Skill UI

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.

This skill provides expert guidance on creating, reviewing, and managing production-grade AWS infrastructure using Terraform. It focuses on best practices for reusable module design, robust remote state management (S3 + DynamoDB), and adhering to modern HashiCorp Configuration Language (HCL) patterns like using 'for_each'. Ideal for teams building or auditing complex, scalable AWS environments.

Expert in implementing Infrastructure as Code (IaC) using Terraform across multi-cloud environments, including AWS, Azure, and GCP. Specializes in creating reusable, modular components, advanced state management (backends, locking, migrations), ensuring security best practices, and developing robust, production-grade cloud infrastructure pipelines.

A comprehensive, multi-phase workflow designed for Infrastructure as Code (IaC) using Terraform. It guides users through provisioning cloud resources, creating reusable modules, managing complex state, handling multi-environment deployments, and integrating robust CI/CD pipelines and security practices. Ideal for establishing scalable, repeatable, and auditable cloud infrastructure.

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.

Utilizes structured critical reasoning to rigorously stress-test ideas, plans, and decisions. This skill allows you to play devil's advocate, conduct pre-mortems, execute red-teaming exercises, or audit underlying assumptions. It employs multiple advanced modes, including Socratic questioning, Hegelian dialectics, and Falsificationism, to identify blind spots, weaknesses, and unstated assumptions before commitment.

A comprehensive skill for structured software development life cycle (SDLC). It guides users through adaptive phases—Specify, Design, Tasks, and Execute—ensuring that every feature is built with traceable requirements, atomic tasks, and rigorous verification. Features include an independent Verifier, persistent decision logging, and a self-improving lessons layer to enhance future development quality. Ideal for complex, multi-component feature implementation.