Skill UI

This skill acts as an expert resource for modern .NET development using C#. It guides the creation of enterprise-grade, high-performance applications by leveraging advanced C# features (like records, pattern matching), strictly adhering to SOLID principles, optimizing memory usage, and implementing comprehensive unit tests. Ideal for complex backend services and architecture tasks.

Guides CTOs and tech leaders through strategy, architecture governance, team scaling, and crisis response, with workflows for technical debt assessment, ADR creation, and vendor evaluation to keep engineering healthy.

A comprehensive guide on configuring and selecting the optimal AI model within the Cursor IDE. It details the strengths of various providers (OpenAI, Anthropic, Google) for specific coding tasks—from quick bug fixes to complex architecture planning—and guides users through model selection, API key setup (BYOK), and cost optimization strategies.

A comprehensive reference guide for structuring development projects within the Cursor IDE. It details best practices for directory layout, defining layered rules (covering global conventions, security constraints, and technology-specific patterns like API routes and database access), and optimizing indexing strategies. This ensures maximum AI effectiveness, improves code maintainability, and streamlines the entire development workflow.

A comprehensive reference architecture guide for implementing enterprise-grade integrations with Customer.io. It details best practices for building robust, scalable, and reliable systems, focusing on event-driven patterns, message queuing (e.g., Redis/Kafka), service layer separation, and idempotency. Ideal for designing complex data pipelines and ensuring high availability in mission-critical applications.

Expert in designing, building, and optimizing scalable data pipelines, modern data warehouses, and real-time streaming architectures. Mastery covers the complete modern data stack, including Spark, dbt, Airflow, and cloud services (AWS, Azure, GCP). Focuses on ELT/ETL, data lakehouse implementation (Delta Lake, Iceberg), data governance, and ensuring high data quality and operational reliability.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

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 provides expert guidance for modern, multi-cloud database administration. It covers comprehensive knowledge across AWS, Azure, and GCP platforms, spanning relational, NoSQL, and graph databases. Key areas include Infrastructure as Code (Terraform, Flyway), achieving high availability, disaster recovery, advanced performance optimization, and ensuring strict regulatory compliance (HIPAA, GDPR). It is essential for managing complex, highly reliable cloud data architectures.

An expert architect specializing in designing, optimizing, and scaling entire data layers from inception (greenfield) to re-architecture. Proficient in selecting the optimal database technology (SQL, NoSQL, Graph, Time-series), designing robust schemas, implementing sophisticated indexing strategies, and planning complex migration paths. Suitable for defining data storage solutions that meet high performance, scalability, and data integrity requirements.

This skill simulates a cloud cost optimization expert specializing in reducing infrastructure expenses across AWS, Azure, and GCP. It analyzes complex cloud spending patterns, identifies waste, overprovisioning, and potential savings opportunities. It provides actionable recommendations, implements cost controls, and proposes cost-effective, reliable architectures, ensuring cost efficiency without sacrificing performance or reliability.

This comprehensive guide outlines the end-to-end process of building production-grade ETL data pipelines using Databricks and Delta Lake. It implements the robust Medallion Architecture (Bronze, Silver, Gold) for scalable data management. Techniques covered include Auto Loader for incremental streaming ingestion, MERGE INTO for upsert semantics, data cleansing, and advanced aggregation into analytics-ready layers. Ideal for establishing reliable data pipelines in a data lakehouse environment.