Skill UI

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.

A comprehensive client library for interacting with Azure Event Hubs in Rust. It facilitates robust data streaming ingestion by providing functionality for both producers (sending event batches) and consumers (receiving and processing events from partitions). Designed for building high-throughput, real-time, event-driven data pipelines and architectures.

A comprehensive client library for interacting with Azure Event Hubs in Rust. It facilitates robust data streaming ingestion by providing functionality for both producers (sending event batches) and consumers (receiving and processing events from partitions). Designed for building high-throughput, real-time, event-driven data pipelines and architectures.

This tool generates comprehensive API contracts and OpenAPI specifications (3.0/3.1) from existing codebases, design documents, or schemas. It establishes a single source of truth for APIs by producing machine-readable contracts, supporting documentation, robust testing (including consumer-driven testing via Pact), and code generation. Use it when you need to define, verify, and document complex service interfaces.

This guide details the use of the GGUF format and quantization techniques for running large language models (LLMs) efficiently. GGUF enables optimal inference on consumer hardware, Apple Silicon, and systems requiring CPU-only operation. By compressing models using various K-quant methods (like Q4_K_M), developers can significantly reduce memory footprint and hardware requirements while maintaining high performance.

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Implements event-driven architecture by integrating Guidewire App Events and webhooks. This allows developers to build event consumers that process critical InsuranceSuite state changes—such as claim creation or policy issuance—in near-real-time, enabling robust, reactive business logic.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

Validates API contracts using Pact and OpenAPI, generating consumer-driven contract tests, verifying responses, and ensuring backward compatibility for CI/CD workflows.

This skill is designed to streamline the process of market research. It helps users gather comprehensive data on target markets, analyze industry trends, and identify key consumer insights. Use it to inform product development, refine business strategies, and make data-driven decisions.