What is Layer 3 System Performance testing?

It is a meta-analysis layer that tracks agent success rates, execution time, and API costs to provide an ROI measurement for your autonomous development workflows.

When does this skill automatically activate?

It activates whenever you discuss testing-related topics such as 'tdd', 'coverage', 'regression', 'baseline', or 'pytest' within the Claude Code environment.

Does this skill require specific testing frameworks?

While it includes optimized patterns for pytest, the underlying methodology for intent validation and system performance is framework-agnostic.

Can it help with regression prevention?

Yes, it provides specific strategies for using Layer 1 tests to prevent structural breaks and Layer 2 validation to prevent architectural drift over time.

How does this skill improve traditional testing?

It adds 'Layer 2' validation which uses Claude's reasoning to ensure code actually matches the documented intent, catching semantic issues that traditional unit tests miss.

Testing Guide & Methodology

Name: Testing Guide & Methodology
Author: akaszubski

byakaszubski

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安全与测试

Implements a comprehensive three-layer testing strategy covering structural validation, semantic intent, and system performance optimization.

关于

The Testing Guide skill provides Claude with a unified methodology for managing software quality throughout the development lifecycle. It introduces a sophisticated three-layer approach that combines traditional deterministic testing via pytest with GenAI-powered semantic validation and high-level system meta-analysis. By guiding Claude to distinguish between structural correctness (Layer 1) and architectural intent (Layer 2), this skill ensures that code not only runs without errors but also aligns perfectly with project goals and maintains long-term maintainability through rigorous regression prevention.

主要功能

Three-layer testing strategy: Structure, Intent, and System Performance
2 GitHub stars
Architectural drift detection to ensure implementation matches design goals
Decision matrix for choosing between pytest and GenAI validation
System-level meta-analysis for tracking agent ROI and model optimization
Standardized TDD, Unit, Integration, and UAT workflow patterns

使用场景

Automating the generation of high-coverage pytest suites for Python projects
Verifying that complex code refactors maintain original architectural intent
Auditing autonomous agent performance and cost-efficiency across large codebases

关于

主要功能

Three-layer testing strategy: Structure, Intent, and System Performance
2 GitHub stars
Architectural drift detection to ensure implementation matches design goals
Decision matrix for choosing between pytest and GenAI validation
System-level meta-analysis for tracking agent ROI and model optimization
Standardized TDD, Unit, Integration, and UAT workflow patterns

使用场景

Automating the generation of high-coverage pytest suites for Python projects
Verifying that complex code refactors maintain original architectural intent
Auditing autonomous agent performance and cost-efficiency across large codebases