Skill UI

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

A comprehensive guide and methodology for proactively hunting adversary abuse of legitimate cloud services (Azure, AWS, GCP, SaaS). This technique focuses on identifying Command and Control (C2), data staging, and exfiltration methods that leverage normal cloud functionality, making detection challenging.

Connects to the MAXIA AI-to-AI marketplace on the Solana blockchain. This tool allows users to discover, buy, and sell AI services directly between decentralized agents. Features include comprehensive crypto intelligence, such as sentiment analysis and trending data; DeFi yield tracking; Web3 wallet security analysis (e.g., rug detection); and real-time GPU pricing, making it a comprehensive hub for Web3 AI interactions.

This skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.

Guides hooking Phoenix AI services with OpenInference tracing, covering setup, spans, instrumentation, and production deployment for LLM workflows using Python or TypeScript.

Generate a TypeSpec API plugin tailored for Microsoft 365 Copilot, wiring REST operations, authentication flows, confirmation cards, and Adaptive Cards into a guided workflow so Copilot can interact with external services securely.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This comprehensive guide provides a deep dive into migrating AI applications from major LLM providers (like OpenAI or Anthropic) to the Cohere platform. It covers critical technical steps, including API call adaptation, re-vectorizing embeddings, migrating tool definitions, and implementing the Adapter Pattern for seamless, gradual re-platforming of existing AI services.

This guide details the process of deploying production-grade inference services on CoreWeave using KServe. It covers setting up autoscaling, GPU scheduling, and implementing scale-to-zero functionality for efficient ML model serving endpoints. Ideal for MLOps engineers deploying large language models (LLMs) or other complex AI models in a production environment.

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This bundle collects comprehensive diagnostic evidence for ElevenLabs services. It automatically gathers SDK versions, API connectivity status, quota usage, voice inventories, and available model metadata. Use this tool when encountering persistent bugs or preparing comprehensive support tickets for technical support.