property-based-testing

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.

aj-geddes/useful-ai-promptsUpdated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill property-based-testing

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Installation Guide

How to use property-based-testing on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add property-based-testing
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill property-based-testing

Fetches property-based-testing from aj-geddes/useful-ai-prompts and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/property-based-testing

Restart Cursor to activate property-based-testing. Access via /property-based-testing in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Property-Based Testing

Table of Contents

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 Hypothesis for Python
fast-check for JavaScript/TypeScript fast-check for JavaScript/TypeScript
junit-quickcheck for Java 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

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.626 reviews
  • D
    Dhruvi JainDec 28, 2024

    Useful defaults in property-based-testing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • A
    Aanya DesaiDec 16, 2024

    I recommend property-based-testing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • O
    OshnikdeepNov 19, 2024

    property-based-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • M
    Michael ThompsonNov 7, 2024

    Solid pick for teams standardizing on skills: property-based-testing is focused, and the summary matches what you get after install.

  • A
    Aanya DixitNov 3, 2024

    Registry listing for property-based-testing matched our evaluation — installs cleanly and behaves as described in the markdown.

  • E
    Emma JainOct 26, 2024

    property-based-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • M
    Michael RobinsonOct 22, 2024

    property-based-testing reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • G
    Ganesh MohaneOct 10, 2024

    Solid pick for teams standardizing on skills: property-based-testing is focused, and the summary matches what you get after install.

  • A
    Ama ChenSep 17, 2024

    Keeps context tight: property-based-testing is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • M
    Mia LopezSep 13, 2024

    I recommend property-based-testing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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