taste-skill-showcase-1: The repo that teaches AI agents taste

A Next.js showcase where <code>.agent/skills/taste-skill/SKILL.md</code> acts like a design system for the coder, not just the interface.

8 min read • View on GitHub • More from Leonxlnx

A wide drafting table shows two possible AI futures for frontend work. On one side the interface is noisy and generic, on the other a rulebook channels the build into a disciplined layout. It explains that the repo is really about controlling the builder, not just styling the page. — This hero frames the whole piece: the hidden product is a set of rules that shape what the agent is allowed to make.

Key Takeaways

The showcase looks like a normal Next.js site, but its real product is a skill file that governs the agent's design choices.
<code>.agent/skills/taste-skill/SKILL.md</code> turns taste into a versioned policy layer with explicit bans, defaults, and layout preferences.
The repo's real shift is from prompt luck to reusable constraints, which makes AI-made frontend more consistent and easier to govern.
The broader bet is that design systems will increasingly live where code is generated, not only where humans consume components.

At first glance, taste-skill-showcase-1 looks like a tidy Next.js demo. That is the decoy. The real product is a file aimed at the builder, not the visitor: .agent/skills/taste-skill/SKILL.md.

That matters because AI frontend work usually fails in the same dull ways. The spacing is fine, the copy is fine, and the result still feels generic. This repo attacks the part most teams leave to chance: the agent's taste defaults.

The hidden product lives in .agent/skills/taste-skill/SKILL.md

Leonxlnx frames taste-skill as a way to give AI good taste and stop it from generating boring, generic slop. This showcase is the proof case. It lets you see what happens when the skill is not a prompt suggestion but the thing shaping the build.

A close-up of handwritten rule sheets and crossed-out visual clichés. It explains how the skill file turns taste into operational constraints instead of vague style advice. — The point is not decoration. It is control, written down tightly enough to change what the agent makes.

Taste becomes rules

The rules are specific enough to matter. They push toward neutral palettes, stable viewport behavior, grid-first layouts, and deterministic typography. They also ban a handful of the most common AI shortcuts, which is why the output avoids the shiny sameness that makes so many generated pages blur together.

That is the most useful lesson in the repo. Taste is not a mysterious attribute that appears after enough retries. It is a contract that can be written down, versioned, and fed back into the agent.

Every shadow, every spring constant, every pixel offset - hand-tuned. No defaults.

Leonxlnx, Creator · tasteskill.dev

A skill file sits between model defaults and the rendered UI, intercepting the usual drift and turning taste into explicit constraints.

How the skill changes the output

Read the pipeline left to right. Base model habits enter first, then the skill file narrows the space of acceptable moves, then those constraints show up as concrete UI decisions. The surprise is not that the result looks cleaner. It is that the repo makes the cleaner output repeatable.

That is why the .agent folder matters. It moves design control one layer earlier, from what should this screen look like to what should the builder be allowed to do.

A split scene compares a generic AI dashboard with a more disciplined version of the same layout. It shows how the same content can feel generic or intentional depending on the rules that shape it. — The contrast is the whole argument. The rules do not add noise, they remove the habits that make AI UI look interchangeable.

What this looks like next to ordinary AI workflows

The comparison is not subtle. Prompt-only coding is fast, but it drifts. Ad hoc local rules are precise, but they are easy to forget. A skill file sits between those extremes: portable enough to reuse, explicit enough to audit, and opinionated enough to keep the output from sliding back to default AI UI.

Approach	Where taste lives	What it buys	What breaks
Prompt-only AI coding	Inside a one-off instruction	Fast to try	Easy to drift
Project-local skill files	In versioned agent-readable rules	Portable and enforceable	Only as good as the rules
Traditional design systems	In components, tokens, and docs	Strong consistency	Built for humans first, not agents

That is the real significance of taste-skill-showcase-1. It is not just a demo of a sharper landing page. It is a prototype for a new kind of frontend governance, where taste is encoded once and applied every time the agent writes.