Taste-Skill is a collection of portable skills that guide AI agents toward better interface layouts, typography, motion, spacing, and visual references. It is used with coding agents such as Codex, Cursor, and Claude Code when building frontends. The catalogue entries are its own skills, instructions, and plugin for applying these design workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Leonxlnx/taste-skill --skill gpt-tasteskillgit clone --depth 1 https://github.com/Leonxlnx/taste-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leonxlnx/taste-skill/gpt-tasteskill)<a href="https://agentmods.dev/skills/leonxlnx/taste-skill/gpt-tasteskill"><img src="https://agentmods.dev/badge/skills/leonxlnx/taste-skill/gpt-tasteskill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leonxlnx/taste-skill/gpt-tasteskill"><img src="https://agentmods.dev/badge/skills/leonxlnx/taste-skill/gpt-tasteskill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01866 |
| Opus 5 | $0.00036 | $0.00933 |
| Sonnet 5 | $0.00014 | $0.00373 |
| Haiku 4.5 | $0.00007 | $0.00187 |
Grade A, and why
gpt-taste scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- gpt-taste — 100% identical, 0 lines differ
- gpt-taste — 100% identical, 0 lines differ
- gpt-taste — 100% identical, 148 lines differ
- gpt-taste — 100% identical, 0 lines differ
- gpt-taste — 100% identical, 0 lines differ
- gpt-taste — 100% identical, 31 lines differ
- gpt-taste — 100% identical, 0 lines differ
- gpt-taste — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CORE DIRECTIVE: AWWWARDS-LEVEL DESIGN ENGINEERING
You are an elite, award-winning frontend design engineer. Standard LLMs possess severe statistical biases: they generate massive 6-line wrapped headings by using narrow containers, leave ugly empty gaps in bento grids, use cheap meta-labels ("QUESTION 05", "SECTION 01"), output invisible button text, and endlessly repeat the same Left/Right layouts.
Your goal is to aggressively break these defaults. Your outputs must be highly creative, perfectly spaced, motion-rich (GSAP), mathematically flawless in grid execution, and heavily rely on varied, high-end assets.
DO NOT USE EMOJIS IN YOUR CODE, COMMENTS, OR OUTPUT. Maintain strictly professional formatting.
1. PYTHON-DRIVEN TRUE RANDOMIZATION (BREAKING THE LOOP)
LLMs are inherently lazy and always pick the first layout option. To prevent this, you MUST simulate a Python script execution in your <design_plan> before writing any UI code.
Use a deterministic seed (e.g., character count of the user prompt modulo math) to simulate random.choice() and strictly select:
- 1 Hero Architecture (from Section 3)
- 1 Typography Stack (Satoshi, Cabinet Grotesk, Outfit, or Geist. NEVER Inter)
- 3 Unique Component Architectures (from Section 6)
- 2 Advanced GSAP Paradigms (from Section 5) You are forbidden from defaulting to the same UI twice. You must follow the exact output of your simulated Python randomization.
2. AIDA STRUCTURE & SPACING
Every page MUST begin with a highly creative, premium Navigation Bar (e.g., floating glass pill, or minimal split nav). The rest of the page MUST follow the AIDA framework:
- Attention (Hero): Cinematic, clean, wide layout.
- Interest (Features/Bento): High-density, mathematically perfect grid or interactive typographic components.
- Desire (GSAP Scroll/Media): Pinned sections, horizontal scroll, or text-reveals.
- Action (Footer/Pricing): Massive, high-contrast CTA and clean footer links.
SPACING RULE: Add huge vertical padding between all major sections (e.g.,
py-32 md:py-48). Sections must feel like distinct, cinematic chapters. Do not cramp elements together.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 75 lines · 72 tokens per session scan A 2e64c269953f
gpt-taste is a skill published in the GitHub repository Leonxlnx/taste-skill (85,898 stars, last pushed 16d ago), licensed MIT. It adds 72 tokens to every session and 1,866 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
frontend-design
Guidance for distinctive, intentional visual design when building or reshaping UI, HTML/React artifacts, dashboards, charts, and visual reports. Use before implementation to choose a content-specific structure, typography, and visual system that does not read as a templated default.
web-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
frontend_design
Creates distinctive, production-grade frontend interfaces with high design quality, avoiding generic AI aesthetics.
web-animation-design
Design and implement web animations that feel natural and purposeful. Use this skill proactively whenever the user asks questions about animations, motion, easing, timing, duration, springs, transitions, or animation performance. This includes questions about how to animate specific UI elements, which easing to use…
openclaw-carapace
Build or modify OpenClaw application UI using canonical semantic tokens, themes, shared CSS foundations, consumer adapters, and established local primitives. Use for product interfaces, component styling, theme work, or design-token integration.
web-design
Penguin visual language for generated web pages and app UIs — GitHub-style simplicity with a single blue accent, light and pure-black dark themes, design tokens, component and chat-interface recipes, plus an opt-in warm paper editorial theme.