Db Seed

# Database Seed Generator

Generate seed scripts that populate databases with realistic, domain-appropriate sample data. Reads your schema and produces ready-to-run seed files.

## Workflow

### 1. Find the Schema

Scan the project for schema definitions:

| Source | Location pattern |
|--------|-----------------|
| Drizzle schema | `src/db/schema.ts`, `src/schema/*.ts`, `db/schema.ts` |
| D1 migrations | `drizzle/*.sql`, `migrations/*.sql` |
| Raw SQL | `schema.sql`, `db/*.sql` |
| Prisma | `prisma/schema.prisma` |

Read all schema files. Build a mental model of:
- Tables and their columns
- Data types and constraints (NOT NULL, UNIQUE, DEFAULT)
- Foreign key relationships (which tables reference which)
- JSON fields stored as TEXT (common in D1/SQLite)

### 2. Determine Seed Parameters

Ask the user:

| Parameter | Options | Default |
|-----------|---------|---------|
| Purpose | dev, demo, testing | dev |
| Volume | small (5-10 rows/table), medium (20-50), large (100+) | small |
| Domain context | "e-commerce store", "SaaS app", "blog", etc. | Infer from schema |
| Output format | TypeScript (Drizzle), raw SQL, or both | Match project's ORM |

**Purpose affects data quality**:
- **dev**: Varied data, some edge cases (empty fields, long strings, unicode)
- **demo**: Polished data that looks good in screenshots and presentations
- **testing**: Systematic data covering boundary conditions, duplicates, special characters

### 3. Plan Insert Order

Build a dependency graph from foreign keys. Insert parent tables before children.

Example order for a blog schema:
```
1. users        (no dependencies)
2. categories   (no dependencies)
3. posts        (depends on users, categories)
4. comments     (depends on users, posts)
5. tags         (no dependencies)
6. post_tags    (depends on posts, tags)
```

**Circular dependencies**: If table A references B and B references A, use nullable foreign keys and insert in two passes (insert with NULL, then UPDATE).

### 4. Generate Realistic Data

**Do NOT use generic placeholders** like "test123", "foo@bar.com", or "Lorem ipsum". Generate data that matches the domain.

#### Data Generation Patterns (no external libraries needed)

**Names**: Use a hardcoded list of common names. Mix genders and cultural backgrounds.
```typescript
const firstNames = ['Sarah', 'James', 'Priya', 'Mohammed', 'Emma', 'Wei', 'Carlos', 'Aisha'];
const lastNames = ['Chen', 'Smith', 'Patel', 'Garcia', 'Kim', 'O\'Brien', 'Nguyen', 'Wilson'];
```

**Emails**: Derive from names — `sarah.chen@example.com`. Use `example.com` domain (RFC 2606 reserved).

**Dates**: Generate within a realistic range. Use ISO 8601 format for D1/SQLite.
```typescript
const randomDate = (daysBack: number) => {
  const d = new Date();
  d.setDate(d.getDate() - Math.floor(Math.random() * daysBack));
  return d.toISOString();
};
```

**IDs**: Use `crypto.randomUUID()` for UUIDs, or sequential integers if the schema uses auto-increment.

**Deterministic seeding**: For reproducible data, use a seeded PRNG:
```typescript
function seededRandom(seed: number) {
  return () => {
    seed = (seed * 16807) % 2147483647;
    return (seed - 1) / 2147483646;
  };
}
const rand = seededRandom(42); // Same seed = same data every time
```

**Prices/amounts**: Use realistic ranges. `(rand() * 900 + 100).toFixed(2)` for $1-$10 range.

**Descriptions/content**: Write 3-5 realistic variations per content