ln-723-seed-data-generator

Convert ORM schemas or entity definitions into deterministic seed data in multiple formats.

Updated Mar 14, 2026
One-click install
npx skills add https://github.com/0xtaoist/taogate --skill ln-723-seed-data-generator-0xtaoist
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ln-723-seed-data-generator
Source: https://github.com/0xtaoist/taogate/tree/main/ln-723-seed-data-generator
Command: npx skills add https://github.com/0xtaoist/taogate --skill ln-723-seed-data-generator-0xtaoist

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Seed data generation from ORM schemas or entity definitions to enable realistic, ready-to-use data without migrations, scaffolding, or ORM models.

Core Features & Use Cases

  • MIGRATE: parse existing ORM schemas (Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, Django) to generate seed datasets.
  • GENERATE: create seed data from explicit entity definitions and relationships, output to C#, TypeScript, Python, JSON, or SQL.
  • Deterministic Seeding: uses a fixed seed to ensure reproducible results across runs.
  • Relationship Consistency: preserves foreign key relationships and generates parent-then-child data consistently.

Quick Start

Provide an ORM schema or an entity definition list and run the generator to produce seed data in your chosen target format.

Frequently Asked Questions about ln-723-seed-data-generator

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

FAQPage Schema
How do I generate seed data from existing ORM schemas?

To generate seed data from ORM schemas, provide your Drizzle, Prisma, TypeORM, EF Core, SQLAlchemy, or Django schema to use MIGRATE mode and produce ready-to-use datasets without requiring actual migrations.

Can I generate seed data without running database migrations?

Yes, you can generate seed data without migrations by using GENERATE mode to create datasets from explicit entity definitions, outputting directly to C#, TypeScript, Python, JSON, or SQL.

How does deterministic Faker-based seeding ensure reproducible results?

Deterministic Faker-based seeding uses a fixed seed value during data generation to guarantee that every run produces the exact same synthetic data, ensuring your seed datasets are fully reproducible.

Does the generator maintain foreign key relationships when creating seed data?

Yes, the generator maintains foreign key relationships by preserving FK constraints and consistently generating parent records before child records to ensure valid relationship consistency.

What is the best way to create seed data for multiple programming languages?

The best way to create seed data for multiple languages is to define your entities once and output the generated seed data concurrently to C#, TypeScript, Python, JSON, or SQL formats.

Do I need ORM models installed to use the seed data generator?

No, you do not need ORM models installed to use the seed data generator, as it processes schema definitions directly to produce ready-to-use data without scaffolding or ORM dependencies.