Unit Testing

Use when implementing features, fixing bugs, or making code changes. Ensures scope is defined before coding, then enforces RED → GREEN → REFACTOR test discipline. Triggers: 'implement', 'add', 'build', 'create', 'fix', 'change', 'feature', 'bug'.

Generate comprehensive PySpark-based data quality validation tests for Databricks tables. Use when creating automated tests for data completeness, accuracy, consistency, and conformity, or when user mentions test generation, data validation, quality monitoring, or PySpark test frameworks.

Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking. Language-agnostic principles with TypeScript/Jest and Python/pytest examples.

Comprehensive guide to writing effective unit, integration, and end-to-end tests with modern testing frameworks

Prevent premature task completion by checking for parsing errors, execution failures, incomplete test results, and missing expected outputs. Acts as final quality gate before declaring tasks complete. Use proactively after major operations or when task success needs verification.

Write, test, and maintain Go code following idiomatic patterns, standard library conventions, and Go best practices. Use when working with Go/Golang projects, writing Go code, or reviewing Go implementations.

Working in isolated worktree directories for parallel work - work normally, tests isolated, orchestrator handles cleanup

Guide through GREEN phase - minimal implementation to pass test

生成多环境配置清单和dotenv模板文件，确保dev/test/staging/prod环境配置完整。当需要创建环境配置、生成.env.example模板、文档化测试框架setup、映射CI环境变量时使用。解决dotenv经常被忽视的痛点。

Guide developers through property-based testing including property definition, shrinking, and framework-specific implementation

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Rigorous RL evaluation - statistical protocols, train/test discipline, metrics, generalization

Guide developers through mutation testing to assess and improve test suite quality

Expert skill for writing production-grade Python tests using pytest with modern fixtures, parametrization, and coverage integration.

Test development with pytest, fixtures, and mocking. Use for writing tests, test patterns, coverage, parametrization, and debugging test failures.

Comprehensive Python testing best practices, pytest conventions, test structure patterns (AAA, Given-When-Then), fixture usage, mocking strategies, code coverage standards, and common anti-patterns. Essential reference for code reviews, test writing, and ensuring high-quality Python test suites with pytest, unittest.mock, and pytest-cov.

Guide for using pytest-mock plugin to write tests with mocking. Use when writing pytest tests that need mocking, patching, spying, or stubbing. Covers mocker fixture usage, patch methods, spy/stub patterns, and assertion helpers.

Write minimal, focused tests that cover core user flows and primary workflows without over-testing during development. Use this skill when deciding what to test, writing behavior-focused tests, naming test cases clearly, mocking external dependencies, or keeping tests fast. Apply when completing feature implementation and adding strategic tests, testing critical paths rather than edge cases, or deferring edge case testing to dedicated testing phases.

Writes comprehensive tests with edge cases during test phase

Build responsive, mobile-first user interfaces that adapt gracefully across all screen sizes using fluid layouts, relative units, breakpoints, and optimized assets. Use this skill when creating or modifying UI layouts, responsive components, CSS media queries, or any code that needs to adapt to different viewport sizes. Use this when implementing mobile-first designs, adding responsive breakpoints (mobile/tablet/desktop), using relative units (rem/em), ensuring touch-friendly tap targets, optimizing images for different screen sizes, testing across multiple devices, or making typography readable on all screens. Use this when working on layout components, grid systems, or any UI code that should respond to screen size changes.