Taste Skill: Anti-Slop Frontend Framework for AI Agents

Taste Skill: The Anti-Slop Frontend Framework for AI Agents

Portable Agent Skills that upgrade AI-built interfaces: stronger layout, typography, motion, and spacing instead of boilerplate-looking UIs. This repository also includes image-generation skills for reference boards (web, mobile, brand kits). Pair them with ChatGPT Images or similar generators, then hand the frames to Codex, Cursor, or Claude Code for implementation.

](https://github.com/Leonxlnx/taste-skill/stargazers) ](#installing) ](https://www.tasteskill.dev/changelog)

Disclaimer Taste Skill has no official token, coin, or crypto project. Any token using my name, image, or project is unaffiliated and not endorsed by me.

Table of contents

Installation
Skills overview
Image-generation skills
Which skill to use when
Image-first workflow tips
Settings (taste-skill only)
Examples created with taste-skill
Support the project
Research and background
Common questions
License

Installation

The installation journey is designed to be simple and portable. The core mechanism relies on a single command that scans the skills directory and pulls in both code-based skills and image-generation skills in a unified way.

Global installation workflow:
Run: npx skills add https://github.com/Leonxlnx/taste-skill
This command searches the skills/ folder within the repository, ensuring that every skill—whether it emits code or outputs image references—follows the same installation path.
Installing a single skill by name (illustrative example):
Run: npx skills add https://github.com/Leonxlnx/taste-skill --skill "imagegen-frontend-mobile"
You can also copy any SKILL.md into your project or paste the content into a ChatGPT or Codex conversation to guide the agent with the same instruction set.

The Taste Skill ecosystem is designed to be installation-agnostic. The CLI discovers and wires up every skill present in the repository’s skills/ directory, so you don’t need to manage multiple installation commands for different skill types.

Skills at a glance

Taste Skill provides two broad families of capabilities: Implementation skills and Image-generation skills. Each skill is crafted to do one job well, so you can mix and match without overconstraining your AI’s behavior.

Implementation skills (output code):
taste-skill: The default all-rounder for premium frontend output. It avoids locking you into a single visual style and emphasizes quality, consistency, and polish across layouts, typography, motion, and spacing.
gpt-taste: A stricter variant tailored for GPT/Codex. This mode enforces higher layout variance, stronger GSAP-driven motion direction, and aggressive anti-slop rules to prevent repetitive UI stumbles.
image-to-code-skill: An image-first pipeline that generates site references, analyzes them, and then implements the frontend to match. It creates a concrete bridge from visual references to living code.
redesign-skill: For existing projects that need a UI audit. It focuses on auditing the UI, fixing layout, spacing, hierarchy, and styling to align with intended design goals.
soft-skill: A polished, calm, expensive-feeling UI with softer contrast, generous whitespace, premium fonts, and spring motion.
output-skill: When the model ships half-finished work, this skill produces a complete, usable output with no placeholders or missing logic.
minimalist-skill: Editorial product UI with a Notion or Linear vibe, restrained palette, and crisp structure.
brutalist-skill: A bold, high-contrast, experimental direction. This is a beta that uses Swiss-type aesthetics and sharp layouts to push visual language.
stitch-skill: Google Stitch-compatible rules, including an optional DESIGN.md export format for easier handoff and collaboration.
Image-generation skills (design images only, no code):
imagegen-frontend-web: Produces website comps such as hero sections, landing pages, and multi-section layouts with strong typography and anti-slop art direction.
imagegen-frontend-mobile: Mobile screens and flows for iOS/Android/cross-platform experiences, with mockups that maintain readability and cohesive sets.
brandkit: Brand-kit boards that cover logo directions, color palettes, typography, and identity applications across product categories.

Which skill should you start with?

Start with taste-skill for a balanced, safe default that works across most frontends.
Choose gpt-taste when you need stricter GPT/Codex-oriented rules, with more rigid motion and layout enforcement.
Use image-to-code-skill for a full image → analyze → code workflow, especially when you begin from visuals.
Apply redesign-skill to improve an existing codebase rather than creating a new one from scratch.
If you already know your visual direction, layer in soft-skill, minimalist-skill, or brutalist-skill to align with your chosen aesthetic.
Add output-skill if your agent tends to truncate results; it ensures complete, finished outputs.
Bring in imagegen-frontend-web, imagegen-frontend-mobile, or brandkit when your deliverable is images (comps, flows, identity boards), then pass the renders to your coding agent for implementation.

Image-first workflow tip

For image-to-code-skill, state the pipeline clearly in the prompt to ensure the agent follows a disciplined sequence: generate images, analyze them, then code. This “image-first” discipline helps align visuals with implementation, reducing drift between concept and product.

ChatGPT Images and Codex workflow

When using image-first capabilities, you can attach or paste the following image sets and request frames:

imagegen-frontend-web
imagegen-frontend-mobile
brandkit

Ask for the frames you need, then feed the renders to Codex, Cursor, or Claude Code. The idea is to chain the image generation step with a subsequent code synthesis step so that a single, coherent workflow produces both references and a working frontend.

Settings (taste-skill only)

Taste Skill exposes numerical dials at the top of the file (range 1–10) to tune behavior. These knobs are designed to be intuitive levers that affect how aggressively the agent experiments with layout, motion, and density.

DESIGN_VARIANCE: Controls layout experimentation. Lower values favor centered, clean, predictable layouts; higher values encourage asymmetric and modern compositions.
MOTION_INTENSITY: Controls the depth and prominence of animation. Lower values emphasize hover interactions; higher values engage scroll, parallax, and magnetic effects.
VISUAL_DENSITY: Controls information density per viewport. Lower values yield spacious designs; higher values pack more information into dashboards and dense UIs.

Examples created with taste-skill

Taste Skill excels at turning design intent into tangible visuals. Here are two representative creations that showcase its capabilities:

These examples illustrate how the same skill set can generate complementary top and bottom sections, providing a cohesive yet dynamic visual language for frontend interfaces.

Support the project

Taste Skill thrives on community support. If the framework helps you accelerate your design-to-code workflow, consider sponsorship as a way to keep development moving forward.

Sponsor on GitHub: Sponsor Your Favorite Maintainer on GitHub
Current Sponsors: A showcase of supporters who have contributed early and often
robinebers
JKc66
u2393696078-rgb
a-human-created-this
AtharvaJaiswal005
ghughes7
mccun934
navanchauhan

Star History Rank

Research background

The research behind Taste Skill’s design principles, anti-slop rules, and skill architecture lives in the research/ directory. This work captures the thinking that informed the creation of the various variants (taste-skill, gpt-taste, image-to-code-skill, redesign-skill, and more).

Common questions

How is this different from other AI design skills?
Taste Skill provides multiple specialized variants with adjustable dials, all designed around anti-repetition and improved design consistency. The framework is intentionally framework-agnostic, focusing on design intent rather than tying to a single API.
Does it work with React, Vue, Svelte?
Yes. The rules are about design intent, not a specific framework’s API. The skills aim to produce correct layout, spacing, typography, and motion that can be wired into any frontend stack.
What is SKILL.md?
SKILL.md is a portable instruction file that agents can load automatically. It can be installed via npx skills add or copied into a repo or pasted into a conversation with an AI agent.
Do image-generation skills install with npx skills add?
Yes. They live under skills/ alongside the code skills, so the same CLI discovers them.
How do I get the best results from image-to-code-skill?
State the pipeline clearly: generate images, then analyze, then code. This helps ensure the generated references align with the implementation.

License

Closing thoughts

Taste Skill represents a deliberate approach to empowering AI agents with portable, upgradeable frontends. It is designed to liberate the UI from boilerplate, delivering premium-feeling interfaces through a family of targeted skills. Whether you need stronger anti-slop behavior, a softer or more minimal aesthetic, or a robust image-to-code workflow, Taste Skill provides a structured, extensible path to fast, polished frontend outcomes. The combination of implementation skills and image-generation skills enables a holistic design-to-implementation pipeline that keeps visuals and code aligned, while preserving flexibility to adapt to React, Vue, Svelte, or any modern frontend framework. By balancing design variance, motion depth, and visual density, Taste Skill helps teams iterate confidently without sacrificing quality or consistency.

Images

Note: All visuals referenced above are drawn from the provided input assets and referenced links. They illustrate the kinds of design language Taste Skill aims to promote—polished, scalable, and adaptable frontends that can be authored, reviewed, and implemented with AI assistance.

Taste Skill: Anti-Slop Frontend Framework for AI Agents

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GitHub - Leonxlnx/taste-skill: Taste Skill: Anti-Slop Frontend Framework for AI Agents

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