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.

A comprehensive, production-grade reference architecture for integrating with Apollo.io. It details a layered design encompassing API services, business logic, background job processing (e.g., lead enrichment), and data modeling, enabling the building of robust sales intelligence and CRM synchronization systems.

This module provides production-ready TypeScript patterns for interacting with the Apollo.io REST API. It implements critical best practices including type-safe client setup (using Zod), custom error handling, exponential backoff retry logic, asynchronous pagination, and rate-aware bulk processing. Use this when building reliable, scalable, and robust integrations with Apollo data.

This skill provides comprehensive guidance on implementing high-performance, non-blocking applications in Python. It utilizes asyncio, async/await, and advanced concurrency patterns to handle I/O-bound workloads efficiently. Ideal for building async web APIs, real-time systems, and concurrent data processing.

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 is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

This is a high-level SDK designed for managing the complete lifecycle of AI projects within Azure AI Foundry. It provides comprehensive tools in TypeScript to create, manage, and evaluate sophisticated AI agents, handle resource connections, process datasets, deploy models, and build custom search indexes.

A high-throughput SDK for .NET applications to connect with Azure Event Hubs. This library enables reliable event publishing, supporting efficient batching and buffered sending for high-volume data streams. It also provides a robust, production-grade event consumption mechanism using EventProcessorClient, which manages checkpointing and ensures scalable, real-time data pipeline integration. Ideal for building microservices and streaming architectures.

A high-throughput SDK for .NET applications to connect with Azure Event Hubs. This library enables reliable event publishing, supporting efficient batching and buffered sending for high-volume data streams. It also provides a robust, production-grade event consumption mechanism using EventProcessorClient, which manages checkpointing and ensures scalable, real-time data pipeline integration. Ideal for building microservices and streaming architectures.

This library offers comprehensive Java clients for interacting with Azure Event Hubs. It enables developers to build robust, high-throughput, real-time streaming applications and event-driven architectures. Features include sending single events, batching events, sending to specific partitions, and using the production-grade EventProcessorClient with checkpointing for reliable data processing.

This library offers comprehensive Java clients for interacting with Azure Event Hubs. It enables developers to build robust, high-throughput, real-time streaming applications and event-driven architectures. Features include sending single events, batching events, sending to specific partitions, and using the production-grade EventProcessorClient with checkpointing for reliable data processing.