What is the recommended coverage threshold?

The skill aims for a minimum of 75% for gate enforcement, with a target of 80%+ and excellence at 90%+ for critical packages.

What automation tools are included?

It includes a Coverage Gap Analyzer, a Table-Driven Test Generator, and a Methodology Guide Generator to accelerate the testing lifecycle.

Is this skill only for Go projects?

While natively optimized for Go, the methodology is 89% transferable to languages like Python, Rust, and TypeScript with minor language-specific adjustments.

Does it support CLI application testing?

Yes, it includes specific patterns and templates for capturing stdout/stderr, mocking os.Exit, and testing flag parsing in CLI apps.

How does the Coverage Gap Analyzer improve workflow?

It identifies untested code paths by priority, providing a 186x speedup over manual analysis to reach coverage targets faster.

Go Testing Strategy & Coverage Optimizer

Name: Go Testing Strategy & Coverage Optimizer
Author: yaleh

byyaleh

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Seguridad y Pruebas

Systematizes project testing with coverage-driven gap closure and automated pattern generation to reach 80%+ code coverage.

Acerca de

This skill transforms ad-hoc testing into a systematic, coverage-driven methodology specifically optimized for Go projects, though 89% transferable to languages like Python and Rust. It provides a comprehensive framework of eight documented patterns—including table-driven tests, golden files, and CLI testing—alongside high-speed automation tools that accelerate test generation and coverage analysis by over 180x. Ideal for teams aiming to bridge coverage gaps from 60% to 80%+, it enforces quality gates and provides ready-to-use templates for unit, integration, and CLI testing within the Claude Code environment.

Características Principales

Automated coverage-driven gap closure algorithm
15 GitHub stars
8 high-performance test patterns including Table-Driven and Golden Files
186x speedup coverage gap analyzer and 200x speedup test generator
Cross-language transferability to Python, Rust, and TypeScript
Comprehensive CLI and integration testing infrastructure templates

Casos de Uso

Boosting Go project test coverage from 60% to 80%+ systematically
Refactoring legacy codebases by adding systematic unit and integration tests
Establishing robust test infrastructure for new command-line applications

Acerca de

Características Principales

Automated coverage-driven gap closure algorithm
15 GitHub stars
8 high-performance test patterns including Table-Driven and Golden Files
186x speedup coverage gap analyzer and 200x speedup test generator
Cross-language transferability to Python, Rust, and TypeScript
Comprehensive CLI and integration testing infrastructure templates

Casos de Uso

Boosting Go project test coverage from 60% to 80%+ systematically
Refactoring legacy codebases by adding systematic unit and integration tests
Establishing robust test infrastructure for new command-line applications