Skill UI

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This guide details the principles and technical implementation of widget-based user interfaces, mimicking the aesthetic of modern mobile operating systems (like iOS Home Screen). It covers core design principles such as strict aspect ratios, glanceability, and corner radius matching. Practical code examples are provided for Web (CSS Grid), iOS (SwiftUI), and Cross-platform (Flutter) development, enabling designers to create modular, highly functional mini-apps.

Generate comprehensive onboarding documentation for software engineers ranging from newcomers to principals. This skill creates two complementary guides: a principal-level overview covering architecture, design principles, and decision logs, and a zero-to-hero contributor guide with setup instructions and workflows. It automatically detects programming languages and includes Mermaid diagrams for clarity.

A critical skill for reviewing WooCommerce extensions, payment integrations, and checkout logic before deployment. Ensures code follows security standards, HPOS compatibility, proper data handling, and runtime discipline to prevent systematic AI failures.

This document provides a comprehensive guide to the principles and vocabulary needed to write high-quality, predictable, and effective skills. It covers advanced concepts like model-invoked vs. user-invoked skills, managing context load, structuring information hierarchies (steps vs. reference), and implementing progressive disclosure to improve agent predictability.

This comprehensive guide provides developers with the foundational principles and actionable code examples for implementing the Y2K aesthetic. Learn how to achieve the signature look—incorporating chrome metallic effects, amorphous blob shapes, digital grid backgrounds, and neon glows—across web (CSS), iOS (SwiftUI), and cross-platform (Flutter) environments.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

This skill analyzes multiple asset classes, including US Treasuries, Dividend ETFs, REITs, and Preferred Stocks, to help users build a high-yield portfolio. By setting a target monthly income and available capital, it calculates optimal asset allocation and ranks opportunities based on risk-adjusted returns, supporting retirement and passive income planning.

This skill generates role-by-module permission matrices for business access control. It supports multiple formats like CSV, SQL, JSON Schema, or Notion. It helps define who can see or change data, ensuring security reviews and compliance. Ideal for growth-stage businesses needing clear access definitions.

This skill simulates a legal trial for your ideas using multiple AI agents. A prosecutor and defense attorney argue opposing sides, while a jury of sub-agents votes independently. A judge then reads the verdict and suggests changes to flip the outcome. It provides critical feedback and diverse perspectives instead of simple agreement, helping you evaluate plans, claims, or decisions through structured debate.

Builds disciplinary and performance improvement plan case registers with case type, linked reviews, goals, review dates, outcomes, and confidentiality settings. Produces CSV, SQL, JSON, or Notion templates on request.

A perfectionist mode for focused improvement on specific UI details or performance targets. Runs bounded attempts to find the best version, stops on budget. Works with multiple AI coding agents for targeted refinement and verification.