Skill UI

An advanced AI-driven framework for holistic performance engineering of multi-agent systems. It provides coordinated profiling, workload distribution, and cost-aware orchestration across various domains (database, application, frontend). Use this tool to identify bottlenecks, improve throughput, enhance reliability, and manage resource constraints in complex agent workflows.

Enables Claude to analyze request and data throughput, detect bottlenecks, and suggest scaling strategies via the throughput-analyzer plugin when performance or capacity optimization is requested.

A comprehensive, multi-step guide for migrating contact and company data into Apollo.io. Use this skill when switching from other CRMs (like Salesforce or HubSpot), consolidating multiple data sources, or executing large-scale data imports. It covers defining field mappings, pre-migration data assessment, and performing high-throughput batch processing using the Apollo Contacts API.

The official Java SDK for Azure Cosmos DB, providing comprehensive client capabilities for NoSQL operations. It supports global distribution, multi-model data structures, and reactive patterns (async/await). Ideal for developing high-throughput, scalable enterprise applications in Java that require reliable, low-latency data storage.

A comprehensive guide and client library for interacting with Azure Cosmos DB, a globally distributed, multi-model NoSQL database, using Python. It provides full support for performing document CRUD operations, executing complex partitioned and cross-partition queries, managing containers, and handling advanced concepts like throughput and asynchronous operations.

A comprehensive Python SDK for interacting with Azure Table Storage and Cosmos DB Table API. This library enables developers to perform full NoSQL key-value operations, including creating, reading, updating, and deleting (CRUD) entities, executing complex queries using filtering, and managing batch transactions. It supports both synchronous and asynchronous operations, making it ideal for building resilient, high-throughput cloud applications.

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.

This SDK provides comprehensive Python support for interacting with Azure Event Hubs, a high-throughput streaming platform. It allows developers to build robust data ingestion pipelines, acting as both event producers (sending data) and event consumers (receiving data). Key features include efficient batch processing, partition key management, and reliable checkpointing using Azure Blob Storage for production-grade stream applications.

This SDK provides comprehensive Python support for interacting with Azure Event Hubs, a high-throughput streaming platform. It allows developers to build robust data ingestion pipelines, acting as both event producers (sending data) and event consumers (receiving data). Key features include efficient batch processing, partition key management, and reliable checkpointing using Azure Blob Storage for production-grade stream applications.