Skill UI

Provides a practical checklist and principles for designing database schemas, indexing strategies, query optimization, and migration planning for both SQL and NoSQL systems, plus guidance on spotting and fixing N+1 issues and denormalization trade-offs.

This comprehensive skill covers the full scope of Firebase services, including Authentication, Firestore, Cloud Functions, and Hosting. It goes beyond basic implementation, focusing on critical architectural principles: adopting a query-first, denormalized NoSQL mindset. Key insights include mandatory security rule design, optimal data modeling for performance, and correctly utilizing modular SDKs to build scalable and secure backends.

Expert guidance on designing highly scalable, distributed NoSQL databases, specifically covering Apache Cassandra and Amazon DynamoDB. This skill emphasizes the 'Query-First' modeling approach, teaching best practices like partitioning, denormalization, single-table design, and avoiding common anti-patterns (e.g., hot partitions, joins) to ensure optimal read/write performance at massive scale.

A guide to building robust, type-safe, and highly testable backend services using functional programming patterns in Node.js or Deno. This pattern utilizes fp-ts and ReaderTaskEither to manage dependency injection, compose complex business logic, and handle typed errors gracefully, promoting clean service layer architecture.

Hono is a minimal and ultrafast web framework built entirely on Web Standards (Request/Response/fetch). It allows developers to build high-performance APIs and full-stack applications that run seamlessly across multiple environments, including Cloudflare Workers, Deno, Bun, and Node.js. It is ideal for edge deployment, Backend for Frontend (BFF) layers, and type-safe backend services.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

This skill guides the execution and reporting standards for advanced demographic analysis, particularly for Population and Development Reviews (PDR). It ensures that demographic rates, life tables, and projections are not only statistically rigorous (handling uncertainty, denominators, and complex inference) but also clearly interpreted in the context of development, policy, and socio-economic meaning. It focuses on reproducibility and methodological transparency.