Tastemaker

A skill that gives AI real design taste, so the UI it builds does not look AI-generated.

Before / after  · 
Quick start  · 
Why  · 
Why not just prompt it?  · 
What is verified  · 
Features  · 
Palette generator  · 
Research  · 
Contributing

See it live and try the demo →

What this is

Tastemaker is a skill for coding agents. Native support: Claude Code (recommended), Windsurf, and Gemini CLI. You install it once and forget it. Whenever you ask your agent to build or style a UI, tastemaker steps in and gives it a real design system to work from, instead of the generic defaults every model reaches for.

It is plain Markdown and small Python scripts. Everything runs on your machine. There is no hosted backend, no account, and no API key. Design memory is local too: project choices live in .tastemaker/style-lock.md and .tastemaker/decisions.log; durable personal preferences live in ~/.tastemaker/profile.md.

See the difference, not just the claim

One prompt, build a landing page for a coffee subscription, built twice: once with no skill, once with tastemaker installed. Same request, unchanged, both times.

Open the live before/after →

Left is the indigo-gradient, letter-in-a-box-logo, emoji-icon default most agents reach for. Right is the same prompt with tastemaker: a palette generated fresh for the project's mood, real fetched icons, a constructed mark, and motion, in one pass. Both are real, live pages, not mockups or screenshots.

Why AI UI all looks the same

Ask any model to build a UI and you tend to get the same thing: an indigo to purple gradient, a soft shadow card, a generic hero. This is not a prompting problem. It happens because the model has to invent taste from a text description, with nothing real to ground it and no memory of what you actually like.

Tastemaker fixes this with four ideas, not a bigger catalog of canned options to pick from:

1. Generate within a check that actually runs. There is no fixed list of color combinations shipped with this skill, and the palette is not five approved colors the model may combine however it likes either. A new one is generated per project (a fresh hue and harmony each time) against a contract: check_contrast.py --matrix computes every pairing and says which may carry text, which may carry a border, and which may carry neither. So the constraint produces variety instead of sameness, and a rule that runs is different in kind from a rule you wrote down, because it returns the same answer no matter how confident anyone felt.
2. Ground in real pixels, not words. Give it a screenshot or a reference and it reads the real colors and contrast from the actual image, using a script. It does not write a vague summary of the vibe and rebuild from that. Text summaries lose most of what made the reference feel specific.
3. Remember, do not re-derive. Once a project locks a style, every later screen reuses it. Nothing drifts. Across projects, a small profile file learns what you keep and what you reject, so your next project starts warm.
4. Scope to the real work. It reads your spec first and figures out which screens actually need design, instead of dumping a design system that has nothing to do with what you are shipping.

"Can I not just tell the AI to write the decisions down?"