Can it help with debugging flaky tests?

Yes, it includes specific strategies to isolate new tests, document intermittent failures, and provide detailed blocker reports for senior engineers.

How does it handle code coverage requirements?

The skill provides specific commands for JaCoCo, Jest, and Pytest-cov to generate reports and verify if specified coverage targets (typically 80%+) are met before task completion.

Which testing frameworks does this skill support?

It supports major frameworks including JUnit and MockK for Kotlin/Java, Jest for JavaScript/TypeScript, and Pytest for Python.

Does it support end-to-end (E2E) testing?

Yes, it provides guidance for critical user workflows, authentication flows, and payment processes, though it recommends keeping E2E tests at roughly 10% of the total test suite.

Testing Implementation

Name: Testing Implementation
Author: Angriff36

byAngriff36

Seguridad y Pruebas

Implements comprehensive test suites and quality assurance workflows using industry-standard frameworks and coverage targets.

Acerca de

This skill empowers Claude to architect and execute robust testing strategies across multiple ecosystems, including Java/Kotlin (JUnit, MockK), JavaScript/TypeScript (Jest), and Python (pytest). It provides structured guidance for unit, integration, and end-to-end testing while enforcing strict quality standards like coverage targets and the Arrange-Act-Assert pattern. By automating validation commands and providing troubleshooting protocols for flaky tests and implementation bugs, it ensures high-quality, production-ready code delivery.

Características Principales

Structured Arrange-Act-Assert patterns for clean, readable tests
Multi-language support for JUnit, MockK, Jest, and Pytest frameworks
Automated coverage reporting and validation commands
0 GitHub stars
Comprehensive guidance for mocking external dependencies and services
Diagnostic protocols for resolving flaky tests and infrastructure blockers

Casos de Uso

Validating codebase quality through automated coverage analysis and reporting
Setting up integration testing environments with in-memory databases
Automating unit test generation for complex business logic and edge cases

Acerca de

Características Principales

Structured Arrange-Act-Assert patterns for clean, readable tests
Multi-language support for JUnit, MockK, Jest, and Pytest frameworks
Automated coverage reporting and validation commands
0 GitHub stars
Comprehensive guidance for mocking external dependencies and services
Diagnostic protocols for resolving flaky tests and infrastructure blockers

Casos de Uso

Validating codebase quality through automated coverage analysis and reporting
Setting up integration testing environments with in-memory databases
Automating unit test generation for complex business logic and edge cases