Skill UI

This skill provides a structured, multi-step workflow for resolving conflicts encountered during in-progress Git merges or rebases. It guides users through analyzing conflict origins, preserving original development intents, resolving conflicting code hunks without introducing new behavior, and executing comprehensive automated checks (typechecking, testing, formatting) to ensure the resulting codebase is stable and functional.

This skill covers the principles of building Software Development Kits (SDKs) that provide an exceptional developer experience (DX). Learn how to design intuitive APIs, utilize progressive disclosure, establish sensible defaults, and implement actionable, guiding error messages. The focus is on driving product adoption by making the developer's workflow seamless and productive. Ideal for product managers, technical leads, and API designers.

A comprehensive guide for developers working with Supabase. Covers best practices for database interaction, robust authorization using Row Level Security (RLS), secure authentication (Auth), and integrating various Supabase products (Storage, Realtime, Edge Functions). Essential for preventing common security vulnerabilities like BOLA/IDOR and ensuring data integrity across your application stack.

This meta-skill acts as a routing layer for complex UI development tasks. It guides the process of selecting the smallest, most specific, and useful skill context using the ui-skills CLI. Use it when starting UI work to analyze goals, identify the correct category, and pinpoint the precise skill set needed for implementation, ensuring development efficiency and accuracy.

This skill systematically scans Apple's comprehensive developer documentation, specifically targeting SwiftUI. It identifies deprecated APIs, finds their modern replacements, and updates the expert skill's reference guide. Use this when required to refresh deprecated features, check for new API deprecations after an iOS/Xcode update, or maintain the most current best practices for SwiftUI development.

Generates championship-grade, F1-inspired test suites based on the WJTTC methodology. This skill enforces a 'tests first' philosophy, analyzing the codebase to define structured test plans. It covers standard industry unit tests (Layer 1) and deep expert edge cases (Layer 2). Furthermore, it includes Signal Integrity Audits to ensure the test suite's reliability is paramount, guiding development toward maximum code quality.

A specialized presentation deck template adopting a cyberpunk, CRT terminal aesthetic. It is ideal for conducting deep technical reviews, such as benchmarking CLI agents, demonstrating code diffs, or presenting complex development tool analyses. Features a dark background, grid lines, command prompt indicators, and structured tagging for maximum professional impact.

This guide details the comprehensive workflow for distributing a single AI skill across various local agent skill directories (Codex, Claude, Pi, Hermes). It addresses canonical locations, symlink management, and specific synchronization commands to ensure that all agents detect the latest skill version while correctly navigating common development pitfalls.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.