name: design-spatial
description: Spatial composition and layout for frontend design. Load with design when composing pages/dashboards/components, or whenever generated UI looks off — overlapping/colliding elements, weak hierarchy, or generic ("looks like every startup site"). The core idea: a model can't trust its own layout, so render it, critique it with fresh eyes, and deviate from your first instinct.
author: Conner K Ward
Design — spatial composition
A model cannot trust its own UI output. Everything else follows from two failures.
1. It can't see what it made
UI is generated as a token stream, never as pixels — so the model cannot perceive collisions, overlap, imbalance, or broken spacing. It will write a headline that runs into the hero image and have no idea.
Render it and judge the image, not the code. Serve with any static server (e.g. python3 -m http.server or npx serve) and screenshot headless via Playwright. Screenshot at a few widths.
Critique with fresh eyes — not your own. Grading your own output rationalizes it; the builder looks at its overlapping headline and calls it fine (this is exactly how a real collision shipped in testing). Use a separate judge — a subagent that did not write the page — and tell it to hunt for what's wrong: collisions, edge tangents, ragged alignment, lopsided weight, no clear focal point, breaks at some width. Fix, re-render, re-judge.
2. Its first idea is the average
Whatever it produces first is the mean of its training data — and there is more than one mean:
- the generic-AI mean: Inter, purple-on-white gradients, centered single column, three equal cards;
- the designer-trend mean: oversized condensed caps, dark-mode + grain, monospace "vibes" microtext, sticker badges.
Landing on the second isn't taste — it's a more flattering average, which is why it slips past. Treat your first instinct as the mean and deviate deliberately — toward this product's specific world (use design-thinking's domain / color-world / signature as the direction), not toward another trend. If the result could be any startup, you shipped the mean.
3. So don't prescribe a style
Any fixed rule — a 12-col grid, an 8-point scale, "mono = data" — becomes next cycle's mean, and a blind model executes it into collisions anyway. Prescribe the process, not the look: see it with fresh eyes, and push off the average toward the domain. Taste supplies the direction (design-thinking / design-philosophy); this skill only insists you look and don't ship the mean.
For iterative spatial tuning, a local page with live controls (sliders, pickers, drag handles) beats one-shot critique.
