Skill UI

This comprehensive guide outlines established best practices for developing robust, scalable, and high-quality enterprise applications using Spring Boot and Java. It covers structuring projects, implementing dependency injection, designing RESTful APIs, managing transactions, securing endpoints, and utilizing advanced testing strategies (Unit, Integration). Ideal for developers aiming for industry-standard code quality.

This tool automates the creation of robust Playwright end-to-end tests. Given a detailed user scenario, it executes a multi-step process using the Playwright MCP, generating high-quality, executable Playwright TypeScript code. It saves the test file and automatically runs and iterates the test until it passes, ensuring comprehensive functional coverage and reliable validation.

This advanced skill automates the enforcement of Python code quality using Ruff. It implements a controlled, iterative workflow that supports safe and unsafe automatic autofixes, formats the code, and reviews diffs after each pass. The process recursively repeats until all findings are resolved or require explicit user decision, ensuring high code consistency and quality gates.

This comprehensive guide outlines essential best practices and structural checks for developing robust Salesforce Apex code. It covers critical areas such as preventing governor limit exceptions (e.g., bulk safety), enforcing security (CRUD/FLS), preventing SOQL injection, adhering to sharing models, and implementing modern Apex idioms. It also mandates rigorous testing strategies (Positive, Negative, Bulk) and proper trigger architecture.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

Guides creation or updates of object-oriented component documentation by analyzing code or existing docs, then applying C4/Arc42/IEEE templates to capture architecture, interfaces, and quality traits.

This tool helps developers run comprehensive test suites using pytest and generates detailed code coverage reports. It identifies specific lines of code that have not been covered by tests, guiding developers to improve test quality until 100% coverage is achieved. Use it to ensure robust and reliable software.

A comprehensive, multi-phase quality engineering audit tool designed to test codebases beyond standard unit tests. It first explores the codebase's domain and architecture to derive behavioral requirements, then executes a rigorous sequence including spec-traced functional tests, three-pass code reviews, and a multi-model specification audit (Council of Three). It is specifically engineered to find the critical 35% of real-world defects that structural code review alone might miss. Works with any programming language.

Transforms shorthand, quasi-code, or fuzzy natural-language notes into production-ready implementations, bridging collaborators with different expertise and ensuring robust, expert-quality output.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

This skill systematically reviews test code to identify and correct common anti-patterns. It detects issues such as flaky tests, overly mocked dependencies (over-mocking), brittle assertions, excessive test size (The Giant), and structural dependencies. It explains the flaw, provides best practices, and suggests refactored, reliable implementations, helping maintain high test quality and stability.