Generate test inputs and shrink failing examples for Python property tests.
npx skills add https://github.com/anam-org/metaxy --skill hypothesis
Please help me install this Agent Skill. Skill: hypothesis Source: https://github.com/anam-org/metaxy/tree/main/.claude/skills/hypothesis Command: npx skills add https://github.com/anam-org/metaxy --skill hypothesis
Hypothesis automates the generation of diverse test inputs and shrinking of failing cases to help you find and fix edge cases quickly, improving test coverage and reliability.
Install Hypothesis and start writing tests using given strategies to automatically generate inputs and verify properties. For example, test that a function preserves invariants across a broad input space, or create composite strategies to build structured inputs for your code.
High-intent search queries and answers about installing and using this skill.
Property-based testing systematically explores broad input spaces to verify invariants, rather than checking specific examples. It automatically generates diverse test inputs and shrinks failing cases to reveal hidden edge cases quickly.
You can generate test inputs automatically with pytest by using the Hypothesis library. Define strategies that specify the input data shapes, and Hypothesis will generate diverse values to run against your test functions.
Yes, Polars integration enables dataframe-oriented property tests for data validation. You can generate complex dataframe structures as test inputs to systematically verify that your data transformations preserve required invariants.
Stateful testing models a system as a rule-based state machine, generating sequences of operations to verify invariants across multiple state transitions. This approach uncovers bugs in complex stateful systems by exploring varied interaction paths.
The best way to shrink failing test cases is using Hypothesis, which automatically minimizes failing inputs to their simplest form. This reduces complex bugs to minimal examples, making them easy to understand and fix.
No, property-based testing replaces manual test case writing with automated input generation. Instead of handcrafting individual examples, you define strategies that generate comprehensive inputs to validate code properties automatically.
simplerick0Automate Python unit, integration, and end-to-end testing with pytest and Hypothesis.
Wanli-LeeGenerate discriminative test cases and verify Python or JavaScript code correctness.
apacheWrite DataFusion DataFrame and SQL queries with Apache Arrow integration in Python.
JayRHaGenerate unit test plans and example tests across multiple frameworks.
