Frontend Design Enhancement Command

Transform functional but generic frontend designs into distinctive, polished user experiences that avoid "AI slop" aesthetics. Based on official research from "Improving frontend design through Skills" by Anthropic.

Core Principles Applied

Distributional Convergence: Breaks away from statistically common "safe defaults" (Inter fonts, purple gradients, minimal animations) that dominate training data distributions.

Altitude-Appropriate Guidance: Balances specificity and flexibility - provides contextual principles with concrete examples without prescribing exact values.

High-Impact Moments: One well-orchestrated page load with staggered reveals > dozen random micro-animations.

Workflow

1. Design Audit (frontend-design-enhancer agent):

   • Identify current fonts, colors, backgrounds, animations
   • Calculate AI Slop Score (0-100, lower is better)
   • Detect distributional defaults:
     • Generic fonts (Inter/Roboto/Open Sans/Lato)
     • Purple-on-white gradients (#a855f7 → #ffffff)
     • Plain backgrounds with no depth
     • Missing or random animations

2. Typography Enhancement (frontend-aesthetics skill):

   • High-Contrast Pairings: Display + monospace, serif + geometric sans
   • Extreme Weight Variations: 100-200 (ultra-thin) OR 800-900 (extra-bold) for headings
   • Size Jumps: 3x+ ratio (hero 4rem → body 1rem), not incremental 1.5x
   • Implement fluid typography with clamp()
   • Select from distinctive font categories (code-aesthetic, editorial, technical, playful, elegant)

3. Color Scheme Design:

   • Create intentional palette with mood (professional, energetic, calm, bold)
   • Forbidden: Purple-on-white (#a855f7 → #ffffff)
   • Draw inspiration from IDE themes and cultural aesthetics
   • Ensure WCAG AA contrast compliance (4.5:1 minimum)
   • Use CSS variables for cohesive system

4. Background Treatment:

   • Layer CSS gradients for depth (mesh gradients, radial glows)
   • Add subtle textures (noise, geometric grids)
   • Implement ambient backgrounds (waves, patterns)
   • Avoid: Plain white (#FFFFFF) or solid colors

5. Animation Implementation (web-artifacts-builder skill):

   • Priority #1: Well-orchestrated page load (highest impact)
   • HTML Projects: CSS-only animations (better performance)
   • React Projects: Framer Motion for complex choreography
   • Implement staggered reveals with Motion variants
   • Add purposeful micro-interactions (hover, click feedback)
   • Always: Respect prefers-reduced-motion (accessibility first)

6. Validation (quality-controller agent):

   • Verify AI Slop Score improved to < 30 (distinctive design)
   • Check accessibility standards (WCAG AA)
   • Test responsive behavior across breakpoints
   • Validate animation performance (GPU-accelerated properties)
   • Store design pattern for future learning

Output

Terminal (Concise):

[OK] Design enhanced with Claude research principles

AI Slop Score: 85 -> 15 (Improved by 70 points - Distinctive)

Improvements Applied:
- Typography: Inter (generic) -> Playfair Display 700 + Source Sans 3 300 (high-contrast)
  * Extreme weights: 700 (headings) vs 300 (body)
  * Size jumps: 4rem hero -> 1rem body (4x ratio)
- Colors: Purple-on-white (#a855f7 → #fff) -> Ocean blue + amber (professional mood)
  * Avoided distributional default
- Background: Plain white -> Layered gradient + subtle noise (depth)
- Animations: None -> Orchestrated page load with staggered reveals (Framer Motion)
  * High-impact moment prioritized

Files Modified: 4 (tailwind.config.js, index.html, App.tsx, index.css)
Pattern Stored: design-enhancement-editorial-professional
Next Steps: Test responsive behavior, validate accessibility
Time: 12 minutes

File (Comprehensive) - Saved to .claude/reports/design-enhancement-[timestamp].md:

• Design Philosophy: Distributional convergence awareness, altitude-appropriate guidance
• Full Audit: Before/after comparison with AI Slop Score breakdown
• Typography:
  • Font pairing rationale (high-contrast, extreme weights)
  • Implementation code (Tailwind config, Google Fonts imports)
  • Size scale with fluid clamp() values
• Color System:
  • Complete palette with HSL values and CSS variables
  • Mood/brand alignment explanation
  • Contrast ratio validation (WCAG AA compliance)
• Background Patterns:
  • Layering techniques (gradients, noise, textures)
  • CSS implementation code
• Animation System:
  • Framer Motion setup and configuration
  • Page transition variants
  • Staggered list animation patterns
  • Micro-interaction examples
  • Reduced motion accessibility
• Accessibility Checklist: Keyboard navigation, screen readers, reduced motion
• Performance Metrics: GPU-accelerated properties used, bundle size impact
• Before/After Screenshots (if available)

Usage Examples

Basic Enhancement:

/design:enhance "Improve landing page aesthetics"
# Applies balanced design improvements across all aspects

Project-Specific:

/design:enhance "Make dashboard look professional with tech-ocean color scheme"
# Targets specific color mood

/design:enhance "Apply editorial design to blog with Playfair Display"
# Requests specific typography category

/design:enhance "React app needs distinctive design with Framer Motion"
# Specifies React + motion library

Problem-Focused:

/design:enhance "Fix generic AI appearance - looks like every tutorial"
# Addresses AI slop problem directly

/design:enhance "Too much purple gradient, needs unique identity"
# Tackles specific distributional default

Technical Implementation

Skills Loaded:

• autonomous-agent:frontend-aesthetics - Core design principles and patterns
• autonomous-agent:web-artifacts-builder - React + Tailwind + Framer Motion patterns
• autonomous-agent:gui-design-principles - Foundational UI/UX principles
• autonomous-agent:pattern-learning - Learn from successful design patterns

Agents Delegated:

• frontend-design-enhancer (Group 3: Hand) - Executes design implementation
• quality-controller (Group 4: Guardian) - Validates accessibility and standards

Auto-Fixes Applied:

• Generic font replacement (Inter → distinctive alternatives)
• Purple gradient elimination (#a855f7 detection)
• Background depth addition (plain → layered)
• Animation implementation (static → purposeful motion)
• Contrast ratio corrections (WCAG AA compliance)

Success Criteria

Quantitative:

• AI Slop Score < 30 (distinctive design)
• WCAG AA contrast: 4.5:1 minimum for text
• Typography size jumps: 3x+ ratio between levels
• Font weights: 100-200 or 800-900 for headings (extreme variation)

Qualitative:

• Distinctive fonts (NOT Inter/Roboto/Open Sans/Lato)
• Intentional color scheme (NOT purple-on-white #a855f7 → #ffffff)
• Layered backgrounds (NOT plain white #FFFFFF)
• High-impact animations (orchestrated page load, NOT random micro-animations)
• Altitude-appropriate implementation (contextual, NOT overly prescriptive)

Learning:

• Design pattern stored in .claude-patterns/
• Font pairing effectiveness tracked
• Color scheme mood success recorded
• Animation impact measured for future optimizations