Can it handle custom data structures?

Yes, the skill can generate complex data strategies and arbitraries for custom objects, records, and nested collections specific to your codebase.

Which frameworks are supported by this skill?

The skill supports Python (Hypothesis/Pytest), JavaScript and TypeScript (fast-check with Jest or Vitest), and Haskell (QuickCheck).

Is stateful testing supported?

Yes, it includes patterns for stateful and model-based testing, allowing you to test complex systems where the order of operations matters.

What is property-based testing compared to unit testing?

While unit tests check specific examples (e.g., 2+2=4), property-based testing checks general rules (e.g., a+b should always equal b+a) against hundreds of randomized inputs to find failures.

Does it help with shrinking failing test cases?

Yes, it configures the testing libraries to use 'shrinking,' which automatically simplifies a complex failing input into the smallest possible example that still triggers the bug.

Property-Based Test Generator

Name: Property-Based Test Generator
Author: Dexploarer

byDexploarer

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Security & Testing

Generates robust property-based tests to uncover edge cases and validate logical invariants across Python, JavaScript, and Haskell projects.

About

Property-Based Test Generator is a sophisticated tool designed to move testing beyond simple input-output examples. By leveraging industry-standard libraries like Hypothesis, fast-check, and QuickCheck, it helps developers identify and test core logic invariants—such as idempotence, commutativity, and round-trip consistency. This skill automates the setup of randomized testing environments, generates complex data strategies, and creates tests that automatically search for edge cases, significantly improving code reliability and identifying bugs that traditional unit tests often miss.

Key Features

Generates templates for common property patterns like Round-trips and Idempotence
Automatic library installation and configuration guidance for various environments
Multi-language support for Python (Hypothesis), JS/TS (fast-check), and Haskell (QuickCheck)
2 GitHub stars
Automatic detection of project language and existing test infrastructure
Support for advanced data strategies including custom records, arrays, and stateful models

Use Cases

Ensuring business logic invariants remain true across a wide spectrum of valid and boundary data
Validating data serialization logic by ensuring encoded data correctly round-trips to the original state
Discovering elusive edge cases in complex algorithms through randomized input stress-testing

About

Key Features

Generates templates for common property patterns like Round-trips and Idempotence
Automatic library installation and configuration guidance for various environments
Multi-language support for Python (Hypothesis), JS/TS (fast-check), and Haskell (QuickCheck)
2 GitHub stars
Automatic detection of project language and existing test infrastructure
Support for advanced data strategies including custom records, arrays, and stateful models

Use Cases

Ensuring business logic invariants remain true across a wide spectrum of valid and boundary data
Validating data serialization logic by ensuring encoded data correctly round-trips to the original state
Discovering elusive edge cases in complex algorithms through randomized input stress-testing