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.
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Installation Guide
How to use property-based-testing on Cursor
AI-first code editor with Composer
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
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches property-based-testing from aj-geddes/useful-ai-prompts and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
List & Monetize Your Skill
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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
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 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
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Related Skills
accessibility-testing
6aj-geddes/useful-ai-prompts
root-cause-analysis
8aj-geddes/useful-ai-prompts
rest-api-design
7aj-geddes/useful-ai-prompts
ansible-automation
4aj-geddes/useful-ai-prompts
prometheus-monitoring
3aj-geddes/useful-ai-prompts
query-caching-strategies
3aj-geddes/useful-ai-prompts
Reviews
- DDhruvi Jain★★★★★Dec 28, 2024
Useful defaults in property-based-testing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAanya Desai★★★★★Dec 16, 2024
I recommend property-based-testing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- OOshnikdeep★★★★★Nov 19, 2024
property-based-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- MMichael Thompson★★★★★Nov 7, 2024
Solid pick for teams standardizing on skills: property-based-testing is focused, and the summary matches what you get after install.
- AAanya Dixit★★★★★Nov 3, 2024
Registry listing for property-based-testing matched our evaluation — installs cleanly and behaves as described in the markdown.
- EEmma Jain★★★★★Oct 26, 2024
property-based-testing has been reliable in day-to-day use. Documentation quality is above average for community skills.
- MMichael Robinson★★★★★Oct 22, 2024
property-based-testing reduced setup friction for our internal harness; good balance of opinion and flexibility.
- GGanesh Mohane★★★★★Oct 10, 2024
Solid pick for teams standardizing on skills: property-based-testing is focused, and the summary matches what you get after install.
- AAma Chen★★★★★Sep 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.
- MMia Lopez★★★★★Sep 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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