Data Seeding Skill

SkillDatabases & data

Create or update database seed scripts for development and testing environments. Use when setting up test data, initializing development databases, creating demo environments, resetting to known state, or generating realistic sample data.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Data Seeding Skill skill

What this skill tells your AI

The instructions your AI receives, as published by motormetrics/motormetrics in .agents/skills/data-seeding/SKILL.md and read by ahel’s review.

Seed scripts live in packages/database/src/seed/.

Running Seeds

pnpm -F @sgcarstrends/database db:seed           # Run all seeds
pnpm -F @sgcarstrends/database db:seed:cars      # Seed specific table

Basic Seed Pattern

// packages/database/src/seed/cars.ts
import { db } from "../index";
import { cars } from "../db/schema";
import { nanoid } from "nanoid";

export async function seedCars() {
  console.log("Seeding cars...");

  const carData = [
    { id: nanoid(), make: "Toyota", model: "Camry", vehicleClass: "Sedan", fuelType: "Petrol", month: "2024-01", number: 150 },
    { id: nanoid(), make: "Honda", model: "Civic", vehicleClass: "Sedan", fuelType: "Petrol", month: "2024-01", number: 120 },
  ];

  await db.insert(cars).values(carData);
  console.log(`Seeded ${carData.length} cars`);
}

Main Seed Runner

// packages/database/src/seed/index.ts
export async function seed() {
  console.log("Starting database seed...");

  await clearDatabase();  // Optional: clear existing data
  await seedCars();
  await seedCOE();
  await seedPosts();

  console.log("Database seeded successfully!");
}

async function clearDatabase() {
  // Delete in reverse order of dependencies
  await db.delete(posts);
  await db.delete(coe);
  await db.delete(cars);
}

Seed with Faker.js

pnpm -F @sgcarstrends/database add -D @faker-js/faker
import { faker } from "@faker-js/faker";

export async function seedRealisticCars(count = 100) {
  const makes = ["Toyota", "Honda", "BMW", "Mercedes"];
  const carData = Array.from({ length: count }, () => ({
    id: nanoid(),
    make: faker.helpers.arrayElement(makes),
    model: faker.vehicle.model(),
    month: faker.date.between({ from: "2020-01-01", to: "2024-12-31" }).toISOString().slice(0, 7),
    number: faker.number.int({ min: 10, max: 500 }),
  }));

  // Batch insert for performance
  const batchSize = 50;
  for (let i = 0; i < carData.length; i += batchSize) {
    await db.insert(cars).values(carData.slice(i, i + batchSize));
  }
}

Environment-Specific Seeds

export async function seed() {
  const env = process.env.NODE_ENV || "development";

  switch (env) {
    case "development":
      await seedDevelopment();  // Small, predictable dataset
      break;
    case "test":
      await seedTesting();  // Minimal, deterministic data
      break;
    case "staging":
      await seedStaging();  // Larger, production-like dataset
      break;
  }
}

Idempotent Seeds (Upsert)

await db.insert(cars).values(carData).onConflictDoUpdate({
  target: cars.id,
  set: { make: carData[0].make, number: carData[0].number, updatedAt: new Date() },
});

Check Before Insert

export async function seedIfEmpty() {
  const existing = await db.select().from(cars).limit(1);
  if (existing.length > 0) {
    console.log("Database has data, skipping seed");
    return;
  }
  await seedCars();
}

Transactions

await db.transaction(async (tx) => {
  await tx.insert(cars).values([...carData]);
  await tx.insert(coe).values([...coeData]);
});

CLI for Selective Seeding

// packages/database/scripts/seed-cli.ts
const seeders = { cars: seedCars, coe: seedCOE, posts: seedPosts, all: seedAll };
const target = process.argv[2] as keyof typeof seeders;

if (!target || !seeders[target]) {
  console.error("Usage: pnpm db:seed [cars|coe|posts|all]");
  process.exit(1);
}

seeders[target]().then(() => process.exit(0)).catch(() => process.exit(1));

Best Practices

  1. Idempotent: Safe to run multiple times (use upserts or check-before-insert)
  2. Environment-Specific: Different data for dev/test/staging
  3. Batch Inserts: Use batching for large datasets
  4. Relationships: Seed parent tables first
  5. Transactions: Use for atomic seeding
  6. Logging: Provide clear progress feedback

References

  • packages/database/AGENTS.md for schema details
  • See schema-design skill for migrations

Signals

GitHub stars
22
Forks
2
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
data-seeding
Source
github.com/motormetrics/motormetrics