Skill UI

This skill provides comprehensive guidance on training Mixture of Experts (MoE) models, including architectures like Mixtral and DeepSeek-V3. It details techniques for achieving large-scale model capacity with significantly reduced computational costs (e.g., 5x reduction vs. dense models). Topics covered include top-k routing, load balancing, expert parallelism, and integration with frameworks like DeepSpeed and HuggingFace for efficient, resource-constrained training.

A comprehensive skill for building and maintaining efficient, scalable monorepositories. This involves strategies for enabling code sharing, ensuring consistent tooling across multiple packages and applications, and managing atomic changes. Use this skill for setting up new monorepo architectures, migrating existing repositories, and optimizing complex CI/CD pipelines.

This guide details advanced architectural patterns for building complex AI systems using multiple Language Model instances (agents). It addresses the single-agent context bottleneck by distributing work, enabling parallelism, and facilitating specialization across different subtasks. Key patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical designs, providing guidelines for context isolation and effective coordination protocols.

This skill provides a comprehensive decision framework and set of patterns for architects designing cloud-agnostic applications across major providers like AWS, Azure, and GCP. It details service comparisons (Compute, Storage, DB), multi-cloud patterns (e.g., Best-of-Breed, DR), cost optimization strategies, and structured migration plans, ensuring robust and resilient enterprise deployments.

This comprehensive workflow guides the process of building and deploying new features that must maintain consistent functionality and design across multiple operating systems (web, mobile, and desktop). It strictly follows an API-first architecture, starting with defining shared API contracts (OpenAPI/GraphQL) and a unified design system. The process then guides parallel implementation across specialized stacks like React/Next.js, SwiftUI, and Jetpack Compose, ensuring high code consistency and faster time-to-market.

This skill provides comprehensive principles for developing multiplayer games, covering essential topics from networking architecture (Dedicated, P2P, Host-based) to advanced synchronization techniques. It details how to handle latency via prediction, interpolation, and lag compensation, optimize bandwidth using delta compression, and implement robust security measures like server authority and anti-cheat systems. Ideal for developers building real-time online experiences.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

This comprehensive skill profile models an expert network engineer specializing in modern cloud networking, security architectures, and performance optimization. It covers multi-cloud environments (AWS, Azure, GCP), advanced concepts like Zero Trust, Service Mesh (Istio), and high-availability design. Key capabilities include implementing IaC (Terraform), load balancing, DNS management, and deep network troubleshooting. Ideal for complex DevOps, SRE, or infrastructure design roles.

This comprehensive guide helps developers make critical architectural decisions in modern Node.js development. It covers framework selection (Fastify, Hono, Express), implementing layered architecture, mastering async patterns, robust error handling, and essential security protocols. The focus is on teaching architectural thinking and problem-solving skills, ensuring the development of scalable, high-performance, and maintainable backend APIs.

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.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.