Skill UI

A comprehensive SDK for running Playwright end-to-end tests at scale. It leverages cloud-hosted browsers within the Azure ecosystem, allowing developers to execute reliable, large-scale automated tests. Features include integrated Azure portal reporting, seamless authentication using Microsoft Entra ID, and strong support for CI/CD pipelines.

A comprehensive SDK for running Playwright end-to-end tests at scale. It leverages cloud-hosted browsers within the Azure ecosystem, allowing developers to execute reliable, large-scale automated tests. Features include integrated Azure portal reporting, seamless authentication using Microsoft Entra ID, and strong support for CI/CD pipelines.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

This skill provides comprehensive scripts and workflows to automatically identify and remove unused AWS resources across multiple services (EBS, S3, EC2, EIPs). By automating the cleanup of orphaned volumes, old snapshots, and stale data, users can eliminate cloud waste, significantly optimizing their AWS expenditure and ensuring a cost-efficient infrastructure.

A comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

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.

A Java exporter that facilitates sending OpenTelemetry telemetry data—including traces, metrics, and logs—from Java applications directly to Azure Monitor or Application Insights. It is crucial for achieving centralized, comprehensive observability into enterprise applications.

This library serves as a low-level exporter for Python applications, enabling the reliable transfer of comprehensive OpenTelemetry telemetry data—including traces, metrics, and logs—to Azure Application Insights. It provides robust configuration options, including connection string setup, Azure AD authentication, custom sampling, and offline storage management, ensuring complete and reliable observability.

This library provides a straightforward, one-line setup for integrating Python applications with Azure Application Insights. It leverages OpenTelemetry for automatic instrumentation across popular frameworks (Flask, Django, FastAPI) and various libraries, enabling comprehensive monitoring of traces, metrics, and logs in a single setup.

This SDK provides comprehensive support for automatically instrumenting Node.js applications to collect observability data. It integrates distributed tracing, metrics, and logs using the OpenTelemetry standard, allowing seamless data export and monitoring within Azure Monitor. It supports various resource types and connection methods.

This SDK provides a comprehensive Java client for interacting with Azure Monitor. It allows developers to execute powerful Kusto Query Language (KQL) queries against Log Analytics workspaces to retrieve detailed operational logs, and to query time-series metrics from various Azure resources. It supports both synchronous and asynchronous operations, making it ideal for building monitoring, auditing, and analytics features in Java applications.