Skills

Taste-Skill: Give AI-Generated Interfaces More Design Taste

If you want AI-generated interfaces to stop looking identical, this is best suited to strengthening frontend layout, typography, motion, and spacing; note the default version is experimental v2, and single-skill installation must use the frontmatter install name rather than the folder name.

Official source · Taste-Skill

Install

npx skills add https://github.com/Leonxlnx/taste-skill

Who It's For

  • Frontend and full-stack developers who use AI to generate frontend interfaces but are unsatisfied with the "cookie-cutter" layouts it produces. This Skill is a set of portable Agent Skills designed to give AI-built interfaces more taste in layout, typography, motion, and spacing, rather than generating identical, generic UI every time.
  • It targets one thing only — interface design quality. It is not a general-purpose code assistant and does not handle backend logic or data issues.
  • Note: the default taste-skill (install name design-taste-frontend) is currently an experimental v2; the official project also keeps the original v1 available as design-taste-frontend-v1. If you prefer stability, choose v1 when installing.
  • The evidence does not state which languages or project frameworks it supports, or whether it fits your specific tech stack — this remains unclear.

Your First Try in 3 Minutes

The officially recommended main path is installation via the skills CLI. Prerequisites: Node.js/npx installed locally, and an agent environment that supports Agent Skills (such as Claude Code, Codex, or Cursor — the project mentions the Skill works with these tools).

  1. Run the following in your project root:

    npx skills add https://github.com/Leonxlnx/taste-skill

    This command scans the repository's skills/ folder and installs the skills inside it; all skills are installed the same way.

  2. If you only want to install a single skill, use its install name (the name: field in the SKILL frontmatter — note this is not the folder name):

    npx skills add https://github.com/Leonxlnx/taste-skill --skill "design-taste-frontend"

  3. After installation, start a low-risk interface task in your agent session — for example, ask it to produce typography and spacing suggestions for a button component in the Skill's style. For the first attempt, have it output design suggestions or sample code rather than directly modifying your existing source files.

Success signal: the agent's output clearly reflects the Skill's design constraints — the layout is no longer a single centered/tidy default answer, but varies according to the settings. The Skill has a set of 1–10 dials (such as DESIGN_VARIANCE, MOTION_INTENSITY, and VISUAL_DENSITY) that control layout experimentation (low = centered/clean, high = asymmetric/modern), motion intensity, and information density. You can adjust these values in SKILL.md and observe the differences in output.

Important note: running npx skills add downloads files from the network and writes them into your project — this is an inherent side effect of the installation action itself. Verify the command and source before executing. Whether the Skill's generated text suggestions will directly modify your files, execute commands, or access the network is not stated in the evidence and remains unknown.

Alternative: you can also copy any SKILL.md into your project, or paste it directly into a ChatGPT / Codex conversation — this is a secondary, officially supported path.

Use Cases

Case 1: Improving the layout taste of a landing page. Scenario: your AI-generated landing page looks too "template-like." Task the agent: "Using the installed taste-skill, propose a more design-driven layout and spacing plan for this hero section." It will apply the Skill's core design guidance (layout, typography, motion, spacing), influenced by settings like DESIGN_VARIANCE — when the value is raised, expect the output to shift from conservative centered/clean layouts to asymmetric, more modern ones. The confirmable output is text plans or examples; whether it will directly rewrite your source files is not stated in the evidence and should be confirmed in the session.

Case 2: Adjusting motion and information density. Scenario: you want more restrained or richer motion. Modify MOTION_INTENSITY and VISUAL_DENSITY (both are 1–10 dials) in the settings section of SKILL.md, then ask the agent to regenerate the same interface. Comparing the differences in motion depth and information density between the two outputs lets you visually verify whether the Skill's settings took effect — a reversible, observation-only, low-risk experiment.

Boundaries of Use

  • Installation fetches files from the network via npx and writes them into your project — this is a clear side effect. Other runtime behavior (whether it reads or writes your other files, executes commands, or goes online) is not stated in the evidence and remains unknown; it cannot be inferred as "risk-free" on that basis.
  • The default version is experimental v2; for stability, switch to design-taste-frontend-v1.
  • Single-skill installation must use the install name (the frontmatter name: field), not the folder name, otherwise you may not get the skill you want.
  • Officially stated installation differences: the CLI method and manually copying SKILL.md / pasting into a ChatGPT or Codex conversation are coexisting official paths; choose the one that fits your environment.
  • Still unconfirmed: the range of supported text/code languages, the specific frameworks it applies to, the complete list of settings (the evidence only lists three example dials), and the exact way skills are triggered in each client.