Unit Testing

Master of defensive Bash scripting for production automation, CI/CD pipelines, and system utilities. Expert in safe, portable, and testable shell scripts. Use when writing, creating, authoring, generating, or developing bash scripts, shell scripts, or automation. Also triggers for learning bash best practices, understanding defensive programming patterns, implementing error handling, ensuring portability, following shellcheck recommendations, or applying production-grade bash standards. Helps with CI/CD scripts, system utilities, deployment automation, and production bash code.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Build async APIs with FastAPI, including endpoints, dependency injection, validation, and testing. Use when creating REST APIs, web backends, or microservices.

Comprehensive guide for building functional tools for LiveKit voice agents using the @function_tool decorator. Use when creating tools for LiveKit agents to enable capabilities like API calls, database queries, multi-agent coordination, or any external integrations. Covers tool design, RunContext handling, interruption patterns, parameter documentation, testing, and production best practices.

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.