Design & UX

Создать новый polished visual artifact или hi-fi UI как self-contained HTML. Use для экранов, лендингов, dashboards, флаеров и визуальных концептов.

Создать timeline/motion artifact и при необходимости экспортировать его в MP4. Use для explainer, walkthrough и animated visual.

Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.

Create handcrafted static SVG graphics and performant SVG animations — icons, illustrations, loaders, path-draw effects, morphing shapes, and motion paths. Load when the user asks to create SVG, draw an SVG icon, animate an SVG, make an SVG loader, path animation, shape morph, animated logo, line drawing effect, or improve trashy AI-generated SVG. Also triggers on "SVG animation", "SMIL", "stroke-dashoffset", "vector illustration", or "self-contained animated SVG".

Orchestrator + builder for distinctive, production-grade frontends that don't look AI-generated. Derives stack and design context from product-soul/PRD/specs, then runs the anti-slop chain — explore distinct directions, lock a DESIGN.md system, build from golden examples with mandatory polish + every interactive/empty/loading/error state, then review. Load when the user asks to build a UI, design a frontend, build a landing page or dashboard or web app, beautify or redesign a page, make a UI ...

Turn a chosen design direction into a canonical DESIGN.md plus production tokens that don't look vibecoded — state-level colors (8-step neutral ramp, rest/hover/active/ disabled, focus ring), APCA-checked contrast, typography, spacing/radius/motion/elevation, an icon strategy, and component contracts for the core atoms. Emits tokens in the project's stack format (shadcn HSL vars or Tailwind v4 @theme). Load when the user asks to build a design system, generate design tokens, create a DESIGN.m...

Review a built frontend against its chosen direction, catch drift back to generic AI defaults, enforce state coverage, ethical patterns, UX heuristics, and polish, and check contrast with APCA (not the legacy WCAG ratio). Produces specific, prioritized fixes — never vibes-based feedback. Works with pasted screenshots or Playwright MCP automated capture. Load when the user asks to review a UI, audit a design, check if a frontend looks generic or vibecoded, evaluate visual quality or polish, sa...

Set a deliberate visual direction before any UI is built — the single biggest lever against generic AI output. Derives a posture from product-soul/PRD/specs, scores a curated archetype palette, then generates 2-3 GENUINELY DISTINCT directions and compares them side-by-side before committing to one. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, explore visual options, says "what should this look like", "make it feel like [Linear/App...

Generate archetype-driven semantic design tokens (colors, typography, spacing, radius, motion, elevation) that don't look vibecoded. Hard-bans Tailwind-default palettes, Inter-only typography, and purple→pink gradients unless the chosen archetype explicitly demands them. Load when the user asks to generate design tokens, create a design system, set up CSS custom properties, build a token scale, design a color system, set up typography scale, or when frontend-design routes here during token ge...

Pick a product archetype from a curated catalog before any UI gets built — the single biggest lever for not looking vibecoded. Routes the request to one of B2B-productivity, enterprise-trust, premium-consumer, playful-consumer, editorial, brutalist-distinctive, dev-tool, or marketing-landing, and returns a complete design philosophy: typography pair, color logic, motion curve, density, icon stance, reference sites, and a "feels like X" claim. Load when the user asks to pick an aesthetic, choo...

Imported skill validate from vercel

Imported skill skill from vercel

Imported skill rendering_animate_svg_wrapper from vercel

Imported skill bundle_conditional from vercel

Imported skill modern_minimalist from anthropic

Imported skill midnight_galaxy from anthropic

Imported skill gif_builder from anthropic

Imported skill frame_composer from anthropic

Imported skill botanical_garden from anthropic

Imported skill arctic_frost from anthropic

VQA methodology for dynamic portfolio optimization — sampling strategies (adaptive CVaR scheduling), optimizer scheduling (PSO+NFT hybrid), and hardware-aware ansatz design (data-guided colored layout, heavy-hex deep-chain layout). Activation: vqa portfolio, dynamic portfolio optimization, CVaR scheduling, hardware-aware ansatz, heavy-hex layout, PSO optimizer, quantum portfolio, 动态投资组合优化

Infinite-dimensional quantum controllability framework using von Neumann algebra techniques. Extends the finite-dimensional Lie algebra rank condition to bilinear quantum systems on infinite-dimensional Hilbert spaces by interpreting algebraic objects through affiliated operators. Use when: analyzing controllability of infinite-dimensional quantum systems, designing quantum control protocols for continuous-variable systems, generalizing Lie algebra rank conditions beyond finite dimensions, or...

VQA methodology for dynamic portfolio optimization — sampling strategies (adaptive CVaR scheduling), optimizer scheduling (PSO+NFT hybrid), and hardware-aware ansatz design (data-guided colored layout, heavy-hex deep-chain layout). Activation: vqa portfolio, dynamic portfolio optimization, CVaR scheduling, hardware-aware ansatz, heavy-hex layout, PSO optimizer, quantum portfolio, 动态投资组合优化

**arXiv ID:** 1612.06370 **Authors:** Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan **Published:** 2016-12-19T20:56:04Z **Abstract:** This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which ...