What problem does it solve? LLM-generated frontend code tends to repeat the same layouts, produce narrow multi-line hero headings, leave empty gaps in bento grids, and use cheap meta-labels. This Skill enforces a rigorous design engineering process that breaks those defaults and produces varied, motion-rich, editorial-quality UI code. ## Core Features & Use Cases - Deterministic Layout Randomization: Simulates a Python RNG step to select hero architectures, typography stacks, components, and GSAP animation paradigms so no two pages look alike. - Strict Layout Rules: Enforces wide hero containers with a 2-3 line H1 limit, gapless bento grids via grid-flow-dense, massive section spacing, and high-contrast buttons. - Advanced GSAP Motion: Requires real ScrollTrigger implementations including pinning, scrubbing text reveals, card stacking, and image scale/fade effects. - 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 every layout rule. ## 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 UI code.
