GPT Taste Skill (gpt-taste)
Gives your AI-generated interfaces Awwwards-level design engineering, banishing boring, generic slop.
The skill only outputs design specs and code without requiring system permissions or data access, so risk is low. However, no user confirmation flow exists, and reliance on external services (like picsum.photos) raises data-flow transparency concerns. The publisher is unverified, but no security issues are apparent. Hence scored 12, deducting for incomplete permission scoping and confirmation mechanisms.
Instructions are clear, but the skill depends on external image services and simulated Python execution without tests or error handling, so it may not work reliably under abnormal input. Static review cannot execute verification, hence scored 7, deducting for insufficient testing and error handling.
The skill targets frontend design scenarios for GPT/Codex with clear boundaries, but lacks non-fit scenarios and depends on overseas services (picsum.photos) that may be unreachable for mainland China users, hence scored 7.
README provides installation and usage instructions, license is clear, and there are maintenance signals, but the skill itself lacks versioning, changelog, and known-limitation disclosure, hence scored 10.
Goals are clear with specific rules, but output quality depends on the LLM and no verifiable example outputs are provided, so static review cannot confirm direct usability, hence scored 7.
There are a README and example images, but no tests or execution evidence, and key claims cannot be independently verified, hence scored 5.
- External dependencies: The skill uses overseas resources like picsum.photos, which may be unreachable from mainland China networks, affecting performance.
- Missing user confirmation: The skill auto-generates designs without explicit user confirmation, which may produce unexpected outputs.
- Data transparency: The skill does not disclose whether user data is transmitted to external services; evaluate before use.
- Publisher is unverified; use with caution, though no specific risk identified.
What does this skill do, and when should you use it?
This skill transforms an AI into an elite frontend design engineer, enforcing Python-driven randomization for layout selection and explicitly banning common AI design slop (6-line headings, grid gaps, cheap labels). It mandates a strict AIDA page structure, gapless bento grids, advanced GSAP motion (pinning, stacking, scrubbing), and massive vertical spacing. After installation, the model must produce a design_plan block before writing any UI code to verify its design choices.
Reads the user's prompt and simulates a Python script execution to perform true randomization, selecting a hero architecture, a typography stack (banning Inter), component architectures, and GSAP animation patterns; enforces a pre-flight <design_plan> output with AIDA checks, hero math verification, bento grid density verification, and label/button scans; generates React code with specific Tailwind classes and GSAP ScrollTrigger patterns for scroll pinning, image scaling/fading, text scrubbing, and card stacking; bans emojis, cheap meta-labels, and stock-photo looks using picsum.photos seeds with CSS filters.
- A developer who wants Awwwards-level landing pages generated by GPT or Codex without manually tweaking layouts.
- A team using vibe coding for frontend builds but frustrated by the common template look of AI output.
- A developer using Codex for rapid prototyping who needs strict design constraints to ensure high quality.
- A frontend engineer wanting advanced GSAP animations (scroll pinning, stacking, text scrubbing) in AI-generated React projects.
- A design-conscious developer who wants to review AI design decisions via a mandatory pre-generation plan.
What are this skill's strengths and limitations?
- Provides hard rules against common AI UI flaws (text wrapping, grid gaps, repetitive layouts).
- Enforces a design_plan pre-flight before code, reducing rework.
- Includes concrete, actionable rules (e.g., max-w classes, grid-flow-dense) that are verifiable.
- Focuses on advanced GSAP animations beyond basic hover effects.
- Part of a broader skill set offering other style variants (minimalist, brutalist, etc.).
- Targets React and Tailwind, limiting use with other frameworks (Vue, Svelte).
- Requires a heavy generation process (multiple GSAP ScrollTrigger patterns) that may be overkill for simple pages.
- The skill is marked 'experimental' and 'v2' in its SKILL.md, indicating its rules are evolving and may be unstable.
- No documentation for testing or production deployment is provided.
- The simulated Python randomization is not truly random but deterministically picks from character count, so results may still be predictable.
How do you install this skill?
This skill is part of the Taste Skill repository, installable via the Agent Skills CLI for the whole collection. To install this specific skill:
npx skills add https://github.com/Leonxlnx/taste-skill --skill "gpt-taste"Alternatively, copy the SKILL.md file into your project or conversation.
How do you use this skill?
After installation, attach the skill file to your AI coding agent (like GPT or Codex) and describe the website you want. Before coding, the model will output a <design_plan> block with a mock Python randomization output, AIDA checks, hero math verification, bento grid density verification, and label/button scans. After you approve this plan, it will generate React code with Tailwind CSS and GSAP animations. You'll need a project with React and Tailwind, and the ability to install GSAP.
How does this skill compare with similar options?
Compared to the default 'design-taste-frontend' skill, this skill is the stricter GPT/Codex variant, with higher layout variance, stronger GSAP direction, and aggressive anti-slop rules. The README describes it as 'Stricter variant for GPT/Codex: higher layout variance, stronger GSAP direction, aggressive anti-slop', indicating it's an enhanced version of the standard taste-skill.