Skill UI

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.

This SDK enables Java developers to build real-time, scalable web applications using Azure Web PubSub. It leverages WebSocket-based messaging to implement critical features such as live chat applications, instant server-to-client push notifications, and real-time data synchronization. Key functionalities include targeted message delivery (to all, groups, or specific users), connection management, and secure client access token generation.

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.

A comprehensive Python SDK for interacting with Azure Queue Storage. This tool enables reliable, asynchronous communication patterns by managing message queuing, task distribution, and data processing workflows. It supports core operations including sending, receiving, peeking, updating, and deleting messages, making it ideal for building resilient, decoupled backend systems on Azure.

Automates Box cloud storage operations through Rube MCP, covering uploads, downloads, content search, folder lifecycle, sharing, collaborations, metadata queries, and sign requests; always start with RUBE_SEARCH_TOOLS to sync the current schema.

Learn how to configure and implement robust backend endpoints to handle webhooks from Clerk. This guide covers processing user lifecycle events (creation, update, deletion) and organization events, ensuring seamless data synchronization to your external database. It emphasizes secure practices like Svix signature verification and idempotency protection.

A comprehensive guide for designing production-grade lead enrichment pipelines in Clay. Covers three integration patterns (Webhook, API), detailed table schema design, advanced data waterfall logic, implementing complex ICP scoring formulas, and setting up robust CRM synchronization flows for go-to-market data operations.

Compares PostgreSQL or MySQL schemas using the database-diff-tool, produces transaction-safe migration scripts and rollback procedures, and outlines synchronization steps for staging/production deployments.

Specializing in building high-performance, cloud-native applications using C# and the latest .NET 8+ framework. This skill covers end-to-end development, including building robust RESTful APIs (Minimal/Controller), implementing data access with Entity Framework Core, structuring code using CQRS/MediatR, and scaffolding Blazor components. Focuses on modern best practices like async/await, DTO mapping, and proper error handling.

A robust solution for implementing and handling real-time webhooks from Customer.io. This tool processes critical marketing events, including delivery status, opens, clicks, bounces, and unsubscribes. Key features include HMAC signature verification for security, reliable asynchronous event processing, and structured routing to manage various customer interactions, allowing data to be streamed efficiently to a data warehouse or CRM.

This guide provides expert patterns for building robust, observable infrastructure for database migrations. It covers real-time monitoring using Change Data Capture (CDC), metrics collection (Prometheus/Grafana), and anomaly detection. Use this skill when setting up CDC pipelines (e.g., Debezium with Kafka) or implementing comprehensive observability for database schema changes and data synchronization processes.

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.