Skill UI

This skill generates comprehensive, production-ready database schema documents. It covers Entity-Relationship (ER) diagrams, detailed SQL DDL definitions, constraint analysis, index strategies, and access pattern optimization. Use it when designing a new database, documenting an existing one, or modeling complex data relationships for engineering review.

A comprehensive Notion dashboard designed for real-time team operations and performance tracking. It provides a centralized overview by aggregating Key Performance Indicators (KPIs) in a grid format, displaying 7-day trend sparklines, showing live activity feeds, and summarizing tasks in a structured database table. Ideal for project managers and team leads who need a single source of truth for project status and team coordination.

A comprehensive guide covering scalable backend architecture patterns, including Repository, Service, and Middleware design. It details best practices for building robust APIs using Node.js, Express, and Next.js. Topics covered include RESTful API structuring, database query optimization (preventing N+1 issues), implementing database transactions, and integrating caching strategies like Redis. This guide is essential for developing high-performance, maintainable server-side applications.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

Reference patterns and best practices for MySQL, covering schema design, indexing strategy, query optimization and common SQL pitfalls. Useful when building or reviewing database layers in applications.

A comprehensive guide to PostgreSQL best practices, covering advanced indexing (GIN, BRIN, Partial), schema design, performance tuning, and security implementation (RLS). It details optimizing common SQL patterns like cursor pagination and UPSERT, essential for building high-performance, reliable database systems.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This guide provides comprehensive instructions for creating a robust, reproducible artifact package for database systems research. It details best practices including containerization, pinning workloads, generating a permanent DOI-archived record, and designing the package to be successfully built and demonstrated on a reviewer's clean machine within a tight time budget. The goal is to strengthen scientific rigor and ensure permanent, open-access citation.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

A comprehensive guide for designing and documenting systematic literature searches for meta-analyses or systematic reviews. It ensures reproducibility by covering multiple databases (e.g., PsycINFO, PubMed), controlled vocabulary, grey literature, and adhering strictly to PRISMA-S guidelines. It helps build a robust, traceable research pool.

A comprehensive guide for designing and presenting highly rigorous technical evaluations for database or system papers. It covers establishing baseline necessity, proving mechanism causality through ablation, testing scalability across data sizes, and mandating the disclosure of loss cases to satisfy skeptical academic reviewers.

A detailed guide for revising academic papers, specifically tailored for systems and database conferences like VLDB. It emphasizes establishing a strong 'page-one contract,' quantifying all performance claims with scoped metrics and baselines, and maintaining rigorous candor regarding design trade-offs and terminology throughout the manuscript.