How does this skill maintain database relationships?

The skill analyzes your schema to identify foreign key constraints and ensures that generated data in child tables correctly references IDs in parent tables.

Can I specify the number of records to generate?

Yes, you can request specific volumes, such as 'generate 50 users' or 'populate the database with 1000 orders', and the skill will scale the generation accordingly.

What formats can the seed scripts be generated in?

It can generate raw SQL insert statements, JavaScript or TypeScript files for Node.js environments, and framework-specific scripts like TypeORM.

Is the generated data secure for production environments?

The skill uses Faker libraries to generate synthetic data, meaning no real user information is used, making it safe for development and staging environments.

Database Data Seeder

Name: Database Data Seeder
Author: intent-solutions-io

byintent-solutions-io

Gestión de Bases de Datos

Generates realistic, relationally-consistent test data and database seed scripts for development and testing environments.

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This skill automates the creation of high-quality sample data by analyzing your database schema and applying Faker-based logic to generate diverse, believable records. It ensures that foreign key relationships remain intact across multiple tables, allowing developers to quickly populate databases for local development, automated testing, or client demonstrations without compromising data structure integrity or manual entry.

Características Principales

Relational integrity maintenance across foreign keys
Configurable data volumes for performance and stress testing
Automated schema analysis for structure-aware generation
Integration with Faker libraries for realistic synthetic data
0 GitHub stars
Multi-format script generation including SQL and JavaScript

Casos de Uso

Populating local development environments with realistic user and product data
Creating idempotent seed scripts for automated CI/CD testing pipelines
Generating large datasets to demonstrate application features to stakeholders

Acerca de

Características Principales

Relational integrity maintenance across foreign keys
Configurable data volumes for performance and stress testing
Automated schema analysis for structure-aware generation
Integration with Faker libraries for realistic synthetic data
0 GitHub stars
Multi-format script generation including SQL and JavaScript

Casos de Uso

Populating local development environments with realistic user and product data
Creating idempotent seed scripts for automated CI/CD testing pipelines
Generating large datasets to demonstrate application features to stakeholders