Skill UI

A comprehensive guide on integrating Algolia search into modern React applications. This skill covers utilizing React InstantSearch with custom hooks (like useSearchBox, useHits) for type-ahead search, handling Next.js Server-Side Rendering (SSR) with InstantSearchNext, and implementing best practices for data synchronization and indexing strategies (e.g., partial updates, batching). Learn how to build high-performance, scalable search features.

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.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

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 comprehensive Java SDK client for securely and efficiently sending custom application logs to Azure Monitor. It utilizes Data Collection Rules (DCR) and Data Collection Endpoints (DCE) to manage the ingestion process. The client supports batch uploads, concurrent processing, and robust error handling, making it ideal for integrating custom logging into Azure Log Analytics workspaces.

This library serves as a low-level exporter for Python applications, enabling the reliable transfer of comprehensive OpenTelemetry telemetry data—including traces, metrics, and logs—to Azure Application Insights. It provides robust configuration options, including connection string setup, Azure AD authentication, custom sampling, and offline storage management, ensuring complete and reliable observability.

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

This SDK provides a comprehensive Python interface for interacting with Azure Blob Storage, Microsoft's scalable object storage service. It enables users to perform core object storage operations, including uploading, downloading, listing, deleting blobs, managing containers, and setting custom metadata. The library supports both synchronous and asynchronous workflows, making it ideal for building robust data pipelines, data lakes, or cloud-native applications that require reliable, scalable file storage.