Skill UI

Expert proficiency in modern Angular development (v20+). Mastery includes Signals for fine-grained reactivity, Standalone Components for modular architecture, and Zoneless applications for peak performance. Skilled in implementing SSR, Hydration, and adopting the latest reactive patterns to build highly performant, scalable enterprise frontend applications.

Expert proficiency in modern Angular development (v20+). Mastery includes Signals for fine-grained reactivity, Standalone Components for modular architecture, and Zoneless applications for peak performance. Skilled in implementing SSR, Hydration, and adopting the latest reactive patterns to build highly performant, scalable enterprise frontend applications.

DSPy lets you declaratively program complex language model systems, assemble modular RAG/agent pipelines, and optimize prompts and modules automatically for reliable LM-driven apps.

This comprehensive skill covers the full scope of Firebase services, including Authentication, Firestore, Cloud Functions, and Hosting. It goes beyond basic implementation, focusing on critical architectural principles: adopting a query-first, denormalized NoSQL mindset. Key insights include mandatory security rule design, optimal data modeling for performance, and correctly utilizing modular SDKs to build scalable and secure backends.

LibAFL is a modular fuzzing library in Rust that exposes advanced mutators, feedback, objectives, and schedulers so you can build bespoke fuzzers or use it as a drop-in replacement for libFuzzer when targeting non-standard architectures or research experiments.

This specialist provides expert architectural guidance for designing and implementing scalable modular monolith backends using NestJS. It leverages Domain-Driven Design (DDD), Clean Architecture, and event-driven patterns to establish clear bounded contexts and module boundaries. Use this skill when building complex systems that require high modularity, maintainability, and a planned path toward microservices, ensuring state isolation and loose coupling within a single deployment unit.

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.

A comprehensive guide to selecting the optimal architecture for Replit applications. It compares three scaling models—single-file scripts (prototypes), modular apps (MVPs), and multi-service systems (large scale)—to help developers choose the correct database, storage, and deployment strategy as their project grows.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

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 guide for creating high-value competitor comparison and alternative pages. Designed for SEO and sales enablement, this skill covers multiple formats (You vs Competitor, Competitor Alternatives, etc.), emphasizing deep research, trust-building content, and modular architecture to capture competitive search traffic and guide users toward conversion.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.