aj-geddes/useful-ai-prompts

property-based-testing

Design property-based tests that verify code properties hold for all inputs using automatic test case generation. Use for property-based, QuickCheck, hypothesis testing, generative testing, and invariant verification.

First seen Jan 21, 2026

Installation

$ npx skills add aj-geddes/useful-ai-prompts --skill property-based-testing

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from aj-geddes/useful-ai-prompts · top by installs.

npx skills add aj-geddes/useful-ai-prompts

Browse all from aj-geddes/useful-ai-prompts

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 334
License LICENSE
Default branch main
Open issues 1
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,144 B
  • docs SUMMARY.md 3,000 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 434 installs

SKILL.md

Property-Based Testing

Table of Contents

  • [Overview](#overview)
  • [When to Use](#when-to-use)
  • [Quick Start](#quick-start)
  • [Reference Guides](#reference-guides)
  • [Best Practices](#best-practices)

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]

class TestStringOperations:
    @given(st.text())
    def test_reverse_twice_returns_original(self, s):
        """Property: Reversing twice returns the original string."""
        assert reverse_string(reverse_string(s)) == s

    @given(st.text())
    def test_reverse_length_unchanged(self, s):
        """Property: Reverse doesn't change length."""
        assert len(reverse_string(s)) == len(s)

    @given(st.text(min_size=1))
    def test_reverse_first_becomes_last(self, s):
        """Property: First char becomes last after reverse."""
        reversed_s = reverse_string(s)
        assert s[0] == reversed_s[-1]
        assert s[-1] == reversed_s[0]
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
[Hypothesis for Python](references/hypothesis-for-python.md) Hypothesis for Python
[fast-check for JavaScript/TypeScript](references/fast-check-for-javascripttypescript.md) fast-check for JavaScript/TypeScript
[junit-quickcheck for Java](references/junit-quickcheck-for-java.md) junit-quickcheck for Java

Best Practices

✅ DO

  • Focus on general properties, not specific cases
  • Test mathematical properties (commutativity, associativity)
  • Verify round-trip encoding/decoding
  • Use shrinking to find minimal failing cases
  • Combine with example-based tests for known edge cases
  • Test invariants that should always hold
  • Generate realistic input distributions

❌ DON'T

  • Test properties that are tautologies
  • Over-constrain input generation
  • Ignore shrunk test failures
  • Replace all example tests with properties
  • Test implementation details
  • Generate invalid inputs without constraints
  • Forget to handle edge cases in generators