Skill UI

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

A comprehensive, production-grade reference architecture for integrating with Apollo.io. It details a layered design encompassing API services, business logic, background job processing (e.g., lead enrichment), and data modeling, enabling the building of robust sales intelligence and CRM synchronization systems.

This skill provides optimal best practices and patterns for building ViewModels, Wizards, and managing complex navigation flows in Avalonia applications. It leverages the power of ReactiveUI for functional state management and Zafiro for structured command handling, ensuring the creation of robust, maintainable, and scalable MVVM architectures.

This skill specializes in generating expert-level, component-level architecture documentation based on the C4 Model. It synthesizes low-level code documentation into a high-level view, clearly defining component boundaries, service interfaces (REST, GraphQL, etc.), and inter-component dependencies. Ideal for documenting complex software systems and creating accurate Mermaid C4 Component diagrams.

This expert skill specializes in generating comprehensive, structured documentation for microservices and containerized architectures using the C4 Model. It covers system boundaries, container definitions, technology stacks, inter-container communication protocols, API specifications (OpenAPI/Swagger), and deployment infrastructure (e.g., Docker, Kubernetes). Ideal for documenting complex system deployments and mapping architectural relationships.

Enables Claude to configure neural network architectures through the neural-network-builder plugin, creating or modifying models and defining layers, activation functions, and training parameters whenever network design or tuning is requested.

This skill covers the end-to-end development of cross-browser extensions (Chrome, Firefox). Expertise includes mastering Manifest V3, structuring complex architecture (service workers, content scripts), managing local/sync storage, building user interfaces, and implementing monetization models. Ideal for building robust, production-ready tools and navigating store publishing policies.

This skill provides a comprehensive guide to implementing the Command Query Responsibility Segregation (CQRS) pattern. It is crucial for separating read and write concerns, allowing independent scaling of read models, optimizing complex query performance, and building robust, event-sourced architectures when read and write data models differ.

A comprehensive guide on configuring and selecting the optimal AI model within the Cursor IDE. It details the strengths of various providers (OpenAI, Anthropic, Google) for specific coding tasks—from quick bug fixes to complex architecture planning—and guides users through model selection, API key setup (BYOK), and cost optimization strategies.

This skill guides the planning and execution of complex business domains using Domain-Driven Design (DDD) principles. It helps developers move from strategic modeling (defining subdomains and bounded contexts) to tactical implementation patterns like CQRS, Event Sourcing, and Saga orchestration. Use it when dealing with complex, high-variability business rules and needing explicit architectural boundaries.

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.

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.