Is the generated data reproducible for regression testing?

By using seed values, you can generate the exact same dataset multiple times, ensuring consistency across different testing runs.

Can I generate data based on my specific database schema?

Yes, you can provide a custom schema or data model to the skill to ensure the generated test data perfectly matches your application's requirements.

Does this skill support localized data like regional addresses?

Yes, you can specify desired locales to generate data that respects regional variations in names, phone numbers, and address formats.

What file formats does the generator support?

The skill typically provides data in structured formats like JSON, but it can also be used to create files ready for direct database insertion via Bash tools.

Test Data Generator

Name: Test Data Generator
Author: intent-solutions-io

byintent-solutions-io

Security & Testing

Generates realistic, schema-compliant test data to populate databases and simulate user behavior for comprehensive software testing.

About

The Test Data Generator skill empowers developers to streamline testing and development workflows by producing diverse, high-quality datasets tailored to specific requirements. Whether you need thousands of realistic user profiles, complex product catalogs, or simulated business transactions, this skill leverages the test-data-generator plugin to create structured data in formats like JSON. It is ideal for populating staging environments, creating fixtures for automated tests, and generating reproducible sample data for demonstrations, ensuring your applications are tested against realistic scenarios.

Key Features

Support for custom schema definitions to ensure data model consistency
Automated generation of realistic user, product, and order records
Locale-specific data generation for regional names and addresses
Direct output formatting for JSON and database insertion scripts
0 GitHub stars
Seed value support for creating reproducible, consistent datasets

Use Cases

Populating a development database with hundreds of unique user records for UI testing
Creating large-scale transaction histories to perform load and stress testing
Generating realistic mock data for client demonstrations and training environments

About

Key Features

Support for custom schema definitions to ensure data model consistency
Automated generation of realistic user, product, and order records
Locale-specific data generation for regional names and addresses
Direct output formatting for JSON and database insertion scripts
0 GitHub stars
Seed value support for creating reproducible, consistent datasets

Use Cases

Populating a development database with hundreds of unique user records for UI testing
Creating large-scale transaction histories to perform load and stress testing
Generating realistic mock data for client demonstrations and training environments