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Found 13 Skills
Pairwise test generation
AI SDLC test-case-driven testing workflow. Use when an AI assistant is asked to derive test cases, create a test plan, expand coverage, or write tests from explicit scenarios before implementing unit, service, transport, or integration tests. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.
Creates a QA planning subtask tagged `qa-plan` using handoff-first context loading, lazy artifact reads, and compact JSON handoff output.
Design test strategies and test plans. Trigger with "how should we test", "test strategy for", "write tests for", "test plan", "what tests do we need", or when the user needs help with testing approaches, coverage, or test architecture.
Design API test plans and cases for REST/GraphQL/gRPC interfaces. Default output Markdown; can request Excel, CSV, or JSON. Use for API testing.
Build a complete test suite with test set and test cases for evaluating an AI agent. Guides through test set type selection, scenario design using vertical-specific templates, expected behavior crafting, and bulk creation. Use when user says "create test cases", "build test suite", "add test scenarios", "set up evaluation tests", or "design test cases".
Design accessibility testing plans and test cases covering WCAG 2.1 standards, screen readers, and keyboard navigation. Default output is Markdown, with support for requesting Excel/CSV/JSON formats. Use for accessibility testing.
Design API testing plans and test cases covering REST/GraphQL/gRPC interfaces. Default output is Markdown, and Excel/CSV/JSON output can be requested. Use for API testing or api-testing.
Systematic LLM prompt engineering: analyzes existing prompts for failure modes, generates structured variants (direct, few-shot, chain-of-thought), designs evaluation rubrics with weighted criteria, and produces test case suites for comparing prompt performance. Triggers on: "prompt engineering", "prompt lab", "generate prompt variants", "A/B test prompts", "evaluate prompt", "optimize prompt", "write a better prompt", "prompt design", "prompt iteration", "few-shot examples", "chain-of-thought prompt", "prompt failure modes", "improve this prompt". Use this skill when designing, improving, or evaluating LLM prompts specifically. NOT for evaluating Claude Code skills or SKILL.md files — use skill-evaluator instead.
设计移动端（iOS/Android）测试方案与用例，覆盖功能、兼容性、性能、网络、安全等。默认输出 Markdown，可请求 Excel/CSV/JSON。Use for 移动端测试 or mobile testing.
Use when the user asks to generate, create, or write unit tests for code. Analyzes the target code, produces a structured test case list for review, then generates test code. Supports Java (JUnit 5, Mockito, AssertJ).