Skill UI

This skill generates comprehensive Technical Design Documents (TDDs), guiding users through structuring architectural decisions, implementation plans, and risk assessments. It follows industry best practices from standards like Google Design Docs, RFC patterns, and ADRs. It focuses on high-level contracts and architectural 'what' and 'why', rather than specific, low-level implementation code.

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.

A comprehensive, adaptive framework for managing the entire software development lifecycle, from initial project specification to final execution. It automatically sizes the depth of the workflow (Specify, Design, Tasks, Execute) based on the complexity and scope of the feature or project. Ideal for starting new projects, mapping existing codebases, implementing complex features with atomic verification, and maintaining persistent state across sessions.

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.

This skill provides production-grade patterns for Unity's Data-Oriented Technology Stack (DOTS). It covers essential components like Entity Component System (ECS), the Job System, and the Burst Compiler, enabling developers to build high-performance, scalable games. Use this when optimizing CPU-bound logic, managing thousands of entities, or migrating traditional OOP game code to a data-oriented design.

Automated versioning strategy helper that activates on API development requests, guiding best practices, code/config generation, and validation for REST, GraphQL, OpenAPI, authentication, and API design patterns.

Automatically generates two complementary onboarding documents for any codebase: a Principal-level guide focusing on design philosophy, technical debt, and architectural 'why'; and a Zero-to-Hero guide providing step-by-step setup, development workflows, and practical 'how-to' instructions. Ideal for new team members or documenting complex systems.

A specialized assistant designed to act as a senior software architect. It critiques code generated by AI tools or rapidly prototyped code, moving beyond simple syntax checks. The auditor identifies deep structural flaws, technical debt, architectural weaknesses, and production risks (e.g., security vulnerabilities, poor error handling, tight coupling) that make code fragile or unsuitable for deployment.

A comprehensive security scanner designed to audit AI agent skills before deployment. It analyzes source code (Python, Bash, JS), detects critical vulnerabilities like command injection, network exfiltration, and unsafe deserialization. Furthermore, it scans markdown files for prompt injection attacks, and assesses dependency supply chain risks, providing a clear PASS/WARN/FAIL verdict and remediation guidance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.