Skill UI

Manage Azure Bot Service resources with the .NET Azure.ResourceManager.BotService SDK, covering provisioning, updates, channel configuration, and key regeneration across Teams, Web Chat, Direct Line, Slack, and more.

Python SDK for managing Azure Bot Service resources, enabling bot creation, channel configuration, connection settings, and lifecycle operations across resource groups and subscriptions.

Python SDK for managing Azure Bot Service resources, enabling bot creation, channel configuration, connection settings, and lifecycle operations across resource groups and subscriptions.

Instrument Node.js services with Azure Monitor's OpenTelemetry SDK, enabling distributed traces, metrics, and logs via auto-instrumentation, manual exporters, and custom telemetry uploads.

Azure.Messaging.ServiceBus (.NET) delivers enterprise-grade queues, topics, subscriptions, sessions, dead-letter handling, and background processors to build reliable Azure Service Bus messaging solutions.

Provides Python SDK access to Azure Service Bus queues, topics, and subscriptions with async send/receive flows, session-aware routing, scheduled messages, and robust settlement for enterprise messaging scenarios.

Client library for TypeScript to send, receive, and manage enterprise messages via Azure Service Bus queues, topics, subscriptions, sessions, and dead-letter handling in distributed applications.

Rust client SDK for Azure Blob Storage that handles container management and blob uploads/downloads with async-friendly APIs, making it easy to integrate Microsoft object storage into Rust services.

This skill defines mandatory architectural standards and best practices for developing robust, scalable, and observable backend services using Node.js, Express, and TypeScript. It mandates layered architecture (Routes → Controllers → Services → Repositories), strong error handling, dependency injection, and input validation (Zod) to ensure maintainability and reliability in production environments. Use when designing or refactoring core API logic.

Automated assistant that walks through backend background worker setup, covering node.js, Python, Go, databases, caching, messaging, and microservices best practices.

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.

Automates creating and validating GCP BigQuery views with best-practice guidance, production-ready code snippets, and integrations for Vertex AI, Cloud Run, Firebase, and similar services to keep analytics workloads consistent.