Skill UI

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 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 module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

Implements standardized function/tool calling across various large language models (LLMs) via OpenRouter. This unified approach allows developers to build sophisticated, multi-step agents and complex workflows, regardless of the underlying provider (e.g., GPT-4o, Claude 3.5). It simplifies the process of achieving structured output and tool-augmented reasoning in production systems.

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.

Enables Claude to orchestrate multi-agent systems using AI SDK v5, setting up agents, handoffs, intelligent routing, and workflows across OpenAI, Anthropic, and Google when users request complex agent coordination.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

This skill provides guidance and patterns for generating pairwise test cases. Pairwise testing is an advanced testing technique used in software quality assurance (QA) to ensure that every combination of two input parameters is tested. It is highly effective for complex systems with multiple interacting variables, helping developers improve test coverage efficiently while minimizing the overall number of required test cases.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

A senior specialist for building modern, production-grade React applications using React 19, Server Components, and Next.js App Router. Expertise includes implementing complex state management (Redux, Zustand), optimizing performance with memoization, handling data fetching with TanStack Query, and ensuring strict TypeScript typing and accessibility standards. Ideal for architecting large-scale frontend systems.

Comprehensive guide to managing data securely in Replit applications. This skill covers three core storage systems: PostgreSQL for structured data and complex queries; Replit KV Database for caching and session management; and Object Storage for file uploads and blobs. Learn best practices including parameterized queries, TTL implementation, and secure data access patterns.

This skill provides implementation patterns for integrating Retell AI's advanced voice agent and telephony features. It is designed for developers building automated systems that require handling complex voice interactions, making scheduled phone calls, or managing conversational AI workflows within a CI/CD or automation environment.