Skill UI

This SDK allows developers to build persistent, sophisticated AI agents directly on Azure AI Foundry. It supports complex conversational workflows by integrating external functions (tools), enabling code execution, web search, and maintaining conversation history through dedicated thread management. Ideal for developing enterprise-grade, production-ready AI applications.

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 for .NET that provides comprehensive client access to Azure AI Foundry services. It enables developers to manage complex AI workflows, including creating and managing conversational agents, handling data connections, uploading datasets, managing model deployments, and setting up search indexes within an Azure AI project structure. Ideal for integrating advanced AI capabilities into enterprise applications.

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.

A comprehensive guide to mastering AI-powered coding assistance within the IDE. Learn to use the chat panel for conversational debugging and architectural discussion, and utilize inline editing for surgical code refactoring. Features advanced context referencing using @-mentions (e.g., @Files, @Codebase) to improve code understanding and generation accuracy.

Guides engineers through selecting software architecture patterns, structuring projects, and documenting trade-offs via checklists, decision frameworks, and validation steps for system design conversations or new projects.

This comprehensive workflow guide covers all production-ready patterns for Mistral AI chat completions. It demonstrates critical advanced features including multi-turn conversation management, real-time streaming responses, structured JSON output using schemas, and implementing robust guardrails and moderation. Ideal for building scalable, production-grade conversational AI applications and advanced chat interfaces.

This skill provides comprehensive patterns for handling sensitive data when integrating with Mistral AI. It covers PII redaction (emails, phone numbers, etc.), implementing data retention policies, and enforcing compliance with global privacy regulations like GDPR and CCPA. Key features include safe API wrappers, dataset sanitization for fine-tuning, and conversation history management with TTL and user erasure rights.

This skill provides a comprehensive guide for setting up and managing webhook endpoints for Retell AI. It allows developers to capture and process crucial call lifecycle events—such as call start, end, and analysis—as well as handle function calls triggered during live conversations. Use this when building sophisticated voice agents or call automation systems that require real-time backend interaction with Retell AI.

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.