Can this skill help increase my project's code coverage?

Yes, it includes a verification workflow designed to identify untested logic and aim for over 80% coverage, with 100% coverage on critical paths.

How does it handle complex requirements?

It analyzes your project documentation, such as user stories and API contracts, and maps 3-5 specific acceptance criteria to concrete test cases across different testing layers.

Which testing frameworks does this skill support?

The Testing Code skill is framework-agnostic and adapts to your project's existing patterns, supporting popular tools like Jest, Vitest, Pytest, Playwright, and more.

Does it support testing for edge cases and failures?

Absolutely. The skill proactively generates test cases for boundary conditions, network timeouts, invalid formats, and API error scenarios (4xx/5xx).

Testing Code Orchestrator

Name: Testing Code Orchestrator
Author: Microck

byMicrock

•

Security & Testing

Writes comprehensive automated test suites and validates functionality against acceptance criteria to ensure high code coverage and reliability.

About

The Testing Code skill provides a systematic framework for Claude to manage the entire testing lifecycle, from interpreting user stories to implementing robust test cases. It supports multi-layered testing strategies including unit, integration, component, and end-to-end (E2E) tests using popular frameworks like Jest, Vitest, and Pytest. By mapping technical requirements directly to test implementation and prioritizing edge cases, this skill helps developers maintain high-quality codebases and ensures that all feature acceptance criteria are met before deployment.

Key Features

81 GitHub stars
Multi-layer test generation across unit, integration, component, and E2E levels
Automated identification of edge cases, boundary conditions, and error paths
Requirement-to-test mapping to ensure 100% coverage of acceptance criteria
Reusable test fixture and mock data management for consistent testing environments
Parallel implementation patterns for rapid test suite development in complex projects

Use Cases

Increasing test coverage for legacy codebases by identifying and filling gaps in existing logic
Writing comprehensive test suites for new features based on user stories and API specs
Automating the validation of critical user flows and API contracts before production releases

About

Key Features

81 GitHub stars
Multi-layer test generation across unit, integration, component, and E2E levels
Automated identification of edge cases, boundary conditions, and error paths
Requirement-to-test mapping to ensure 100% coverage of acceptance criteria
Reusable test fixture and mock data management for consistent testing environments
Parallel implementation patterns for rapid test suite development in complex projects

Use Cases

Increasing test coverage for legacy codebases by identifying and filling gaps in existing logic
Writing comprehensive test suites for new features based on user stories and API specs
Automating the validation of critical user flows and API contracts before production releases