How does it handle test isolation?

The skill includes strategies for cleaning up data between runs, such as database truncation and transaction management, ensuring tests do not interfere with each other.

What is test data management in Claude Code?

It is a specialized skill that helps AI-assisted development by providing patterns to generate and maintain realistic datasets for unit, integration, and E2E tests.

Does this skill support both Python and TypeScript?

Yes, it provides idiomatic implementation patterns and code examples for both Python (using FactoryBoy and Pytest) and TypeScript (using Faker).

Can I use this for complex relational data?

Absolutely. It covers factory composition and sub-factories, allowing you to automatically create complex, multi-table object graphs with a single command.

Test Data Management

Name: Test Data Management
Author: yonatangross

byyonatangross

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Security & Testing

Simplifies the creation and maintenance of robust test datasets through factories, fixtures, and automated seeding strategies.

About

Test Data Management provides a comprehensive framework for handling complex data requirements in automated testing across Python and TypeScript environments. It moves beyond brittle, hard-coded values by implementing the Factory Pattern, managing reusable JSON fixtures, and establishing reliable database seeding workflows. By focusing on test isolation and realistic synthetic data generation via tools like Faker and FactoryBoy, this skill helps developers eliminate flaky tests and simulate real-world scenarios with minimal boilerplate code.

Key Features

Management and integration of reusable, version-controlled JSON fixtures
29 GitHub stars
Realistic synthetic data generation for complex, nested object graphs
Implementation of the Factory Pattern for Python (FactoryBoy) and TypeScript (Faker)
Automated database seeding for consistent local and CI/CD test environments
Comprehensive test isolation strategies including transaction rollbacks and cleanup

Use Cases

Setting up complex user states and related entities for end-to-end workflows
Generating large, realistic datasets for performance and integration testing
Ensuring clean database states between test runs to prevent state pollution

About

Key Features

Management and integration of reusable, version-controlled JSON fixtures
29 GitHub stars
Realistic synthetic data generation for complex, nested object graphs
Implementation of the Factory Pattern for Python (FactoryBoy) and TypeScript (Faker)
Automated database seeding for consistent local and CI/CD test environments
Comprehensive test isolation strategies including transaction rollbacks and cleanup

Use Cases

Setting up complex user states and related entities for end-to-end workflows
Generating large, realistic datasets for performance and integration testing
Ensuring clean database states between test runs to prevent state pollution