Skill UI

Automates GraphQL resolver creation for backend teams, giving step-by-step guidance, industry best practices, and production-ready code/configs to validate resolvers across Node.js, Python, Go, database design, caching, messaging, and microservices.

This skill provides comprehensive guidance for senior-level Laravel engineering. It focuses on building production-grade, maintainable, and highly secure applications using modern architectural patterns (clean architecture, services, etc.). Use this when developing new features, refactoring complex code, designing robust APIs, or optimizing database interactions within a Laravel environment. Adheres to best practices like Eloquent optimization and proper authentication flows.

Automates designing and implementing table partitioning strategies to keep large databases performant, optimize time-series workloads, and shorten maintenance windows, with guidance on key selection, SQL scripts, and query tuning.

Guides designing and implementing horizontal database sharding, covering key selection, migration planning, and cross-shard coordination for scalable systems.

Catalogs proven n8n workflow architectural patterns for webhook handling, APIs, database syncing, AI agents, and scheduled tasks, helping teams pick structures, plan nodes, and handle triggers, data paths, and error strategies during workflow design and troubleshooting.

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.

This skill provides a comprehensive guide to PostgreSQL best practices and performance optimization, maintained by Supabase. It covers critical areas such as query tuning, schema design, connection management, security (RLS), and indexing. Use this guide when writing complex SQL, reviewing database architecture, or diagnosing performance bottlenecks to ensure robust and efficient database operations.

A comprehensive expert guide for mastering Prisma ORM. It covers deep knowledge of schema design best practices, managing complex database migrations across PostgreSQL, MySQL, and SQLite, and optimizing various query patterns (N+1, select, raw queries). Use this skill to build robust, performant, and scalable backend services.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

This skill provides systematic patterns and best practices for transforming slow database queries into high-performance operations. It covers techniques like proper indexing, query plan analysis (e.g., using EXPLAIN), schema design improvements, and resolving common issues like N+1 query problems. Use it to debug performance bottlenecks, reduce database load, and ensure scalable application backends.