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Design the smallest effective set of tests for a change, then generate them in the project's style. Use when: fixing bugs, adding features, preventing regressions, or improving weak test coverage across web, JVM, Python, and native codebases.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Clean up AI-generated or over-engineered code while preserving behavior. Use when: refactoring noisy code, removing dead code, simplifying abstractions, or making code more idiomatic without changing the public API.
Review AI-generated code changes for unintended consequences, logic errors, UI inconsistencies, and context-specific issues. Use when: code has just been modified, flag potential bugs, verify UI consistency, check implementation correctness against project patterns.
Turn a feature request, issue, or rough spec into a safe implementation plan for an existing codebase. Use when: planning a new feature, scoping impacted files, evaluating trade-offs, or deciding rollout steps before coding.
Turn small ideas into fully-fledged projects through structured discovery, research, and specification. Use when: starting a new project from a rough idea, need to validate feasibility, want to create comprehensive project documentation, exploring feature requirements and design patterns. Conversational, adaptive workflow.
Audit the quality and robustness of an implementation by inventorying the code, understanding how it works, then researching reference examples and community standards online to identify gaps. Use when: checking if a DSP routine is correct, validating a JUCE component follows best practices, reviewing an entire module for structural weaknesses, or verifying a feature is not just "working" but properly implemented.
Initialize or bootstrap a Ralph workspace. Use when starting a repo, preparing the workspace for first use, creating or seeding the `.ralph` folder, filling the first project understanding / journal entries, and documenting setup blockers before substantive work begins.
| name | test-strategy-and-generation |
| description | Design the smallest effective set of tests for a change, then generate them in the project's style. Use when: fixing bugs, adding features, preventing regressions, or improving weak test coverage across web, JVM, Python, and native codebases. |
| argument-hint | Describe the change, bug, or code path that needs test coverage |
| user-invocable | true |
| disable-model-invocation | false |
Good testing is selective. Do not maximize test count. Choose the smallest set of tests that proves the behavior, guards the highest-risk paths, and matches the codebase's testing style.
Prefer stable tests around contracts and behavior over tests that mirror implementation details.
Use workflow.md for the full test-selection flow and stack-specific guidance.
Deliver:
/test-strategy-and-generation -> "Add regression tests for this pagination bug"/test-strategy-and-generation -> "What tests should back this new Spring service flow?"/test-strategy-and-generation -> "Generate the right pytest coverage for this upload endpoint"See EXAMPLES.md for example test-boundary choices, case selection, and output formats.