Skill UI

Automates aria attribute helper support for frontend development requests by offering guidance, best practices, production-ready code/configuration, and validation whenever aria attribute helper topics or patterns are mentioned.

This expert skill guides advanced integration with Atlassian products (Jira and Confluence) using the MCP protocol. It covers full lifecycle management, including setting up OAuth/API token authentication, designing complex JQL/CQL queries, automating sprint workflows, syncing documentation, and robust debugging of API calls. Ideal for system architects and developers building cohesive enterprise workflows.

This expert skill guides advanced integration with Atlassian products (Jira and Confluence) using the MCP protocol. It covers full lifecycle management, including setting up OAuth/API token authentication, designing complex JQL/CQL queries, automating sprint workflows, syncing documentation, and robust debugging of API calls. Ideal for system architects and developers building cohesive enterprise workflows.

Provides expert AWS architecture, security, and implementation guidance backed by MCP tooling, helping teams choose services, migrate workloads, and troubleshoot production issues.

Provides canonical AWS serverless patterns covering Lambda handlers, DynamoDB single-table modeling, CDK infrastructure, and S3 event processing along with anti-patterns and a checklist for production-ready deployments.

This comprehensive guide covers the best practices for building production-ready, scalable serverless applications on Amazon Web Services (AWS). Learn to leverage core services like Lambda, API Gateway, and DynamoDB, and implement advanced patterns such as event-driven architectures (SQS/SNS). The content emphasizes performance optimization, robust error handling, structured logging, and deploying infrastructure using SAM/CDK.

This comprehensive guide covers the best practices for building production-ready, scalable serverless applications on Amazon Web Services (AWS). Learn to leverage core services like Lambda, API Gateway, and DynamoDB, and implement advanced patterns such as event-driven architectures (SQS/SNS). The content emphasizes performance optimization, robust error handling, structured logging, and deploying infrastructure using SAM/CDK.

A comprehensive solution for deploying multi-service, containerized applications (frontend + backend) to Azure Container Apps. This utility uses the Azure Developer CLI (azd) to manage the entire lifecycle, ensuring idempotent infrastructure provisioning, remote builds, managed identity integration, and robust environment configuration across development, staging, and production environments.

A comprehensive solution for deploying multi-service, containerized applications (frontend + backend) to Azure Container Apps. This utility uses the Azure Developer CLI (azd) to manage the entire lifecycle, ensuring idempotent infrastructure provisioning, remote builds, managed identity integration, and robust environment configuration across development, staging, and production environments.

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.

This comprehensive guide details how to build robust, production-grade NoSQL microservices using Python and Azure Cosmos DB. It emphasizes following architectural best practices, including the Service Layer pattern, TDD principles, secure authentication (DefaultAzureCredential), and implementing graceful degradation, making it ideal for developing enterprise-level backend systems.