Skill UI

A comprehensive guide detailing modern and idiomatic best practices for C# development. This reference covers advanced topics including LINQ optimization, null safety implementation, asynchronous programming patterns (Async/Await), and utilizing modern features like Records and Pattern Matching to write robust, high-performance code.

This skill optimizes articles for Answer Engine Optimization (AEO), ensuring content is structured for maximum extractability by AI models like ChatGPT and Perplexity. It rewrites vague headings into direct questions, inserts self-contained 50-80 word answer capsules, audits and trims long paragraphs, and highlights trust signals, making your content highly readable and citable by AI search engines.

This skill generates comprehensive, production-ready database schema documents. It covers Entity-Relationship (ER) diagrams, detailed SQL DDL definitions, constraint analysis, index strategies, and access pattern optimization. Use it when designing a new database, documenting an existing one, or modeling complex data relationships for engineering review.

This guide details the entire lifecycle of building a robust anti-phishing security awareness program. It covers everything from initial phishing simulations and designing role-specific training modules to continuous deployment, metric tracking, and optimization. The goal is to improve human security posture, ensure compliance, and build a strong defense culture against social engineering threats.

Provides comprehensive architectural guidelines and code patterns for developing high-end, immersive 3D web experiences. This skill covers advanced topics such as custom WebGL shaders, post-processing effects (Bloom, Depth of Field), realistic physics interactions, and crucial performance optimization techniques, ensuring a premium, polished user experience across devices.

A comprehensive skill for managing AWS operational excellence, cost governance, and observability. It provides guidance on pre-deployment cost estimation (TCO), detailed spending analysis using Cost Explorer, setup of CloudWatch alarms, auditing activity via CloudTrail, and overall security posture assessment. Essential for optimizing cloud spending and ensuring reliable infrastructure.

This guide details advanced strategies for publishing technical content across major developer platforms like Dev.to and Hashnode. It covers cross-posting best practices, canonical URL management for optimal SEO, and platform-specific optimization (e.g., tagging, content performance) to maximize reach and build authority.

Use @expo/ui to build high-fidelity, native user interfaces across iOS and Android from a single React codebase. This library renders real native UI components, such as SwiftUI on iOS and Jetpack Compose on Android. It promotes a universal component layer for cross-platform consistency, while also providing controlled access to platform-specific optimizations and drop-in replacements for existing community libraries.

Provides a comprehensive guide for authentically marketing and growing open source projects. Covers best practices for GitHub optimization (README, repository setup), community building, and implementing sustainable growth strategies, ensuring the project is discoverable and valuable.

A robust protocol designed for multi-agent collaboration on long-running projects. It simulates a self-optimizing city where diverse AI agents (citizens) interact using decentralized markdown files (The Register, Contracts, Chronicle). This framework enables cross-vendor optimization, dynamic work routing, and constitutional evolution without requiring a centralized server or specific runtime environment.

This skill manages Bing Webmaster Tools and IndexNow submissions to improve visibility on Bing, Amazon, and other search engines. It enables Microsoft Copilot citations by ensuring content is indexed in the Bing index. Useful for submitting URLs, comparing link data, and verifying IndexNow status for non-Google search surfaces.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.