generating-database-seed-data

name	generating-database-seed-data
description	Process this skill enables AI assistant to generate realistic test data and database seed scripts for development and testing environments. it uses faker libraries to create realistic data, maintains relational integrity, and allows configurable data volumes. u... Use when working with databases or data models. Trigger with phrases like 'database', 'query', or 'schema'.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	1.22.0
author	Jeremy Longshore <[e-mail]>
license	MIT
tags	["database","testing","database-seed"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Data Seeder Generator

Overview

Generate realistic database seed scripts that populate development and testing environments with representative data. This skill creates seed data that respects foreign key relationships, unique constraints, check constraints, and data type validations using Faker libraries (faker.js, Faker for Python, or raw SQL with random functions).

Prerequisites

• Database schema definition (SQL DDL, ORM models, or Prisma schema) to understand table structures
• Target database connection for schema introspection (optional, can work from DDL files)
• Faker library available: @faker-js/faker (Node.js), faker (Python), or Bogus (.NET)
• Knowledge of referential integrity constraints (foreign keys, cascades)
• Target data volume per table (e.g., 100 users, 1000 orders, 5000 line items)

Instructions

1. Analyze the database schema to catalog all tables, columns, data types, constraints, and foreign key relationships. Build a dependency graph where parent tables (referenced by foreign keys) must be seeded before child tables.

2. Determine the seeding order by topologically sorting the dependency graph. Tables with no foreign keys are seeded first (users, categories, products), then tables referencing them (orders, reviews), then junction tables and deeply nested tables last.

3. Map each column to an appropriate Faker generator based on column name and data type:

   • first_name, last_name -> faker.person.firstName(), faker.person.lastName()
   • email -> faker.internet.email() with unique enforcement
   • phone -> faker.phone.number()
   • address, city, state, zip -> faker.location.*
   • created_at, updated_at -> faker.date.between({ from: '2023-01-01', to: '2024-12-31' })
   • price, amount -> faker.commerce.price({ min: 1, max: 999 })
   • description, bio -> faker.lorem.paragraph()
   • status -> Random selection from CHECK constraint values or enum values