Taste Skill: design rules that raise the quality of AI-generated interfaces

Taste Skill is an open-source library of agent skill files whose goal is to raise the visual quality of interfaces generated by AI. It does not depend on any particular model. Instead, it turns design judgement into structured rules and injects them into the workflow of AI coding tools, so that the output escapes the familiar "cheap template" feeling in layout, typography, motion and spacing.

What problem it solves

Writing front-end code with AI is already fast, but fast is not the same as good-looking. By default, models gravitate towards the same combination of centred cards, evenly split grids, large corner radii and gradients; the more sites you build, the stronger the sameness becomes. Taste Skill's approach is to add a layer of "design constraints" on top of prompting: parameters that a designer would normally settle through experience — tonal range, type scale, whitespace rhythm, motion intensity — become rule files that a model can read and execute. It behaves more like a design gatekeeper than a code linter.

Main capabilities

• Multi-style skill library: besides the main skill it ships directions such as gpt-taste, soft-skill, minimalist-skill and brutalist-skill, corresponding to refined and gentle, minimal editorial, and hard-edged brutal tones, so you can pick according to the character of the project.
• Image generation skills: imagegen-frontend-web produces web design mockups, imagegen-frontend-mobile produces mobile interfaces, and brandkit produces brand identity boards; they can work together with ChatGPT Images or the image mode of Codex.
• Image-to-code workflow: image-to-code-skill first produces a design reference image, then performs a structural analysis of that image, and finally lands it as front-end code, reducing the drift between "design" and "implementation".
• Reworking existing projects: redesign-skill takes an audit-first approach, diagnosing the problems of the current interface before proposing a rework plan, which suits inherited legacy projects.
• Output completeness safeguards: output-skill suppresses half-finished work, placeholders and truncated code blocks, preventing the AI from stopping halfway.
• Three adjustable parameters: DESIGN_VARIANCE, MOTION_INTENSITY and VISUAL_DENSITY control the amplitude of design variation, the strength of motion and visual density respectively; change them and the whole style shifts.

How to use it

1. Install the skill library: run npx skills add https://github.com/Leonxlnx/taste-skill in a terminal; a single install can be reused across several supported tools.
2. Choose a skill: use design-taste-frontend for general projects, image-to-code when you want images before code, and high-end-visual-design when you are after a premium feel.
3. Hand it to the AI: describe the interface requirements as usual in Cursor, Claude Code, Codex, Gemini CLI, v0 or Lovable; the AI generates code according to the rules inside the skill, with no need to write elaborate prompts.
4. Tune the parameters: open SKILL.md, change the three values at the top, and regenerate to obtain a different visual direction.

Where it fits

• Brand sites and studio websites: florists, design studios, luxury goods and other sectors with high expectations of visual tone can reuse the Anti-Slop rules directly.
• Early prototype validation: use the image skills to produce high-fidelity mockups quickly, confirm the direction, then let a coding tool reproduce it precisely.
• Visual upgrades for older projects: apply redesign-skill for a systematic UI audit and modernisation of legacy systems.
• Multi-screen mobile design: rely on imagegen-frontend-mobile to generate consistent iOS and Android interface concepts.
• Design system groundwork: use the brand kit and brand boards to accumulate specifications that can actually be implemented.

Limits and things to watch

Taste Skill provides aesthetic constraints, not an automated design service. It cannot decide product positioning for you, nor does it judge content priority; if the brief itself is vague, the rules can only make the interface more refined, not more correct. The real effect of the three parameters depends on how well the underlying model understands them, and behaviour differs between tools. In addition, the style library leans towards presentational pages such as marketing pages and portfolios; complex back-office systems still need separate usability and information architecture conventions.

FAQ

Is Taste Skill free? It is an open-source skill file library distributed through a GitHub repository, and you can install and modify it yourself. The exact licence terms follow the description in the repository.

Do I have to use Cursor? No. It is also compatible with Claude Code, Codex, Gemini CLI, v0, Lovable, OpenCode and similar tools, and the skill files are interchangeable between them.

Can I use it without knowing design? Yes. The default parameters already improve the output noticeably; if you want finer control you only adjust three parameters, rather than writing a design specification from scratch.

How is it different from Cursor Rules? Cursor Rules constrain code conventions and engineering agreements and barely touch the visual layer; Taste Skill acts directly on typography, motion, spacing and design-system choices. The two solve problems at different levels.