Skill UI

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

Apply Robert C. Martin-inspired clean code rules to improve code readability, naming conventions, function design, and responsibilities. This skill ensures everyday implementation quality, readable control flow, and testable code by following established best practices for software development.

Apply Vaughn Vernon-inspired rules for building aggregates, domain events, repositories, and application services. This skill helps structure code using bounded contexts and domain logic, ensuring clean architecture and maintainable software design during development.

Apply Martin Fowler-inspired refactoring rules to improve existing code structure while preserving observable behavior. Covers code smells, small safe refactoring steps, test-backed cleanup, and separating refactoring from feature changes.

This skill applies Refactoring.Guru-inspired rules for diagnosing code smells and selecting appropriate refactoring techniques. It helps preserve behavior while avoiding uncontrolled redesign. Before making design or code decisions, read the mini rules first. Use the full guide only as a deeper reference when needed. It ensures targeted cleanup without over-engineering.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

Quickly polish UI code by fixing spacing, hierarchy, typography, and small layout issues. Applies an opinionated baseline for a scoped cleanup, respecting existing project constraints and accessibility.

This skill guides you through a comprehensive, multi-phase interview to capture every necessary detail of a new project idea. It is designed to run BEFORE any code is written, forcing detailed specification gathering across purpose, features, data models, and technical constraints. The output is a clean, self-contained prompt.md file, ensuring subsequent development is based on solid, unambiguous requirements and preventing costly mistakes.

This skill performs a full-sweep analysis across all quality dimensions including code decay, architecture, tech debt, and test quality. It applies fixes directly to the codebase, auto-applying safe changes and confirming risky ones before execution. It iterates through reviews, tests, debt, and audits to converge on a clean state, generating a comprehensive Full Sweep Report.

This skill reviews generated or changed production code against Clean Code principles (SOLID, DRY, KISS, YAGNI) and LLM-specific failure modes. It operates in guard-pass, live, and review modes to ensure code quality before delivery, fixing violations and preventing systematic AI errors.

A constraint-based protocol designed to professionalize existing code by normalizing comments and performing safe, non-semantic cleanup. This skill converts informal, outdated, or missing documentation into clear, concise, and professional standards without altering the core logic or behavior. Ideal for preparing code for code review, open-sourcing, or formal documentation.

Refactor tangled or hard-to-maintain code into clean, testable modules using SOLID principles. Assess code smells, plan incremental changes, and verify with tests for safer long-term maintenance.