data patterns (email, phone, dates, etc.)

2. Generate Realistic Data:

   • Use faker libraries for realistic data
   • Maintain referential integrity
   • Follow business logic constraints
   • Create diverse but realistic scenarios

3. Seed Database:

   • Insert data in correct order (respect foreign keys)
   • Handle different database systems
   • Provide both SQL and ORM-based seeders
   • Support incremental seeding

4. Customize Generation:

   • Allow quantity specification
   • Support different data scenarios (edge cases, happy path)
   • Enable data relationships customization
   • Provide reproducible seeds (with random seed values)

Supported Tools

• JavaScript/TypeScript: Faker.js, Chance.js, Casual
• Python: Faker, Factory Boy, Mimesis
• Ruby: Faker, FactoryBot
• Raw SQL: Generate INSERT statements
• ORMs: Prisma, TypeORM, Sequelize, Django, Rails

Usage Examples

@seed-data-generator
@seed-data-generator --count 100
@seed-data-generator --table users
@seed-data-generator --scenario e-commerce
@seed-data-generator --realistic-relationships

SQL Seed Data

PostgreSQL - Basic Insert

-- seed/001_users.sql
INSERT INTO users (username, email, password_hash, active, created_at)
VALUES
  ('john_doe', '[email protected]', '$2b$10$...', true, '2024-01-15 10:00:00'),
  ('jane_smith', '[email protected]', '$2b$10$...', true, '2024-01-16 11:30:00'),
  ('bob_wilson', '[email protected]', '$2b$10$...', true, '2024-01-17 09:15:00'),
  ('alice_brown', '[email protected]', '$2b$10$...', false, '2024-01-18 14:45:00'),
  ('charlie_davis', '[email protected]', '$2b$10$...', true, '2024-01-19 16:20:00');

-- seed/002_categories.sql
INSERT INTO categories (name, slug, parent_id)
VALUES
  ('Electronics', 'electronics', NULL),
  ('Computers', 'computers', 1),
  ('Laptops', 'laptops', 2),
  ('Desktops', 'desktops', 2),
  ('Accessories', 'accessories', 1),
  ('Clothing', 'clothing', NULL),
  ('Men', 'men', 6),
  ('Women', 'women', 6);

-- seed/003_products.sql
INSERT INTO products (name, description, price, stock_quantity, category_id, created_at)
VALUES
  (
    'MacBook Pro 16"',
    'Powerful laptop with M3 chip, 16GB RAM, 512GB SSD',
    2499.99,
    15,
    3,
    NOW()
  ),
  (
    'Dell XPS 13',
    'Compact laptop with Intel i7, 16GB RAM, 512GB SSD',
    1299.99,
    20,
    3,
    NOW()
  ),
  (
    'Gaming Desktop',
    'High-performance desktop with RTX 4080, 32GB RAM',
    2999.99,
    8,
    4,
    NOW()
  ),
  (
    'Wireless Mouse',
    'Ergonomic wireless mouse with precision tracking',
    29.99,
    100,
    5,
    NOW()
  ),
  (
    'Mechanical Keyboard',
    'RGB mechanical keyboard with Cherry MX switches',
    149.99,
    45,
    5,
    NOW()
  );

-- seed/004_orders.sql
INSERT INTO orders (user_id, total_amount, status, created_at)
VALUES
  (1, 2529.98, 'completed', '2024-01-20 10:30:00'),
  (2, 1299.99, 'completed', '2024-01-21 14:15:00'),
  (3, 179.98, 'processing', '2024-01-22 09:45:00'),
  (1, 2999.99, 'pending', '2024-01-23 16:00:00'),
  (4, 29.99, 'completed', '2024-01-24 11:20:00');

-- seed/005_order_items.sql
INSERT INTO order_items (order_id, product_id, quantity, price)
VALUES
  -- Order 1
  (1, 1, 1, 2499.99),
  (1, 4, 1, 29.99),
  -- Order 2
  (2, 2, 1, 1299.99),
  -- Order 3
  (3, 4, 1, 29.99),
  (3, 5, 1, 149.99),
  -- Order 4
  (4, 3, 1, 2999.99),
  -- Order 5
  (5, 4, 1, 29.99);