How does it handle edge case testing?

The generator automatically injects rows containing null values (where allowed), empty strings, and minimum/maximum boundary values for numerical and date fields.

What data types does the Test Fixture Generator support?

It supports a wide range of data types including integers, floats, strings, dates, booleans, and enums, all customizable via a YAML schema.

What format is the generated data in?

The skill produces a Pandas DataFrame, making it easy to export your test fixtures directly to CSV, JSON, Parquet, or SQL databases.

Can I generate specific string formats like emails?

Yes, the skill includes built-in pattern support for common data formats like email addresses and phone numbers to ensure realistic testing.

Is the schema definition complex?

No, it uses a simple YAML format where you define column names, types, and constraints like min/max values or nullability.

Test Fixture Generator

Name: Test Fixture Generator
Author: majesticlabs-dev

bymajesticlabs-dev

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Security & Testing

Generates high-fidelity synthetic test data and edge cases for robust ETL pipeline and database testing.

About

This skill automates the creation of comprehensive test datasets based on schema specifications, specifically designed for ETL pipelines and data-driven applications. It intelligently generates various data types—including integers, floats, strings, dates, and enums—while automatically injecting critical edge cases like null values, boundary limits, and empty strings. By providing realistic yet synthetic data, it helps developers and data engineers identify potential pipeline failures and data validation issues before they reach production environments.

Key Features

Configurable data patterns for specialized formats like email and phone numbers
Seamless integration with Pandas for easy export to CSV or SQL formats
Automatic edge case injection for boundary and limit testing
Schema-driven data generation for multiple types including integers, floats, and dates
Nullable field simulation to test data integrity and constraints
19 GitHub stars

Use Cases

Stress-testing database schemas with large-scale synthetic datasets
Generating mock data for frontend UI development and API testing
Validating ETL pipeline transformations with boundary-value datasets

About

Key Features

Configurable data patterns for specialized formats like email and phone numbers
Seamless integration with Pandas for easy export to CSV or SQL formats
Automatic edge case injection for boundary and limit testing
Schema-driven data generation for multiple types including integers, floats, and dates
Nullable field simulation to test data integrity and constraints
19 GitHub stars

Use Cases

Stress-testing database schemas with large-scale synthetic datasets
Generating mock data for frontend UI development and API testing
Validating ETL pipeline transformations with boundary-value datasets