Taste-Skill: AI Frontend Aesthetic Upgrade
Give your AI good taste — stop generic, boring frontend code and produce premium, design-conscious UIs.
- Source repo
- Leonxlnx/taste-skill
- Stars
- ★ 99k
- Last updated
- today
- License
- MIT
- Primary language
- JavaScript
- FA score
- 31/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changesChatGPT · Codex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- When building a new website frontend with Claude Code or Codex, and you want the default output to be more design-aware and less 'AI-slop'.
- Not a fit if
- Teams doing backend or data work, not frontend design
- Projects that need v1's exact behavior and can't accept the experimental v2 default
- Source review
- 31/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
Taste-Skill is a collection of portable Agent Skills designed to improve the design quality of AI-generated frontend interfaces. It provides specialized SKILL.md files that guide coding agents like Claude Code, Codex, and Cursor with design principles, layout rules, and motion guidance to avoid boilerplate-looking UIs. The repository includes both code-implementation skills and image-generation skills, installable via the npx skills add CLI or by copying SKILL.md files into projects. Key skills include design-taste-frontend (v2 experimental), gpt-taste, image-to-code, redesign, soft, minimalist, brutalist, and three image generation skills (imagegen-frontend-web, imagegen-frontend-mobile, brandkit). All skills are MIT-licensed and come with supporting research documents and examples.
Taste-Skill provides a set of SKILL.md instruction files that coding agents can load automatically. After installation, the agent reads the project brief, infers the design language, and tunes three key parameters: DESIGN_VARIANCE (layout experimentation), MOTION_INTENSITY (animation depth), and VISUAL_DENSITY (information per viewport). Code-oriented skills (e.g., design-taste-frontend) output actual frontend code including layout, typography, spacing, and motion (e.g., canonical GSAP code skeletons). Image-generation skills (e.g., imagegen-frontend-web) produce design reference images only, without code. Users can install all skills via npx skills add https://github.com/Leonxlnx/taste-skill or select a single skill with the --skill flag. After installation, agents follow the skill rules to generate or refactor frontend interfaces.
- When building a new website frontend with Claude Code or Codex, and you want the default output to be more design-aware and less 'AI-slop'.
- When you have an existing codebase and want to redesign its UI; use redesign-skill to audit the interface first, then improve layout, spacing, and hierarchy.
- When you need design mockups (not code) for web or mobile; use imagegen-frontend-web or imagegen-frontend-mobile to generate references, then hand them to a coding agent.
- When you need a brand kit; use brandkit to generate logo directions, palettes, type, and identity applications.
- When using GPT/Codex and want stricter layout and motion rules; install gpt-taste for higher variance and aggressive anti-slop behavior.
How do you install or deploy this agent?
Install via npm's npx command: first ensure Node.js is installed, then run npx skills add https://github.com/Leonxlnx/taste-skill to install all skills, or specify a single skill with npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend". Alternatively, you can copy the SKILL.md files directly into your project or paste them into conversations.
How do you use this agent?
After installation, instruct your coding agent to follow the skill, e.g., Please follow the design-taste-frontend skill to generate the homepage code. For an image-to-code workflow, tell the agent to generate images first, then analyze, then code. Skills can be tuned by adjusting numeric settings (1-10) at the top of the file, such as DESIGN_VARIANCE for layout experimentation.
What are this agent's strengths and limitations?
- Provides multiple specialized skill variants for different scenarios (e.g., v1/v2, gpt-taste, redesign).
- Key skills (design-taste-frontend) have adjustable parameters (layout variance, motion intensity, visual density) for output control.
- Framework-agnostic: works with React, Vue, Svelte, etc., rules focus on design intent, not specific APIs.
- Includes image-generation skills that integrate with ChatGPT Images and Codex, enabling an image-to-code pipeline.
- The default skill (v2) is experimental and may change, though v1 is preserved for stability.
- Installation depends on the npx skills add CLI from Vercel Labs, adding a toolchain dependency.
- Focuses solely on frontend design; not useful for backend or data tasks.
- Image-generation skills require external image generation models (e.g., ChatGPT Images) and are not out-of-the-box standalone.
How does this agent compare with similar options?
Compatible with the Agent Skills ecosystem from Vercel Labs, using its installation CLI.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Taste-Skill: AI Frontend Aesthetic Upgrade This agent | 31 · Major gaps | CLIFree + model costs | ★ 99k | today | JavaScript | ChatGPT · Codex · Claude Code |
| UIZZE · Better UI for Coding Agents | 47 · Major gaps | Agent plugin / skillFreemium | ★ 63 | 6d ago | JavaScript | Codex · Claude Code |
| oh-my-design | 76 · Good | CLIFree | ★ 539 | 4d ago | JavaScript | Codex · Claude Code |
| Monocolor Editorial Print | 43 · Major gaps | Agent plugin / skillFree + model costs | ★ 3.4k | 1mo ago | Python | Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows the repository contains only prompt skills, with no code execution, network requests, or file system operations. Therefore, least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, and source attribution have no actual evidence, all scored 0. No malicious behavior found, but security cannot be verified.
Self-consistency: The skill list in README is mostly consistent with descriptions, but v2 is marked experimental, introducing uncertainty, scored 1. Dependency availability: No runtime dependencies mentioned, but installation relies on npx skills CLI, whose availability is unverified, scored 0. Failure messages: No error handling or failure prompts provided, scored 0.
Audience and scenarios: Clearly targets AI agents and developers, offering multiple skills for different scenarios, scored 2. Capability boundaries: Clearly distinguishes code skills and image generation skills, explaining their uses, scored 2. Trigger precision: Skill install names are clear, but trigger conditions or contexts are not specified, scored 1. Environment fit: Declares framework-agnostic and supports multiple agents, but no specific environment configuration provided, scored 2.
Information architecture: README is well-structured with table of contents and skill tables, scored 2. Install notes: Provides npx skills add command and single-skill installation method, scored 2. Naming stability: Install names are stable, v1 and v2 clearly distinguished, scored 2. Examples and FAQ: Provides example images and common questions, scored 2. Known limitations: Only mentions v2 is experimental, no other limitations listed, scored 1. License: MIT license clearly stated, scored 2. Versioning and changelog: CHANGELOG.md exists but content not provided in files, scored 2. Maintenance responsibility: Provides feedback channels and sponsorship info, but no explicit maintenance plan, scored 1.
Output usability: Skills aim to improve frontend design quality, output code or images, but specific output formats not provided, scored 2. Marginal value: Offers multiple skill variants, increasing flexibility, but actual effectiveness not demonstrated, scored 2. Cost-benefit: Simple installation, but no performance or resource consumption info, scored 1.
Claim traceability: Claims in README lack specific evidence or research links, scored 1. Cross-source corroboration: No third-party verification or test results, scored 0. Fact-inference separation: Some content like 'anti-slop' is subjective, not distinguishing facts from inferences, scored 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- The repository contains only prompt skills, no code execution, but security cannot be verified.
- v2 is marked experimental, may have unstable behavior.
- Installation relies on npx skills CLI, whose availability and security are unverified.
- No tests or verification results provided; claims lack evidence.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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v2.1.2 tasteskill v2.1.2
Release notes excerpt
Fixes [#151](https://github.com/Leonxlnx/taste-skill/issues/151): design-taste-frontend did not load in Claude Code. The skill description added in 2.1.0 contained an unquoted : , so its YAML frontmatter failed to parse and the skill was silently dropped ("Unknown skill"). Update to get it back: ```bash npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend" ``` Claude Code plugin users: /plugin update. …View on GitHub ↗ -
v2.1.1 tasteskill v2.1.1
Release notes excerpt
One skill to reach for. Installing taste-skill no longer means an agent might pick an older skill for the job. Install or upgrade ```bash npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend" ``` What changed - The classic skills are opt-in. soft, minimalist, brutalist, gpt-taste, Stitch, v1 and v2-experimental run only when you call them by name (/high-end-visual-design in Claude Code, $minimalist-ui in Codex). An agent never picks one by itself. …View on GitHub ↗ -
v2.1.0 tasteskill v2.1
Release notes excerpt
tasteskill v2.1 builds the screens people work in, not only the pages they read, and keeps every page's frame clean. Install or upgrade ```bash npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend" ``` The install name did not change. Needs Node 22+ and Chrome, Edge or Chromium. Installs nothing, uses no API keys. What is new …View on GitHub ↗