Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given…
Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants.
Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants.
Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug.
Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI tests.
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SKILL.md
Property-Based Testing
An example test asserts one point. A property asserts a rule over the whole input domain and lets the generator hunt for the counterexample. That trade is worth making when the code has an algebraic shape — an inverse, an invariant, an oracle — and not otherwise. Code with no such shape gets example tests; saying so is a valid outcome.
Check first whether the shape is missing or merely buried. A calculation wrapped in I/O, a string built by concatenation, an in-place mutation — each has a property and no seam to assert it through. See [references/refactoring.md](references/refactoring.md) before concluding there is nothing to assert.
Property catalog
Property
Formula
Where it applies
Roundtrip
decode(encode(x)) == x
Serialization, conversion pairs
Inverse
f(g(x)) == x
encrypt/decrypt, compress/decompress
Oracle
new(x) == reference(x)
Optimization, refactoring, reimplementation
Idempotence
f(f(x)) == f(x)
Normalization, formatting, sorting
Invariant
Holds before and after
Any transformation, contract state
Easy to verify
is_sorted(sort(x))
Complex algorithms with cheap checkers
Commutativity
f(a, b) == f(b, a)
Binary and set operations
Associativity
f(f(a,b), c) == f(a, f(b,c))
Combining operations
Identity
f(x, e) == x
Operations with a neutral element
Strength ordering, weakest to strongest: no crash → type preservation → invariant → idempotence → roundtrip / oracle.
Assert the strongest property the code supports. "No crash" alone rarely justifies the dependency — if that is all you can find, either a small rearrangement exposes something stronger, or the honest report is that this code is a poor PBT candidate. Rule out the first before settling for the second.
The two ways a property test asserts nothing
Tautology.assert add(a, b) == a + b restates the implementation; no bug they
share can fail it. Pick a property that constrains the function without recomputing it. Note the exception: f(x) == f(x) is a genuine determinism property when f is not obviously pure — serializers over dicts or sets, hashing, anything reading the clock.
Vacuity.assume() that filters out nearly every input passes without
exercising anything, and self-contradictory assume() passes having run zero cases. Push constraints into the strategy so the generator produces valid inputs directly.
Where to look next
Load the one that matches the task in front of you:
If the project already uses a PBT library, just write the tests in it. If it does not, adding one is a dependency decision that belongs to the user — offer it once with the specific property you would write, and take the answer either way.
