Use when Java implementation work should be guided by Test-Driven Development, including maintaining a test list, choosing the next behavior, writing a failing test first, implementing only enough production code to pass, and refactoring while keeping tests green. This should trigger for requests such as Apply TDD; Use test-driven development; Drive this Java change with tests; Write the failing test first; Red-green-refactor this feature. Part of Plinth Toolkit
npx skills add https://github.com/jabrena/plinth --skill 054-design-tdd
Guide Java developers through Test-Driven Development for implementation work. This is an interactive SKILL.
What is covered in this Skill?
Keep implementation work inside the red-green-refactor cycle, with one selected behavior driving each small change.
references/054-design-tdd.md before applying TDD guidanceRead references/054-design-tdd.md, then list candidate behaviors, edge cases, error paths, integration boundaries, and design questions that may need tests. Add newly discovered cases to the list as work proceeds.
Choose the next useful test case: small enough to implement in one cycle, valuable enough to move the design forward, and specific enough to expose the expected observable outcome.
Write or describe the failing unit, integration, acceptance, or characterization test before production code. Use the test to clarify the public interface, API shape, inputs, outputs, errors, or usage of the code.
Implement the smallest functional production code needed to pass the selected test. Avoid speculative branches, abstractions, framework wiring, or cleanup that is not required by the current failing test.
After the selected test passes, refactor new and existing code to improve names, duplication, responsibility placement, type design, and test clarity while keeping the relevant tests green.
Report the selected behavior, failing-test-first signal, code added to pass, refactoring performed, verification results, skipped checks, missing tests, and remaining risks. Return to the test list for the next cycle.
For detailed guidance, examples, and constraints, see references/054-design-tdd.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/054-design-tdd 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.