Skill UI

A comprehensive guide to building production-ready Apache Airflow Directed Acyclic Graphs (DAGs). This skill covers best practices for operators, sensors, testing, deployment, and designing robust data pipelines. Use this when orchestrating complex workflows, managing ETL processes, or ensuring reliable batch job execution in a production environment.

Manage Azure Machine Learning resources including workspaces, data assets, models, compute clusters, jobs, pipelines, and environments using the Azure ML SDK v2 for Python. Ideal for end-to-end ML workflow automation.

Client library for Azure Event Hubs in Rust, enabling sending and receiving events, batch processing, and streaming data ingestion. Ideal for building scalable event-driven applications with high-throughput data pipelines.

This skill covers using the Azure Event Hubs SDK for TypeScript to build high-throughput event streaming applications. It includes real-time data ingestion, sending events to specific partitions, receiving events with checkpointing for production reliability, and handling batch processing options. Ideal for IoT telemetry and real-time data pipelines.

This toolkit provides comprehensive automated control over CircleCI CI/CD pipelines via Rube MCP. Users can trigger new builds, monitor workflow and job statuses, retrieve detailed job information, download build artifacts, and extract detailed test metadata. It streamlines complex DevOps operations by managing the entire CI/CD lifecycle.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

Expert data engineer specializing in scalable data pipelines, modern data warehouses, lakehouse architectures, and real-time streaming. Proficient in Apache Spark, dbt, Airflow, Kafka, and cloud-native platforms (AWS, Azure, GCP). Focuses on reliable, performant, and cost-effective data solutions with strong emphasis on data quality and governance.

Design scalable, reliable and cost-effective batch and streaming data pipelines. Covers ETL/ELT, Lambda, Kappa and Lakehouse patterns, Airflow/Prefect orchestration, dbt and Spark transformations, Delta Lake/Iceberg storage, data quality checks and cost optimization.

This skill enables implementing data quality validation using Great Expectations, dbt tests, and data contracts. It is used for building reliable data pipelines, setting up validation rules, and establishing data contracts between teams. It includes automating validation in CI/CD and monitoring data quality metrics to ensure data reliability in production environments.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.

This skill enables the creation of comprehensive monitoring solutions for database migrations. It covers Change Data Capture (CDC) pipelines, real-time data synchronization, anomaly detection, and automated alerting. You will learn to build observability infrastructure using tools like Prometheus, Grafana, Kafka, and Debezium to track migration metrics, document processing, and replication lag in enterprise environments.

Quick reference for using pipe and flow in fp-ts to chain functions, compose operations, and build reusable data pipelines with TypeScript.