Skill UI

This guide outlines modern best practices for building clean, maintainable, and scalable user interfaces using Avalonia. It emphasizes leveraging semantic containers (like HeaderedContainer, EdgePanel), defining shared styles, and creating generic components to minimize XAML redundancy and deep nesting. It guides developers to use Behaviors over code-behind, ensuring adherence to clean architectural principles.

Guides developers through configuring Cursor workspaces that span multiple repositories or packages, covering strategy selection, .cursorrules inheritance, selective indexing, and cross-project context handling to keep monorepo-style workflows productive.

Guides developers through mastering Cursor IDE tab completion and AI code suggestions, explaining how to trigger, accept, and configure ghost text workflows for consistent code style and context-aware results.

Guides developers through Exa neural-search anti-patterns, covering query style, search types, content retrieval, date filters, and autoprompt cost so reviewers or auditors avoid integration mistakes.

This skill enforces a mandatory, comprehensive quality loop for all code changes. It guides users through running multiple specialized validation tools—including linters, type checkers, and security scanners—for diverse ecosystems (e.g., Node.js/TypeScript, Python). It ensures that code is robust, adheres to established style guides, and maintains type safety across all components before it can be considered complete or committed.

Guides how to craft detailed prompts for Google Stitch so it generates high-quality web and mobile UI designs, covering specificity, visual style, multi-screen structure, platform targeting, and iterative refinement.

Guides research teams through a two-stage manuscript workflow: outline sections with research-lookup, enforce IMRAD structure, citation styles, figures, and reporting guidelines, then convert into polished prose for journals.

Guides product teams through Geoffrey Moore-style positioning statements to clarify target customer, underserved need, category, benefit, and differentiation before messaging or launch planning.

Guides teams through writing Mike Cohn‑style user stories with Gherkin acceptance criteria so product needs become actionable, testable development work with shared understanding.

Guides explanations through Socratic-style dialogue, asking reflective questions, using small thought experiments, and a single analogy so learners arrive at clarity rather than receiving a lecture.

Guides ML teams through reviewer-style ablation planning, covering context gathering, structured experiment design, feasibility checks, and compute-aware sequencing before implementation.

Guides researchers through organizing assumptions, invariant objects, notation, and derivation maps to turn loose goals, messy notes, or partial formula chains into a coherent package suitable for paper-style theory drafts or blocker reports.