Skill UI

A master-level architect specializing in reviewing and guiding complex, modern distributed systems. This skill excels at evaluating adherence to patterns like Clean Architecture, DDD, and Microservices. Use it when assessing scalability, resilience, security posture, and maintainability across major design changes, ensuring the resulting system is robust and future-proof.

Provides structured ADR patterns, templates, and practices for capturing significant architectural decisions, rationale, tradeoffs, and lifecycle status to onboard teams and review historical choices.

This skill simulates a Principal Software Architect, specializing in designing, reviewing, and documenting high-level system architectures. It guides users through defining core system structures, evaluating complex technology trade-offs (e.g., ACID vs. eventual consistency), and planning for extreme scalability. Key outputs include comprehensive architecture diagrams (Mermaid) and formal Architecture Decision Records (ADRs), ensuring every major technical choice is documented with rationale and consequences.

This skill acts as a code education expert, specializing in breaking down complex code, algorithms, or system architecture. It transforms difficult technical concepts into understandable explanations using clear narratives, visual diagrams, and step-by-step breakdowns. Ideal for creating learning materials, onboarding guides, or detailed technical reviews for developers of all levels.

This comprehensive template provides a production-grade reference architecture for CodeRabbit AI code reviews. It is designed to guide teams in establishing rigorous coding standards by defining specific, best-practice instructions for various layers (API, Database, Authentication, Frontend, CI/CD, Infrastructure). Use this as a robust starting point to build a comprehensive, automated, and secure code quality review process.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.

A comprehensive, end-to-end workflow bundle guiding developers through the entire software development lifecycle. It covers project scaffolding, frontend development (React/Next.js), robust backend architecture, database design, rigorous testing (TDD, E2E), code quality review, and modern CI/CD deployment practices for web and mobile applications.

A sophisticated AI system that coordinates multiple specialized agents (e.g., Security Auditors, Performance Analysts, Architecture Specialists) to perform comprehensive, multi-perspective code assessments. It analyzes code context, manages state, and synthesizes conflicting insights to provide deep, holistic reviews for complex software projects.

This skill provides the production-ready reference architecture for integrating Exa into complex applications. It covers advanced search pipelines, RAG implementation for context generation, and structured content discovery. Use this to design new Exa integrations, review project structures, or establish industry-standard best practices for neural search systems.

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

This guide provides the best-practice reference architecture for developing complex Obsidian plugins. It details a highly modular project structure, enforcing separation of concerns by implementing dedicated layers for commands, views, and services (e.g., vault operations). Use this when designing a new plugin, reviewing existing project structure, or establishing standardized development practices for maintainable and scalable Obsidian extensions.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.