Skill UI

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.

The Azure AI Projects SDK enables developers to build and manage sophisticated, end-to-end AI applications directly on Microsoft Foundry. It provides comprehensive client operations for managing agents, datasets, model deployments, and indexes. Key features include integrating diverse tools (like code interpreters and file search) and handling complex conversational flows, making it ideal for building production-grade AI workflows.

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.

The official Java SDK for Azure Cosmos DB, providing comprehensive client capabilities for NoSQL operations. It supports global distribution, multi-model data structures, and reactive patterns (async/await). Ideal for developing high-throughput, scalable enterprise applications in Java that require reliable, low-latency data storage.

A comprehensive guide and client library for interacting with Azure Cosmos DB, a globally distributed, multi-model NoSQL database, using Python. It provides full support for performing document CRUD operations, executing complex partitioned and cross-partition queries, managing containers, and handling advanced concepts like throughput and asynchronous operations.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

Master expert patterns for building robust serverless applications using Azure Functions. This guide covers multi-language support (.NET, Python, Node.js), including the isolated worker model, Durable Functions orchestration, cold start optimization, and implementing various triggers (HTTP, Timer, Blob). Essential for developing scalable, production-ready cloud backend services.

This is the .NET SDK for managing Arize AI Observability and Evaluation resources within Azure Resource Manager (ARM). It provides programmatic access to create, read, update, and delete AI organization resources. It is essential for MLOps professionals and developers who need to monitor, track, and evaluate the performance of AI models deployed on Azure.

This SDK provides a dedicated Python interface for securely sending custom logs to Azure Monitor Log Analytics workspace. It utilizes the Logs Ingestion API, supporting features like batch compression, advanced error handling, asynchronous uploads, and compatibility with various authentication models, including sovereign clouds.

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.