Seed Data Generator Protocol

This skill helps developers populate empty local or staging databases with massive amounts of realistic data for load testing and UI development.

Core assumption: Simple random strings (asdfgh) are useless for UI testing. Seed data must look real and respect Foreign Key constraints to successfully insert.

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1. Schema Analysis & Topological Sort

Before generating data, read the schema and understand the relationships:

• If orders depends on users and products.
• If order_items depends on orders and products.
• Topological Sort (Insert Order): users -> products -> orders -> order_items.

(Never try to insert an order item before the order exists).

2. Smart Field Generation (Faking)

Map column names and data types to specific Faker generators:

• email -> Faker.Internet.Email()
• first_name, last_name, full_name -> Faker.Person.FullName()
• status (VARCHAR) -> Random pick from ('active', 'pending', 'cancelled').
• description, bio -> Faker.Lorem.Paragraph()
• created_at -> Random Timestamp between NOW() - 1 year and NOW().

3. Output Generation

Provide an executable seeder script (TypeScript/Prisma, Python, or raw SQL depending on the user's stack). Raw SQL is the default.

Required Outputs (Must write BOTH to docs/database-report/):

1. Human-Readable Markdown (docs/database-report/seed-data-report.md)

### 🔗 Dependency Graph Resolution
Insert Order:
1. `companies`
2. `users` (Depends on `companies`)
3. `posts` (Depends on `users`)

### 🛠️ Seed Script (Raw SQL)
```sql
-- Disable triggers temporarily for fast bulk inserts
SET session_replication_role = 'replica';

-- 1. Insert Companies
INSERT INTO companies (id, name, created_at) VALUES 
('c1', 'Acme Corp', '2023-01-15 10:00:00'),
('c2', 'Globex', '2023-02-20 11:30:00');

-- 2. Insert Users
INSERT INTO users (id, company_id, email, first_name) VALUES 
('u1', 'c1', 'john.acme@example.com', 'John'),
('u2', 'c2', 'sarah.globex@example.com', 'Sarah');

-- Re-enable triggers
SET session_replication_role = 'origin';

2. **Machine-Readable JSON (`docs/database-report/seed-data-output.json`)**
```json
{
  "skill": "seed-data-generator",
  "insertion_order": ["companies", "users", "posts"],
  "faker_mappings": {
    "users.email": "Faker.Internet.Email()",
    "companies.name": "Faker.Company.CompanyName()"
  },
  "rows_generated": {
    "companies": 2,
    "users": 2
  }
}

---

Guardrails

• Performance: For requesting >10,000 rows, do not output literal SQL INSERT statements. Instead, output a Python/Node script using faker and fast bulk COPY commands.
• Unique Constraints: Be extremely careful with random generators hitting duplicate values on UNIQUE columns. Append id or sequence numbers to emails/usernames if necessary.
• Environment: Warn the user to NEVER run seed scripts in production.