vs-design-diverge

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name: vs-design-diverge description: Create non-generic, high-entropy frontend interfaces using Verbalized Sampling (VS) to break mode collapse and maximize design creativity.

This skill guides the creation of distinctive, production-grade frontend interfaces by explicitly mitigating "Mode Collapse" (the tendency to produce generic AI-slop). It uses Verbalized Sampling logic to unlock LLM creativity and deliver high-entropy, unforgettable designs.

Phase 0: Context Discovery (MANDATORY)

BEFORE any design analysis, you MUST gather deep context from the user. Use the AskUserQuestion tool to probe the following dimensions:

Discovery Dimensions

1. Emotional Tone: What feeling or atmosphere should the design evoke? (e.g., "trustworthy", "edgy", "playful", "luxurious")
2. Target Audience: Who will see this? What are their expectations and technical sophistication?
3. Reference Points / Anti-References: What does the user want to emulate? What do they explicitly want to avoid?
4. Business Context: What problem does this UI solve? What's the usage scenario?

Context Signals

• Analyze existing code if present—extract style patterns, color schemes, component conventions
• Parse user prompt for implicit context keywords (landing page, dashboard, portfolio, SaaS, etc.)
• Ask follow-up questions even if the initial prompt seems simple—surface the full vision in the user's mind

CRITICAL: Do not proceed to Phase 1 until you have sufficient context. A vague prompt requires MORE questions, not assumptions.

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Phase 1: Identify the Mode (The Generic Baseline)

• Verbalize the most predictable, high-probability (P ≈ 0.95) design for this request.
• Identify "AI-slop" markers based on the context. Common examples include:
  • Inter/Roboto/System fonts
  • Rounded blue/purple buttons
  • Standard F-pattern layouts
  • White backgrounds with gradient accents
  • Generic hero sections with stock-style imagery
• CRITICAL: You are forbidden from choosing this baseline.

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Phase 2: Sample the Long-Tail (Probability Mapping)

Generate three distinct aesthetic directions and assign a "Typicality Score" (T-Score) from 0 to 1.0 (where 1.0 is most generic):

• Direction A (T ≈ 0.7): Modern/Clean but safe.
• Direction B (T ≈ 0.4): Distinctive/Characterful (Specific niche style).
• Direction C (T < 0.2): Experimental/Bold (High-entropy, unexpected).

T-Score Justification Required: For each direction, explicitly state WHY it has that T-Score. What makes it more or less typical? Reference specific design choices.

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Phase 3: Commit to Low-Typicality

Select the direction with the lowest T-Score that still meets:

1. Functional requirements from Phase 0
2. All Quality Guardrails (see below)

Commit to this BOLD aesthetic with intentionality. The choice must be DELIBERATE, not accidental.

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Quality Guardrails (NON-NEGOTIABLE)

These principles MUST be satisfied regardless of how experimental the design becomes. If a Low-T direction violates any of these, increase T until compliance is achieved.

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Anti-Patterns (EXPLICIT FAILURES)

If your design exhibits these patterns, you have FAILED the skill's intent:

1. Accidental Design

• Making things "different" without intentionality
• Randomness masquerading as creativity
• Unable to articulate WHY a choice was made
• Test: If asked "why this color/font/layout?", you must have a coherent answer

2. Frankenstein Style

• Mixing incompatible aesthetics without synthesis
• No unifying design language
• Elements that feel like they belong to different projects
• Test: Could you describe this design's "personality" in one sentence?

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Frontend Aesthetics Guidelines (VS-Enhanced)

Apply the "Inversion Principle": If a choice feels "obvious," it has too much probability mass. Consider the lower-probability, higher-impact alternative—but only if it serves the context.

Typography (Context-Aware Selection)

• AI-slop examples: Inter, Roboto, Arial, System fonts, Space Grotesk in default usage
• Low-T approach: High-character display faces paired with refined, unexpected body typefaces. Variable fonts, unusual weights.
• Remember: The "right" font depends on context. A brutalist portfolio may use industrial sans-serifs; a luxury brand may use refined serifs.

Color & Theme (Chromatic Divergence)

• Avoid even distributions. Commit to a cohesive but "dissonant-yet-beautiful" palette.
• Use CSS variables for systematic theming.
• Prefer sophisticated textures, noise, and lighting over flat fills—when appropriate to context.

Spatial Composition (Anti-Grid Logic)

• If a standard grid is P=0.9, consider P=0.1 layouts: Asymmetry, overlapping elements, diagonal flows, or editorial-style whitespace.
• Caveat: Data-heavy interfaces (dashboards, tables) may require conventional grids for usability.

Motion (High-Impact Entropy)

• Focus on "Micro-delights": staggered reveals, scroll-bound transformations, custom eased transitions.
• Avoid motion for motion's sake—every animation should have purpose.

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Strategic Frameworks (Context-Dependent)

When the user's goal involves persuasion or conversion (landing pages, marketing sites, product showcases), apply the AIDA Framework to structure the visual narrative:

AIDA Framework

When to Apply AIDA:

• Landing pages and marketing sites
• Product launch pages
• Portfolio pieces meant to attract clients
• Any UI where the primary goal is persuasion/conversion

When NOT to Apply AIDA:

• Dashboards and data-heavy interfaces (prioritize usability)
• Documentation or content-focused sites (prioritize readability)
• Internal tools (prioritize efficiency)

VS + AIDA Integration: Apply Low-T aesthetics to EACH stage of AIDA. A generic hero section fails at Attention; a predictable CTA fails at Action. The AIDA structure provides the "what," VS provides the "how."

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Implementation Standards

• Production-Grade: Code must be functional, accessible (A11y), and performant.
• Complexity-Typicality Balance: As the design becomes more "Low-Probability," implementation complexity must increase proportionally to maintain quality.
• No Refusal on Complexity: Claude is capable of extraordinary creative work. Do not simplify the vision for brevity.

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Execution Process Summary

Copy[Phase 0] Context Discovery
    ↓ (AskUserQuestion tool - gather all dimensions)
[Phase 1] Identify the Mode
    ↓ (verbalize the generic baseline)
[Phase 2] Sample the Long-Tail
    ↓ (three directions with justified T-Scores)
[Phase 3] Commit to Low-Typicality
    ↓ (select lowest T that passes Guardrails)
[Phase 4] Execute Code
    ↓ (implement with meticulous attention)
[Phase 5] Surprise Check
    ↓ (does this look like typical AI output? if yes, refactor)

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Final Validation

Before delivering, ask yourself:

1. Intentionality: Can I justify every major design decision?
2. Consistency: Does the design follow its own internal logic?
3. Guardrails: Are hierarchy, legibility, consistency, and clarity preserved?
4. Surprise: Would this stand out in a lineup of AI-generated designs?

REMEMBER: The goal is to maximize "Surprise Score" while maintaining "Production Quality." Break the mold—deliberately.