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 AgriciDaniel/gogh --skill taste-skill-v2git clone --depth 1 https://github.com/AgriciDaniel/goghWrote 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/agricidaniel/gogh/taste-skill-v2)<a href="https://agentmods.dev/skills/agricidaniel/gogh/taste-skill-v2"><img src="https://agentmods.dev/badge/skills/agricidaniel/gogh/taste-skill-v2/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/agricidaniel/gogh/taste-skill-v2"><img src="https://agentmods.dev/badge/skills/agricidaniel/gogh/taste-skill-v2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.21955 |
| Opus 5 | $0.00000 | $0.10977 |
| Sonnet 5 | $0.00000 | $0.04391 |
| Haiku 4.5 | $0.00000 | $0.02195 |
Grade A, and why
taste-skill-v2 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.
This is a copy
98% identical to design-taste-frontend — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: design-taste-frontend description: Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
tasteskill: Anti-Slop Frontend Skill
Landing pages, portfolios, and redesigns. Not dashboards, not data tables, not multi-step product UI. Every rule below is contextual. None of it fires automatically. First read the brief, then pull only what fits.
0. BRIEF INFERENCE (Read the Room Before Anything Else)
Before touching code or tweaking dials, infer what the user actually wants. Most LLM design output is bad because the model jumps to a default aesthetic instead of reading the room.
0.A Read these signals first
- Page kind - landing (SaaS / consumer / agency / event), portfolio (dev / designer / creative studio), redesign (preserve vs overhaul), editorial / blog.
- Vibe words the user used - "minimalist", "calm", "Linear-style", "Awwwards", "brutalist", "premium consumer", "Apple-y", "playful", "serious B2B", "editorial", "agency-y", "glassy", "dark tech".
- Reference signals - URLs they linked, screenshots they pasted, products they named, brands they're competing with.
- Audience - B2B procurement panel vs. design-conscious consumer vs. recruiter scanning a portfolio. The audience picks the aesthetic, not your taste.
- Brand assets that already exist - logo, color, type, photography. For redesigns, these are starting material, not optional input (see Section 11).
- Quiet constraints - accessibility-first audiences, public-sector, regulated industries, trust-first commerce, kids' products. These constraints OVERRIDE aesthetic preference.
0.B Output a one-line "Design Read" before generating
Before any code, state in one line: "Reading this as: <page kind> for <audience>, with a <vibe> language, leaning toward <design system or aesthetic family>."
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.
- 10d ago First seen · 1,208 lines · 0 tokens per session scan A 5973aab78218
taste-skill-v2 is a skill published in the GitHub repository AgriciDaniel/gogh (58 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 21,955 tokens. A static security scan graded it A with 0 findings. It is 98% identical to design-taste-frontend, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
chakra-ui-builder
Build responsive, accessible UI components and layouts using Chakra UI v3, install or configure Chakra UI in new and existing projects, and design scalable themes using tokens, semantic tokens, recipes, and slot recipes. Use this skill whenever a user asks to build, create, or generate any UI component, page, form…
visual-ralph
Visual Ralph orchestration for frontend UI from generated references, static references, or live URL targets, using $ralph with built-in visual verdict and pixel-diff evidence until the implementation matches and leaves a reproducible design system.
frontend-ui-dark-ts
Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations. Use when creating dashboards, admin panels, or data-rich interfaces with a refined dark aesthetic.
reveal-hover-effect
Build cursor-following spotlight reveals that expose a second aligned image through a soft radial mask. Use for hover-to-color, before-and-after, x-ray, material, texture, product-detail, and illustrated hero effects where a desaturated or embossed base image should remain visible while another treatment follows an…
frontend-visual-qa
Audits already-rendered web, landing-page, HTML deck/slide, browser tool/game, dashboard/admin, design-system, and desktop UIs using real-browser or native-app journeys, inspected screenshots, DOM geometry, responsive or projection viewports, and a bundled Playwright sweep. Use after UI implementation to find…
prototype-web
A clickable, high-fidelity web product prototype with navigation, a hero section, feature cards, steps, social proof, and optional pricing. It is designed to resemble a finished landing page while remaining a prototype.