Use when you need to review, improve, or write Java unit tests — including migrating from JUnit 4 to JUnit 5, adopting AssertJ for fluent assertions, structuring tests with Given-When-Then, ensuring test independence, applying parameterized tests, mocking dependencies with Mockito, verifying boundary conditions (RIGHT-BICEP, CORRECT, A-TRIP), leveraging JSpecify null-safety annotations, or eliminating testing anti-patterns such as reflection-based tests or shared mutable state. This should trigger for requests such as Review Java code for unit tests; Apply best practices for unit tests in Java code; Write fast JUnit unit tests for Java code; Improve Mockito-based Java unit tests; Refactor Java tests to isolate collaborators. Part of Plinth Toolkit
npx skills add https://github.com/jabrena/plinth --skill 131-java-testing-unit-testing
Review and improve Java unit tests using modern JUnit 5, AssertJ, and Mockito best practices.
What is covered in this Skill?
@Test, @BeforeEach, @AfterEach, @DisplayName, @Nested, @ParameterizedTestassertThat, assertThatThrownBy@ValueSource/@CsvSource/@MethodSource@Mock, @InjectMocks, MockitoExtensionassertThatThrownBy, exception messages@NullMarked, @NullableScope: The reference is organized by examples (good/bad code patterns) for each core area. Apply recommendations based on applicable examples.
Before applying any unit test changes, ensure the project compiles. If compilation fails, stop immediately — do not proceed until resolved. After applying improvements, run full verification.
./mvnw compile or mvn compile before applying any change./mvnw clean verify or mvn clean verify after applying improvementsRun ./mvnw compile or mvn compile and stop immediately if compilation fails.
Read references/131-java-testing-unit-testing.md and identify modernization and quality gaps in current tests.
Implement or refactor tests using JUnit 5, AssertJ, Mockito, parameterization, and stronger boundary checks.
Run ./mvnw clean verify or mvn clean verify after applying improvements.
For detailed guidance, examples, and constraints, see references/131-java-testing-unit-testing.md.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take jabrena/131-java-testing-unit-testing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.