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manastalukdar
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seed-data

Generate realistic seed data for Prisma, TypeORM, Django, SQLAlchemy, and Sequelize.

1|Updated Jan 26, 2026
Search Tags:#testing#database#orm#data generation#seed data#faker
One-click install
npx skills add https://github.com/manastalukdar/claude-devstudio --skill seed-data-manastalukdar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seed-data
Source: https://github.com/manastalukdar/claude-devstudio/tree/main/skills/seed-data
Command: npx skills add https://github.com/manastalukdar/claude-devstudio --skill seed-data-manastalukdar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the generation of realistic seed and fixture data for your database, ensuring your development and testing environments are populated with meaningful, relationship-aware data.

Core Features & Use Cases

  • ORM Integration: Supports Prisma, TypeORM, Django, SQLAlchemy, and Sequelize.
  • Realistic Data: Leverages Faker.js/Faker Python for diverse and believable data points (names, emails, dates, etc.).
  • Relationship Handling: Generates data that respects foreign key constraints and relationships between models.
  • Volume Control: Allows specifying data volume (small, medium, large) to suit different testing needs.
  • Use Case: Populate your new e-commerce application's database with 100 users, 500 products, and 1000 orders, complete with realistic customer details and purchase histories, to test the checkout flow.

Quick Start

Use the seed-data skill to generate medium volume seed data for your project.

Frequently Asked Questions about seed-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate realistic seed data for Prisma and TypeORM databases?▼

Yes, this Skill supports Django and SQLAlchemy, analyzing your database schemas to create fixture data that maintains proper relationships. It automatically respects foreign key constraints to ensure your testing environments have meaningful data.

Can I control the volume of fixture data generated for testing?▼

Yes, you can control the fixture data volume by specifying small, medium, or large datasets. This allows you to tailor the generated seed data to suit different testing needs, from simple unit tests to complex performance scenarios.

Does the generated seed data respect foreign key constraints and model relationships?▼

This Skill stands out by automatically analyzing database schemas to generate relationship-aware fixture data using Faker libraries. Unlike manual data generation, it seamlessly integrates with ORMs like Prisma, TypeORM, Django, SQLAlchemy, and Sequelize to maintain constraints.

What is the best way to populate an e-commerce database with test users and orders?▼

The best way to populate an e-commerce database is using this Skill to generate realistic test data. It can create hundreds of users, products, and orders complete with customer details and purchase histories to effectively test your checkout flow.

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