Skill UI

A comprehensive guide detailing best practices for creating modern, clean, and highly maintainable user interfaces using Avalonia and Zafiro.Avalonia. It emphasizes leveraging semantic containers, defining shared styles, and utilizing generic components to dramatically reduce XAML redundancy and improve code structure. Learn to adopt patterns like Behaviors over Converters, and prevent deep nesting for optimal developer experience.

This guide details production-ready patterns for integrating the Customer.io SDK (customerio-node). It covers best practices including building type-safe clients, implementing robust retry logic with exponential backoff for network stability, performing efficient event batching for high-volume data streams, and managing singleton lifecycles. Use these patterns to build scalable, reliable, and maintainable user behavior tracking systems.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

Generates realistic users, products, orders, or schema-driven data via the test-data-generator plugin for populating test databases, simulating user behavior, and creating fixtures for automated or API testing.

This comprehensive guide provides solutions for common PostHog integration issues, helping developers diagnose and fix tracking failures. It covers critical topics including event capture problems, feature flag misbehavior, API authentication errors (401/429), and user identity fragmentation. Learn best practices for robust data analytics implementation.

A comprehensive guide for deploying PostHog analytics platform across diverse production environments. Covers advanced configurations for Next.js/Vercel (including reverse proxy for ad blocker bypass and edge functions for server-side capture), self-hosted setups using Docker, and deployment patterns for Google Cloud Run. Ensures robust, reliable user behavior tracking regardless of the hosting platform.

Execute end-to-end automated testing for mobile applications across both iOS and Android platforms. This skill simulates real user interactions, covering UI flows, gestures (swipe, pinch), form validation, and platform-specific behaviors (e.g., Safe Area handling, Android back button logic). It supports industry-standard frameworks like Appium, Detox, and Maestro, making it essential for ensuring cross-platform quality assurance and robust CI/CD pipelines.

This assistant streamlines the workflow for managing GitHub issues using the GitHub CLI. It guides users through structured processes to create, list, and view issues. When creating, it prompts for detailed information (including issue type, reproduction steps, and expected vs. actual behavior for bugs), ensuring high-quality issue reports. It also allows filtering and listing of existing issues.

This skill provides expert guidance on Makepad's cross-platform development lifecycle. It helps users understand platform-specific APIs, manage conditional compilation (e.g., macOS, Windows, WebGL2), and handle OS-specific behaviors across desktop, mobile, and web environments. Use it when targeting diverse deployment targets or integrating platform-level features.

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.

Analyzes behavioral reports from security sandboxes (Cuckoo, AnyRun) to detect advanced anti-analysis and evasion techniques. It specifically identifies indicators such as timing checks, hypervisor artifact queries, user interaction patterns, and environment fingerprinting. This tool is essential for threat hunting, enhancing detection rules, and conducting deep malware triage.