Skill UI

This skill is a powerful orchestrator designed to build complete full-stack applications from simple natural language requests. It analyzes user requirements, determines the optimal project type and technology stack, generates a detailed plan, and coordinates specialized agents (frontend, backend, database, etc.) to scaffold and build the entire project structure, accelerating the development lifecycle.

This skill is a powerful orchestrator designed to build complete full-stack applications from simple natural language requests. It analyzes user requirements, determines the optimal project type and technology stack, generates a detailed plan, and coordinates specialized agents (frontend, backend, database, etc.) to scaffold and build the entire project structure, accelerating the development lifecycle.

This is a low-level SDK for Java designed to create and manage persistent AI agents within Azure AI. It provides comprehensive tools for handling the entire agent lifecycle, including creating agents, managing conversation threads, sending messages, running agent processes, and cleaning up resources. Ideal for building robust, stateful AI applications requiring fine-grained control over agent interactions.

This is a low-level SDK for Java designed to create and manage persistent AI agents within Azure AI. It provides comprehensive tools for handling the entire agent lifecycle, including creating agents, managing conversation threads, sending messages, running agent processes, and cleaning up resources. Ideal for building robust, stateful AI applications requiring fine-grained control over agent interactions.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

A powerful AI tool designed for complex, coordinated programming tasks. It enables developers to generate, refactor, and scaffold entire features across multiple files (e.g., schema, API routes, service layers, tests) while maintaining consistency with existing codebase patterns. It supports agent mode for autonomous, multi-step changes and allows systematic migration and review of all affected files via detailed diffs.

Guides Claude Code skill authors through designing and structuring workflow-based skills with numbered phases, decision routing, subagent delegation, and progressive disclosure so multi-step automations remain predictable and auditable.

A comprehensive guide to implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.