Agent skill · design visual · sickn33
design-spatial
Design — spatial composition
Why this skill is useful
Provides domain-specific knowledge on spatial composition and design critique processes that the AI wouldn't reliably generate on its own.
What it needs
About 11k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Design — spatial composition When to Use Use this skill when you need 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/design-spatial