Skill UI

A comprehensive guide for implementing user interfaces that mimic spatial computing devices (like Apple Vision Pro). It focuses on creating digital elements with a sense of depth (Z-Space), utilizing glass-like materials via backdrop blur, and designing gaze-aware interactions, making digital information feel physically anchored in the real environment.

This skill guide provides comprehensive implementation details for the Metro/Modern UI style, mimicking the aesthetic of Windows Phone/8 start screens. It focuses on creating sharply cornered, flat-color tiles arranged in a horizontally scrolling grid. Key features include live data updates (e.g., tile flipping) and replicating the classic interactive 'tilt' effect. Applicable for cross-platform development using Web (CSS), iOS (SwiftUI), and Flutter.

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.

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.

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 outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

Programmatically manage your entire contact database within Mailtrap. This guide covers using the API to create, update, delete, and bulk import contacts. It is essential for synchronizing contact data with CRMs, triggering custom automation events, building targeted segments, or performing large-scale list cleanups.

This comprehensive guide details how to configure, integrate, and troubleshoot live email sending using Mailtrap. It covers two primary methods: the Email API (REST) and SMTP. Learn the differences between core email streams—transactional (app-generated notifications), bulk (promotional marketing), and batch requests (high-volume efficiency)—along with recommended integration patterns and specific technical endpoints.

This guide details the process of adding, verifying, and publishing necessary DNS records (SPF, DKIM, DMARC) for a custom sending domain in Mailtrap. It covers both manual compliance steps via the UI and programmatic automation through the Sending Domains API. Essential for ensuring high email deliverability and authentication compliance.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.

This skill guides the systematic triage of project issues and external pull requests (PRs). It moves development items through a defined state machine using roles (e.g., bug, enhancement) and states (e.g., ready-for-agent, wontfix). The process involves context gathering, cross-codebase verification, deep questioning (grilling), and generating comprehensive, agent-ready briefs or marking them for human implementation.

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.