Can I ensure the generated data is the same every time?

By utilizing seed values, you can ensure reproducible results, which is critical for consistent automated testing across different development environments.

Does the skill support international addresses and names?

Yes, you can specify locale considerations to ensure names, addresses, and phone numbers are appropriate for specific regional variations.

Can I generate data based on my own database schema?

Yes, you can provide a specific schema or data model, and the skill will generate consistent data that adheres to your defined structure and requirements.

Is it possible to create large datasets for load testing?

Absolutely. The skill can generate hundreds or thousands of records, such as users or orders, to help simulate real-world traffic and stress-test your application.

Realistic Test Data Generator

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

byintent-solutions-io

Seguridad y Pruebas

Generates high-fidelity mock data including users, products, and custom schemas to streamline software testing and database population.

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The Realistic Test Data Generator skill empowers Claude to produce diverse, schema-compliant datasets for development and QA environments. By leveraging specialized plugins, it can automatically create realistic user profiles, e-commerce transactions, and complex business records that adhere to specific data models. This skill is essential for developers needing to perform load testing, populate staging databases, or create consistent fixtures for automated test suites without manually writing tedious JSON or SQL scripts.

Características Principales

0 GitHub stars
Bulk data creation for performance and load testing scenarios
Seed value support for reproducible datasets across test runs
Automated generation of realistic user profiles with valid contact details
Custom schema support for specialized business data models
Locale-specific generation for internationalization (i18n) testing

Casos de Uso

Populating staging databases with realistic sample data for stakeholder demonstrations
Creating large-scale datasets to test application performance and database indexing
Generating mock JSON payloads for API development and automated unit testing

Acerca de

Características Principales

0 GitHub stars
Bulk data creation for performance and load testing scenarios
Seed value support for reproducible datasets across test runs
Automated generation of realistic user profiles with valid contact details
Custom schema support for specialized business data models
Locale-specific generation for internationalization (i18n) testing

Casos de Uso

Populating staging databases with realistic sample data for stakeholder demonstrations
Creating large-scale datasets to test application performance and database indexing
Generating mock JSON payloads for API development and automated unit testing