Skill UI

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

Guides teams through assessing business goals, designing measurement plans, and implementing GA4/GTM tracking so marketing and product decisions rely on validated events, UTM strategies, and clean conversions.

Guides teams through assessing business goals, designing measurement plans, and implementing GA4/GTM tracking so marketing and product decisions rely on validated events, UTM strategies, and clean conversions.

A master-level architect specializing in reviewing and guiding complex, modern distributed systems. This skill excels at evaluating adherence to patterns like Clean Architecture, DDD, and Microservices. Use it when assessing scalability, resilience, security posture, and maintainability across major design changes, ensuring the resulting system is robust and future-proof.

This skill provides expertise in proven backend architectural patterns, including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design (DDD). It guides users in building highly maintainable, testable, and scalable systems. Use it when designing new services, refactoring complex monoliths, establishing team standards, or planning microservice decompositions. It emphasizes defining clear boundaries and handling complex, durable workflows.

This skill provides expertise in proven backend architectural patterns, including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design (DDD). It guides users in building highly maintainable, testable, and scalable systems. Use it when designing new services, refactoring complex monoliths, establishing team standards, or planning microservice decompositions. It emphasizes defining clear boundaries and handling complex, durable workflows.

A comprehensive guide detailing best practices for creating modern, clean, and highly maintainable user interfaces using Avalonia and Zafiro.Avalonia. It emphasizes leveraging semantic containers, defining shared styles, and utilizing generic components to dramatically reduce XAML redundancy and improve code structure. Learn to adopt patterns like Behaviors over Converters, and prevent deep nesting for optimal developer experience.

This is a low-level SDK for Java designed to create and manage persistent AI agents within Azure AI. It provides comprehensive tools for handling the entire agent lifecycle, including creating agents, managing conversation threads, sending messages, running agent processes, and cleaning up resources. Ideal for building robust, stateful AI applications requiring fine-grained control over agent interactions.

This is a low-level SDK for Java designed to create and manage persistent AI agents within Azure AI. It provides comprehensive tools for handling the entire agent lifecycle, including creating agents, managing conversation threads, sending messages, running agent processes, and cleaning up resources. Ideal for building robust, stateful AI applications requiring fine-grained control over agent interactions.

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.

Automate Composio Campaign Cleaner operations via Rube MCP, always starting with `RUBE_SEARCH_TOOLS` to discover the current schemas, connecting the toolkit, and invoking `RUBE_MULTI_EXECUTE_TOOL` with schema-compliant arguments and session handling.

A comprehensive guide to optimizing data enrichment workflows within Clay. Learn techniques to improve throughput, reduce API costs, and maximize data hit rates by correctly ordering enrichment columns, implementing conditional run rules, pre-cleaning raw leads, and managing auto-update settings. Use this when processing large datasets slowly or encountering high costs.