skills/avalonia-viewmodels-zafiro/SKILL.md
Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.
npx skillsauth add legendaryabhi/agent-skills-hub avalonia-viewmodels-zafiroInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides a set of best practices and patterns for creating ViewModels, Wizards, and managing navigation in Avalonia applications, leveraging the power of ReactiveUI and the Zafiro toolkit.
ReactiveObject, WhenAnyValue, etc.) to handle state and logic.IEnhancedCommand for better command management, including progress reporting and name/text attributes.SlimWizard and WizardBuilder for a declarative and maintainable approach.[Section] attribute to register and discover UI sections automatically.DataTypeViewLocator and manage dependencies in the CompositionRoot.SlimWizard.For real-world implementations, refer to the Angor project:
CreateProjectFlowV2.cs: Excellent example of complex Wizard building.HomeViewModel.cs: Simple section ViewModel using functional-reactive commands.development
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
development
Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and lending systems. Use when building decentralized finance applications or smart contract protocols.
tools
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
testing
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.