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 stitch-skillgit 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/stitch-skill)<a href="https://agentmods.dev/skills/leonxlnx/taste-skill/stitch-skill"><img src="https://agentmods.dev/badge/skills/leonxlnx/taste-skill/stitch-skill/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/stitch-skill"><img src="https://agentmods.dev/badge/skills/leonxlnx/taste-skill/stitch-skill.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 | $0.00050 | $0.02741 |
| Opus 5 | $0.00025 | $0.01371 |
| Sonnet 5 | $0.00010 | $0.00548 |
| Haiku 4.5 | $0.00005 | $0.00274 |
Grade A, and why
stitch-design-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 9d 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:
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
- stitch-design-taste — 100% identical, 41 lines differ
- stitch-design-taste — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stitch Design Taste — Semantic Design System Skill
Overview
This skill generates DESIGN.md files optimized for Google Stitch screen generation. It translates the battle-tested anti-slop frontend engineering directives into Stitch's native semantic design language — descriptive, natural-language rules paired with precise values that Stitch's AI agent can interpret to produce premium, non-generic interfaces.
The generated DESIGN.md serves as the single source of truth for prompting Stitch to generate new screens that align with a curated, high-agency design language. Stitch interprets design through "Visual Descriptions" supported by specific color values, typography specs, and component behaviors.
Prerequisites
- Access to Google Stitch via labs.google/stitch
- Optionally: Stitch MCP Server for programmatic integration with Cursor, Antigravity, or Gemini CLI
The Goal
Generate a DESIGN.md file that encodes:
- Visual atmosphere — the mood, density, and design philosophy
- Color calibration — neutrals, accents, and banned patterns with hex codes
- Typographic architecture — font stacks, scale hierarchy, and anti-patterns
- Component behaviors — buttons, cards, inputs with interaction states
- Layout principles — grid systems, spacing philosophy, responsive strategy
- Motion philosophy — animation engine specs, spring physics, perpetual micro-interactions
- Anti-patterns — explicit list of banned AI design clichés
Analysis & Synthesis Instructions
1. Define the Atmosphere
Evaluate the target project's intent. Use evocative adjectives from the taste spectrum:
- Density: "Art Gallery Airy" (1–3) → "Daily App Balanced" (4–7) → "Cockpit Dense" (8–10)
- Variance: "Predictable Symmetric" (1–3) → "Offset Asymmetric" (4–7) → "Artsy Chaotic" (8–10)
- Motion: "Static Restrained" (1–3) → "Fluid CSS" (4–7) → "Cinematic Choreography" (8–10)
Default baseline: Variance 8, Motion 6, Density 4. Adapt dynamically based on user's vibe description.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 185 lines · 50 tokens per session scan A 46bdb08fee2d
stitch-design-taste is a skill published in the GitHub repository Leonxlnx/taste-skill (85,228 stars, last pushed 15d ago), licensed MIT. It adds 50 tokens to every session and 2,741 once invoked, about $0.0003 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.
mckinsey-cover
An image-generation guide for making Chinese-language covers and infographics in a McKinsey-inspired consulting style.
design-md-extractor
Extract a visual design system from any website URL and generate a DESIGN.md that strictly follows the google-labs-code/design.md specification format. Use this skill for: "create a DESIGN.md for [url]", "extract design system from [url]", "generate design tokens for [url]", "analyze [brand]'s visual style", "make a…
image-design
A guide for creating detailed prompts for AI-generated images using photographic choices such as the subject, composition, lighting, camera view, style, and texture.
extension-assets
Generate and manage all Chrome extension assets: icons (16–128px), CWS listing images, promotional tiles, and public/ folder setup. Supports ImageMagick, Gemini API, and manual prompt templates.
extension-ui
Build polished Chrome extension UIs (popup/sidepanel/options). Analyze existing UI, suggest improvements, set up design systems, enforce a11y and UX best practices.