Skill UI

A specialized reverse-engineering skill designed to extract comprehensive functional and non-functional specifications from existing, undocumented, or legacy codebases. It operates by analyzing code structure, tracing data flows, and identifying observable behaviors. The output is structured using EARS (Easy Approach to Requirements Syntax), providing clearer requirements documentation, architectural diagrams, and dependency maps for onboarding or system modernization.

Automates SSH key manager workflows within DevOps Basics, offering guided best practices, code/config generation, and validation for foundational workflows such as git, containers, CI/CD, and infrastructure.

This skill guides developers through advanced, safe, and systematic code refactoring using Test-Driven Development (TDD) principles. It provides a comprehensive workflow encompassing pre-assessment, detecting code smells, applying major design patterns (SOLID, Factory, Strategy), optimizing performance, and ensuring safety through rigorous, incremental testing. Use this when improving code structure, restructuring large components, or updating legacy systems while guaranteeing functional integrity and maintainability.

This skill provides comprehensive, structured guidance for testing Temporal workflows written in Python. It covers best practices for unit testing (using time-skipping), integration testing (mocking dependencies), and determinism validation through replay testing. Ideal for ensuring high code coverage and robust CI/CD pipelines for complex asynchronous workflows.

A comprehensive collection of production-ready, reusable Terraform modules designed for standardized infrastructure provisioning across major cloud platforms: AWS, Azure, and GCP. This library enforces Infrastructure as Code (IaC) best practices, allowing users to build, test, and deploy multi-cloud compatible components efficiently and reliably.

Provides Terraform and OpenTofu best-practice guidance for module design, testing selection, CI/CD pipeline setup, security scanning, and architecture decisions in production infrastructure-as-code projects.

This skill provides advanced expertise in Infrastructure as Code (IaC) using Terraform and OpenTofu. It covers designing robust, reusable modules, managing complex multi-cloud state backends, and implementing enterprise-grade CI/CD pipelines. It specializes in governance, Policy-as-Code, and achieving GitOps compliance for full infrastructure automation.

Provides automated guidance for Terraform state manager tasks in advanced DevOps workflows, covering Kubernetes, CI/CD, monitoring, and infrastructure-as-code best practices.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

This comprehensive workflow provides a complete cycle of quality assurance for software development. It covers every stage from defining the test strategy (TDD, Unit Testing) through integration, E2E testing, browser automation, performance profiling, security scanning, and final code review. Use this when setting up robust testing infrastructure or ensuring production-ready software quality.

A comprehensive framework guiding feature planning and implementation through adaptive phases: Specify, Design, Tasks, and Execute. It enforces highly structured development practices by using EARS notation, atomic tasks, deterministic Conventional Commits, and mandatory code validation scripts. Features include requirement traceability, automatic verification, and a self-improving lessons layer, ensuring quality and compliance throughout the lifecycle.

This skill acts as a specialized tutorial engineering specialist, transforming raw, complex technical concepts or codebases into structured, progressive, and engaging educational content. It applies pedagogical design principles, such as progressive disclosure, spaced repetition, and hands-on exercises, to create documentation, courses, and onboarding materials that teach users how to build, debug, and optimize, rather than just referencing information.