property-based-testing
Use when writing tests for serialization, validation, normalization, or pure functions - provides property catalog, pattern detection, and library reference for property-based testing
Best use case
property-based-testing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when writing tests for serialization, validation, normalization, or pure functions - provides property catalog, pattern detection, and library reference for property-based testing
Teams using property-based-testing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/property-based-testing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How property-based-testing Compares
| Feature / Agent | property-based-testing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when writing tests for serialization, validation, normalization, or pure functions - provides property catalog, pattern detection, and library reference for property-based testing
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Property-Based Testing ## Overview Property-based testing (PBT) generates random inputs and verifies that properties hold for all of them. Instead of testing specific examples, you test invariants. **When PBT beats example-based tests:** - Serialization pairs (encode/decode) - Pure functions with clear contracts - Validators and normalizers - Data structure operations ## Property Catalog | Property | Formula | When to Use | |----------|---------|-------------| | **Roundtrip** | `decode(encode(x)) == x` | Serialization, conversion pairs | | **Idempotence** | `f(f(x)) == f(x)` | Normalization, formatting, sorting | | **Invariant** | Property holds before/after | Any transformation | | **Commutativity** | `f(a, b) == f(b, a)` | Binary/set operations | | **Associativity** | `f(f(a,b), c) == f(a, f(b,c))` | Combining operations | | **Identity** | `f(x, identity) == x` | Operations with neutral element | | **Inverse** | `f(g(x)) == x` | encrypt/decrypt, compress/decompress | | **Oracle** | `new_impl(x) == reference(x)` | Optimization, refactoring | | **Easy to Verify** | `is_sorted(sort(x))` | Complex algorithms | | **No Exception** | No crash on valid input | Baseline (weakest) | **Strength hierarchy** (weakest to strongest): ``` No Exception -> Type Preservation -> Invariant -> Idempotence -> Roundtrip ``` Always aim for the strongest property that applies. ## Pattern Detection **Use PBT when you see:** | Pattern | Property | Priority | |---------|----------|----------| | `encode`/`decode`, `serialize`/`deserialize` | Roundtrip | HIGH | | `toJSON`/`fromJSON`, `pack`/`unpack` | Roundtrip | HIGH | | Pure functions with clear contracts | Multiple | HIGH | | `normalize`, `sanitize`, `canonicalize` | Idempotence | MEDIUM | | `is_valid`, `validate` with normalizers | Valid after normalize | MEDIUM | | Sorting, ordering, comparators | Idempotence + ordering | MEDIUM | | Custom collections (add/remove/get) | Invariants | MEDIUM | | Builder/factory patterns | Output invariants | LOW | ## When NOT to Use - Simple CRUD without transformation logic - UI/presentation logic - Integration tests requiring complex external setup - Code with side effects that cannot be isolated - Prototyping where requirements are fluid - Tests where specific examples suffice and edge cases are understood ## Library Quick Reference | Language | Library | Import | |----------|---------|--------| | Python | Hypothesis | `from hypothesis import given, strategies as st` | | TypeScript/JS | fast-check | `import fc from 'fast-check'` | | Rust | proptest | `use proptest::prelude::*` | | Go | rapid | `import "pgregory.net/rapid"` | | Java | jqwik | `@Property` annotations | | Haskell | QuickCheck | `import Test.QuickCheck` | **For library-specific syntax and patterns:** Use `@ed3d-research-agents:internet-researcher` to get current documentation. ## Input Strategy Best Practices 1. **Constrain early:** Build constraints INTO the strategy, not via `assume()` ```python # GOOD st.integers(min_value=1, max_value=100) # BAD - high rejection rate st.integers().filter(lambda x: 1 <= x <= 100) ``` 2. **Size limits:** Prevent slow tests ```python st.lists(st.integers(), max_size=100) st.text(max_size=1000) ``` 3. **Realistic data:** Match real-world constraints ```python st.integers(min_value=0, max_value=150) # Real ages, not arbitrary ints ``` 4. **Reuse strategies:** Define once, use across tests ```python valid_users = st.builds(User, ...) @given(valid_users) def test_one(user): ... @given(valid_users) def test_two(user): ... ``` ## Settings Guide ```python # Development (fast feedback) @settings(max_examples=10) # CI (thorough) @settings(max_examples=200) # Nightly/Release (exhaustive) @settings(max_examples=1000, deadline=None) ``` ## Quality Checklist Before committing PBT tests: - [ ] Not tautological (assertion doesn't compare same expression) - [ ] Strong assertion (not just "no crash") - [ ] Not vacuous (inputs not over-filtered by `assume()`) - [ ] Edge cases covered with explicit examples (`@example`) - [ ] No reimplementation of function logic in assertion - [ ] Strategy constraints are realistic - [ ] Settings appropriate for context ## Red Flags - **Tautological:** `assert sorted(xs) == sorted(xs)` tests nothing - **Only "no crash":** Always look for stronger properties - **Vacuous:** Multiple `assume()` calls filter out most inputs - **Reimplementation:** `assert add(a, b) == a + b` if that's how add is implemented - **Missing edge cases:** No `@example([])`, `@example([1])` decorators - **Overly constrained:** Many `assume()` calls means redesign the strategy ## Common Mistakes | Mistake | Fix | |---------|-----| | Testing mock behavior | Test real behavior | | Reimplementing function in test | Use algebraic properties | | Filtering with assume() | Build constraints into strategy | | No edge case examples | Add @example decorators | | One property only | Add multiple properties (length, ordering, etc.) |
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