What problem does it solve? LLM-generated frontend code tends to produce repetitive layouts, narrow headings that wrap into 6-line walls, gappy bento grids, invisible button text, and static pages without motion. This Skill enforces a rigorous design engineering process that eliminates these default failure patterns. ## Core Features & Use Cases - Deterministic Layout Randomization: Simulates a Python RNG seeded by prompt character count to select hero architectures, typography stacks, components, and GSAP paradigms, preventing repeated layouts. - Strict Structural Rules: Enforces AIDA page structure, 2-3 line hero headings via wide containers, gapless bento grids with grid-flow-dense, and massive section spacing. - Advanced GSAP Motion: Implements scroll pinning, scrubbing text reveals, image scale/fade on scroll, and card stacking with real GSAP ScrollTrigger code. - Use Case: Ask for a landing page for a SaaS product and receive a complete React/Tailwind page with a cinematic hero, dense bento feature grid, pinned scroll sections, and a pre-flight design plan verifying all constraints. ## Quick Start Ask the AI to build a landing page for your product using the gpt-taste skill and it will output a verified design plan followed by the full animated React code.
