Skill UI

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 comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

The official SDK for interacting with Azure AI Search service using Python. It provides comprehensive functionality for advanced search retrieval, including full-text search, vector similarity search (HNSW), hybrid search, and semantic ranking. Use this tool to build sophisticated enterprise search applications that require deep content understanding and precise information retrieval.

A Python implementation utilizing the Azure Speech-to-Text REST API for transcribing short audio files (up to 60 seconds). This method avoids the need for the dedicated Speech SDK, allowing developers to perform speech recognition simply by making HTTP POST requests. It supports both simple and detailed response formats and recommends chunked transfer for lower latency.

This skill defines structured behavioral frameworks (e.g., Brainstorm, Implement, Debug, Review, Teach, Ship) to guide the AI's output and approach based on the specific task phase. It optimizes performance by ensuring the AI adopts the correct mindset and output format—from initial ideation to final deployment—guaranteeing systematic, high-quality results across the entire development lifecycle.

Automated GCP skill that activates when you ask about BigQuery ML model creator tasks, delivering best-practice guidance, production-ready code, and validation for compute, storage, BigQuery, Vertex AI, and other platform services.

Bridges frozen vision encoders and LLMs for high-quality image captioning, VQA, retrieval, and multimodal chat, enabling zero-shot reasoning without fine-tuning and offering efficient Q-Former training.

Enables Claude to configure neural network architectures through the neural-network-builder plugin, creating or modifying models and defining layers, activation functions, and training parameters whenever network design or tuning is requested.

This skill serves as a comprehensive health assistant, designed to analyze complex medical information, track symptoms over time, and provide personalized wellness guidance. It is useful for users seeking structured health monitoring and preliminary insights into their condition, but it must not replace professional medical diagnosis or advice.

Provides expert guidance on designing, implementing, and optimizing cutting-edge computer vision pipelines. Expertise spans real-time object detection (YOLO26), advanced promptable segmentation (SAM 3), and leveraging Vision Language Models (VLMs) for complex visual reasoning. Ideal for building high-performance systems in robotics, autonomous vehicles, and industrial inspection, including 3D reconstruction and edge deployment optimization.

This skill analyzes the predictable ways language models lose performance as context length increases. It details core patterns, including 'lost-in-middle,' context poisoning, and distraction. Use this knowledge to diagnose unpredictable agent failures, design highly reliable systems for long document processing, and implement advanced context engineering strategies.

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.