Does it help with code coverage?

Yes, the skill provides coverage strategies to help you analyze results and add tests for uncovered critical paths in your codebase.

How does it improve test maintainability?

By enforcing the 'test behavior, not implementation' principle, it ensures your tests verify observable outcomes rather than internal method calls, making them resilient to refactors.

What languages does the Test Patterns skill support?

It includes specific patterns for Python (pytest), TypeScript (Jest/Vitest), and Ruby (RSpec), but its core principles apply to any modern programming language.

Can it generate test data automatically?

Absolutely. It includes patterns for test data factories and builders to streamline the creation of complex test objects with sensible defaults.

Test Patterns

Name: Test Patterns
Author: jayteealao

byjayteealao

보안 및 테스팅

Simplifies the creation and organization of robust tests using industry-standard patterns across multiple programming languages.

소개

Test Patterns is a comprehensive Claude Code skill designed to help developers write high-quality tests by enforcing best practices like Arrange-Act-Assert (AAA) and behavior-driven verification. It provides domain-specific guidance for unit testing, integration testing, and test data factory creation while supporting various frameworks like pytest, Jest, and RSpec. This skill is ideal for teams looking to improve test coverage, standardize their testing conventions, and ensure that tests remain resilient to code refactors.

주요 기능

Guidance on Arrange-Act-Assert (AAA) structure and naming conventions
Cross-language support for Python (pytest), TypeScript (Jest/Vitest), and Ruby (RSpec)
Standardized patterns for Unit, Integration, and API testing
Automated test data factory and fixture generation
Strategies for meaningful test coverage and mocking dependencies
0 GitHub stars

사용 사례

Establishing a consistent testing culture within a multi-language development team
Generating unit tests for complex business logic in new or existing modules
Setting up integration tests to verify API endpoints and database persistence

소개

주요 기능

Guidance on Arrange-Act-Assert (AAA) structure and naming conventions
Cross-language support for Python (pytest), TypeScript (Jest/Vitest), and Ruby (RSpec)
Standardized patterns for Unit, Integration, and API testing
Automated test data factory and fixture generation
Strategies for meaningful test coverage and mocking dependencies
0 GitHub stars

사용 사례

Establishing a consistent testing culture within a multi-language development team
Generating unit tests for complex business logic in new or existing modules
Setting up integration tests to verify API endpoints and database persistence