Skill UI

This comprehensive guide serves as a technical reference for migrating applications from the Universal Windows Platform (UWP) framework to the modern WinUI 3 / Windows App SDK. It details crucial API changes, including namespace updates, window management (CoreWindow to AppWindow), threading models (CoreDispatcher to DispatcherQueue), and best practices for dialogs and background tasks. Essential for developers updating legacy UWP codebases.

Dispatch dedicated agents with isolated contexts to handle multiple independent failures or tasks in parallel. Use when you have several unrelated issues that can be investigated separately without shared state, while preserving your own context for coordination and review.

Dispatch a code review sub-agent to validate work quality before merging or completing tasks. This skill ensures issues are caught early, preventing bugs from spreading. It involves preparing git SHAs, using specific templates, and handling feedback from the review agent to maintain code quality and architectural integrity.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

Build clinical and healthcare deep learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train, and evaluate using a modular five-stage workflow.

TorchDrug is a modular PyTorch library for graph learning tasks including molecular property prediction, molecule generation, and knowledge graph reasoning. It provides datasets, models, and training engines for building workflows in computational chemistry and biology. Ensure compatibility with PyTorch 1.8-2.0 and Python 3.7-3.10 before implementation.

This skill rigorously verifies all claims of completion, whether for a task, a bug fix, or a feature rollout. It prevents false assurances by demanding fresh, multi-faceted evidence, including full test suite results, comprehensive coverage reports, integration status, and validation commands. Use it before declaring a component ready for GO or accepting a sub-agent's success report.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive diagnostic reference for resolving common technical failures encountered when running web scraping and automation tasks on the Apify platform. Covers issues such as Actor run failures, API rate limits (429), authentication errors (401), anti-bot blocks, and memory allocation issues, providing structured fixes and best practices for robust scraping pipelines.

This comprehensive workflow manages the full property lifecycle using the AppFolio API. It handles authentication, creating and updating property records and units, onboarding new tenants, generating lease agreements, and querying real-time occupancy/vacancy data. Ideal for automating core real estate management tasks in a SaaS environment.

This comprehensive guide addresses common and complex errors encountered when automating Apple Notes on macOS using JXA or osascript. It covers three major areas: TCC permission denials, AppleEvent communication failures, and iCloud synchronization issues. The document provides a detailed error reference table, root cause analysis, tested fixes, and diagnostic scripts to help developers successfully automate Notes tasks.

This comprehensive guide details the process of managing BambooHR API changes, addressing breaking changes, and migrating from legacy patterns. It covers critical tasks such as converting XML to JSON, updating deprecated field usages (e.g., 'bestEmail' to 'workEmail'), and implementing robust testing and rollback strategies for seamless integration.