Skill UI

A comprehensive guide for designing and implementing robust Infrastructure as Code (IaC) using Terraform or OpenTofu. Covers module hierarchy design, best practices for naming conventions, structuring multi-environment pipelines, advanced testing strategies (native tests, Terratest, static analysis), and integrating security scanning (trivy, checkov) into CI/CD workflows.

A comprehensive guide to writing idiomatic and efficient Go code. It covers best practices for error handling (using `%w`), managing slices and maps, implementing robust concurrency with goroutines and channels, designing small composable interfaces, and avoiding common anti-patterns. Essential for improving code quality in Go projects.

A comprehensive guide for designing and implementing professional, data-driven dashboards across web, iOS, and Flutter. It details core principles like modular grid layouts, establishing data hierarchy (KPIs, charts), and maintaining minimalist aesthetics. Includes specific code examples using CSS Grid, SwiftUI, and Flutter widgets to ensure optimal user experience and data clarity.

A comprehensive style guide and implementation reference for the Retro Futurism aesthetic. Perfect for designing interfaces that evoke the 1950s and 1960s space age, atompunk, or mid-century modern vibes. It outlines core principles, color palettes, typography, and provides cross-platform code examples (CSS, SwiftUI, Flutter, React Native) to achieve sleek, aerodynamic, chrome finishes.

A comprehensive guide for designing futuristic, tactical Heads-Up Displays (HUDs) and user interfaces. This skill provides core principles, visual DNA, and ready-to-implement code snippets for Web (CSS/SVG), App (SwiftUI), and Mobile (Flutter) platforms. Perfect for creating high-tech, sci-fi, or military-themed dashboards, featuring circular radars, minimalist strokes, and technical monospace typography.

A comprehensive guide and shared vocabulary for designing and structuring complex software modules. Use this when you need to refine an existing module's interface, identify opportunities for deepening its capabilities, determine optimal internal seams, or improve code testability and AI-navigability. It establishes clear principles for Module, Interface, Depth, and Seam.

This skill provides comprehensive best practices for designing modern and robust React user interfaces. It covers critical patterns for managing asynchronous data, including optimal loading state indicators (skeletons vs. spinners), structured error handling hierarchies, and designing contextual empty states. Essential for improving user experience during data fetching and operations.

This comprehensive guide outlines industry best practices for designing robust and consistent REST APIs. It covers proper resource naming conventions, standardized HTTP method semantics, detailed HTTP status codes (e.g., 201, 404, 422), structured response formats (including pagination and links), and advanced strategies like cursor-based pagination. Essential for building scalable and maintainable APIs.

This comprehensive guide outlines an opinionated set of conventions for building robust Ruby on Rails applications. It covers core architectural principles, including rich models, state-as-records, minimal dependencies, and best practices for CRUD operations and testing. It is mandatory to consult these guidelines whenever designing, writing, reviewing, or refactoring any Rails code.

This guide outlines the rigorous methodological standards for empirical work in AI fairness, accountability, and transparency (FAccT). It advises researchers on matching specific claims (e.g., disparate harm) to appropriate evidence types—including disaggregated metrics, coded qualitative data, and documented ethical procedures. It stresses the importance of ethical review, handling protected attributes, and acknowledging limitations to ensure high scientific and ethical rigor.

A guide for designing and structuring empirical tables and figures for submissions to International Organizations (IOs). Focuses on meeting strict word limits, ensuring exhibits are self-contained, reproducible, and clearly separating core arguments (main text) from supporting details (supplementary material). Best practices include explicit unit labeling, prioritizing effect plots over dense tables, and guaranteeing that all displayed numbers match the original analysis code.

A comprehensive guide for designing and reporting rigorous computational studies in Operations Research (OR) manuscripts. It covers best practices for selecting benchmark instances and strong baselines, applying statistical methods to stochastic output (e.g., confidence intervals), and adhering to mandatory code and data deposit standards (ORJournal workflow) to ensure full reproducibility.