Skill UI

Utilizes the Azure AI Projects SDK's ImageBasedHostedAgentDefinition to build robust, container-based AI agents deployed in Azure AI Foundry. This workflow is essential for creating production-grade agents that require custom logic, precise resource allocation (CPU/Memory), and integration of advanced tools like code interpreters.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

Automates cpu profiler configuration guidance for performance testing, covering load, stress, benchmarking, and monitoring scenarios to ensure best practices.

Analyzes CPU, memory, I/O, and database traces to find and fix bottlenecks, offering remediation strategies when diagnosing slow applications, optimizing resource use, or preventing performance issues.

Provides a comprehensive guide and Python implementation for creating container-based hosted agents in Azure AI Projects. It utilizes ImageBasedHostedAgentDefinition to define the agent's container image, required resources (CPU/memory), supported tools, and communication protocols. Essential for deploying robust, production-ready AI agents within the Azure ecosystem.

A systematic, multi-phase workflow designed for diagnosing and resolving complex Linux system issues. It guides users through initial assessment, resource analysis (CPU, memory, disk), process investigation, log analysis, network diagnostics, and service failure resolution. Ideal for system administrators and DevOps engineers handling production incidents.

Enables Claude to monitor and analyze application CPU usage, detect hotspots, inefficient loops and regexes, and offer optimization strategies such as asynchronous processing or algorithmic improvements to boost performance.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.