Skill UI

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

This skill assists in researching prediction markets, treating them as potential data sources or oracle signals for corporate decision intelligence, product development, and agent inputs. It provides source-grounded analysis of implied probabilities, signal quality, and integration patterns, while strictly avoiding investment advice.

A comprehensive guide detailing the architectural standards, technology stack, file structure, and coding patterns for building modern, AI-driven full-stack applications. It covers best practices for Next.js, FastAPI, database interaction (Supabase), and structured AI integration (Claude), ensuring consistency and scalability across the development lifecycle.

This comprehensive guide outlines a multi-stage verification pipeline for Quarkus applications. It covers build artifacts, static analysis (Checkstyle, SonarQube, PMD), comprehensive testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native compilation, and performance testing (K6). Use this before PRs or deployments to ensure code quality and stability.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

This guide provides comprehensive patterns for writing reliable and maintainable tests in Rust, adhering to the TDD methodology. It covers unit testing, integration testing, asynchronous programming with Tokio, property-based testing, mocking, and robust error/panic handling. Ideal for developers looking to elevate their Rust code quality and coverage.

This skill executes the final two phases (Refinement and Completion) of the structured SPARC development methodology. It systematically elevates an implemented feature to production-grade quality by performing detailed code reviews (verifying compliance against specifications, architecture, and quality standards), maximizing test coverage (unit, integration, and edge cases), validating performance, and generating comprehensive documentation and traceability matrices. It ensures the feature is fully ready for deployment.

This comprehensive guide details the complete Test-Driven Development (TDD) workflow for Spring Boot applications. It covers best practices for writing unit tests (JUnit 5, Mockito), web layer tests (MockMvc), integration tests (SpringBootTest), and persistence tests (DataJpaTest). It also instructs how to use Testcontainers for realistic database simulations and achieve high code coverage with JaCoCo, ensuring robust and maintainable codebases.

A comprehensive CI/CD quality gate checklist for Spring Boot projects. It guides developers through mandatory steps including building, static analysis (SpotBugs, PMD), running unit/integration tests, measuring code coverage, performing security scans (OWASP), and reviewing diffs before merging or deploying.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

A comprehensive, multi-stage skill designed to simulate a professional Continuous Integration (CI) pipeline. It guides users through critical quality gates, including build verification, type checking, linting, running test suites (with coverage checks), security scanning for secrets, and final diff review. Use this skill before creating a Pull Request or finalizing any significant feature to ensure code robustness and adherence to best practices.

A comprehensive framework for performing end-to-end (E2E) testing on various Windows native desktop applications. It utilizes pywinauto and the Windows UI Automation (UIA) API, supporting major frameworks including WPF, WinForms, Win32/MFC, UWP, and Qt. This skill helps automate complex GUI interactions, making it ideal for CI/CD integration and building robust desktop test suites.