Property Based Testing

This generates random inputs to verify your code satisfies general properties rather than just testing handpicked examples. It's great for catching edge cases you'd never think to test manually, like testing that reversing a string twice returns the original or that sorting preserves length. The skill covers Hypothesis for Python, fast-check for JavaScript, and junit-quickcheck for Java. The real value is in the shrinking: when a test fails, these tools automatically minimize the failing input to show you the simplest case that breaks your code. Works best alongside traditional tests, not as a replacement, and you need to think carefully about what properties actually matter rather than just restating your implementation.

skills.sh installs: 454
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill property-based-testing --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Property-based testing verifies that code satisfies general properties or invariants for a wide range of automatically generated inputs, rather than testing specific examples. This approach finds edge cases and bugs that example-based tests often miss.

When to Use

Testing algorithms with mathematical properties
Verifying invariants that should always hold
Finding edge cases automatically
Testing parsers and serializers (round-trip properties)
Validating data transformations
Testing sorting, searching, and data structure operations
Discovering unexpected input combinations

Quick Start

Minimal working example:

# test_string_operations.py
import pytest
from hypothesis import given, strategies as st, assume, example

def reverse_string(s: str) -> str:
    """Reverse a string."""
    return s[::-1]

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