Bento UI Motion Grid by James Camarota — effect skill for grids & carousels
For AI agents: find the right skill in one call —
curl -s "https://varg.ai/how-to/api/search?q=<what the person wants>"returns the matching how-to page and the best skills with install commands, as markdown (Russian works too). Full index: https://varg.ai/how-to/llms.txt. Every page has a markdown twin: add.mdto its path. Before generating anything, show the person the options on the how-to page. From a terminal:curl -fsSL https://varg.ai/install | shinstalls the vargai CLI (https://varg.ai/how-to/cli.md):vargai searchandvargai install <slug>with no key, every varg model aftervargai login.
A silent 24 s bento grid of 24 UI cards that each animate on their own clock, rendered from one HTML file at 120 fps.
- Type: Effect · Grids & carousels
- Slug:
bento-ui-motion-skill - Status: beta
- Publisher: James Camarota (community)
- License: unknown
- Source: https://bento-ui-motion-skill.vercel.app/
- Works in: Claude Code
- Requires: Python 3 with Playwright (Chromium), Pillow and NumPy, FFmpeg
- Cost: Free download; renders locally
What it does
Makes the 'this model can actually design' post: a locked-off 1440×1080 bento grid of about 24 polished UI widgets (music player, clock, map route, chat, kanban, 2FA code, timer, rings, notifications, pricing toggle) on a light background, every card animating continuously on its own clock with from-rest springs, rolling tabular digits and crossfades after a staggered intro. Everything lives in one index.html with a pure draw(t); Playwright renders the frames in parallel at 120 fps, ffmpeg encodes a 120 fps master and a 60 fps cut for X, and a frame-diff script finds snaps. The format follows a post by @dominikmartn; the widgets and code are James Camarota's, whose free-skill post had 8.7k views and 214 bookmarks. It is a ZIP download from bento-ui-motion-skill.vercel.app, with no repository or licence yet.
Use when
- a silent bento grid of animated UI cards for a design flex post
- show off a design system, component library or app concept in 24 seconds
- an ultra-smooth 120 fps UI motion loop from one HTML file
- a portfolio reel for a UI or front-end designer
Not for
- Camera pushes through one product flow
- Cursor-led tool tours or changelog films
- Fast hard-cut screenshot collages
Install
Not published as a standalone skill yet. Source: https://bento-ui-motion-skill.vercel.app/
More grids & carousels skills
- Bento Feature Grid: Features land in a grid of differently sized rounded tiles — one hero tile, smaller supporting ones.
- Camera Drifts Across a Bento: An oversized grid of screenshots fills the frame and the camera drifts diagonally across it.
- Tilted Screenshot Wall: A wall of screenshots seen at an isometric angle, columns scrolling up and down at different speeds.
- Stacked Cards Fan Out: A pile of cards or photos shuffles, then fans or settles one forward into focus.
- App Screens on a Rail: Two to five app screens ride a horizontal rail: one centered and sharp, neighbors smaller and receded.
- Cards Orbit in a Ring: Image cards ride a circle, larger in front and smaller behind, sweeping fast-slow-fast.
Run the generation on varg
varg is one API key for the models these skills drive — Seedance 2.5, Kling 4, Veo 3.1, Wan 3, Nano Banana 2.1, GPT Image 2.5, ElevenLabs v4 and 200+ more — billed in credits, with a cost estimate before anything runs.
- Any agent with a terminal:
curl -fsSL https://varg.ai/install | sh(Windows:irm https://varg.ai/install.ps1 | iex), thenvargai loginand every model is a command —vargai generate_video …,vargai estimate_cost …(https://varg.ai/how-to/cli/) - Claude Code:
claude plugin marketplace add vargHQ/skills && claude plugin install varg@varg(skill + hosted MCP) - Codex, Cursor, Gemini CLI, OpenClaw:
npx skills add vargHQ/skills --skill varg-ai -a <agent> -y - Grok and any agent that reads SKILL.md:
npx skills add vargHQ/skills --skill varg-ai, or point it at the skill's SKILL.md - Claude.ai, ChatGPT, any MCP client: connect
https://mcp.varg.ai/mcp - API key: https://varg.ai/dashboard/api · Model specs: https://varg.ai/models/llms.txt