Building Azure OpenAI features in .NET requires correct clients, authentication, and service-specific SDK patterns. This skill provides focused C# guidance for chat, embeddings, media, RAG, tools, and production practices.

Managing Azure Machine Learning resources requires precise SDK v2 patterns across authentication, assets, compute, jobs, and pipelines. This skill provides practical Python examples for common Azure ML workflows.

Java developers need reliable patterns for extracting structured data from documents. This skill provides Azure SDK examples for analysis, custom models, classification, and administration.

Document extraction in .NET requires correct Azure models, authentication, and result handling. This skill provides focused C# workflows for analysis, classification, and custom model management.

Persistent agent workflows require coordinated threads, messages, runs, tools, and cleanup. This skill provides focused .NET patterns for implementing those workflows with Azure AI Agents.

Persistent agent workflows require coordinated clients, threads, messages, runs, and cleanup. This skill provides Java patterns for configuring and managing those Azure resources.

Reactive Avalonia architecture can become inconsistent and difficult to maintain. This skill applies Zafiro, DynamicData, ReactiveUI, MVVM, validation, naming, and lifecycle conventions.

Complex Avalonia application flows can produce tightly coupled ViewModels and inconsistent navigation. This skill provides focused Zafiro and ReactiveUI patterns for maintainable MVVM architecture.

Avalonia views often become repetitive and deeply nested. This skill applies Zafiro.Avalonia containers, shared styles, behaviors, and reusable components to simplify layout design.

Autonomous workflows often fail because errors, costs, and context grow across steps. This skill provides patterns for bounded planning, execution, reflection, recovery, and human approval.

Autonomous coding agents need reliable loops, tool contracts, permissions, and context handling. This skill provides Python patterns for architecture, browser automation, checkpoints, and MCP integration.

Async Python code can fail through blocking calls, weak cancellation, or uncontrolled concurrency. This skill gives practical asyncio patterns for reliable services, APIs, scrapers, and tests.

ARM Cortex-M firmware can fail because of interrupt races, cache incoherence, and incorrect peripheral access. This skill provides platform-specific design, implementation, and validation guidance.

Architecture choices often outpace clear requirements and trade-off analysis. This skill structures discovery, pattern comparison, and decision records for explainable system designs.

Complex system changes can hide scalability, resilience, security, and maintainability risks. This skill reviews architecture decisions and provides prioritized recommendations with tradeoffs and validation steps.

Application bottlenecks often span services, databases, browsers, and infrastructure. This skill coordinates profiling, targeted optimization, load testing, and continuous monitoring across the stack.

Starting a new app often requires many architecture and tooling decisions. This skill turns a plain-language request into a template-guided plan, stack choice, and agent workflow.

Mock APIs are hard to keep realistic, aligned with contracts, and useful for tests. This skill helps design stateful mocks, scenarios, fixtures, deployment steps, and documentation.

Choosing an API style and consistent conventions requires many connected decisions. This skill provides focused guidance for design, security, evolution, validation, and documentation.

API teams need consistent contracts that are easy to use and maintain. This skill guides REST and GraphQL design with templates, checklists, and review criteria.