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Taste Design Review

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madebymustafa
taste-design-review

Structured visual and usability audit of existing interfaces — landing pages, SaaS apps, portfolios. Screenshot-first review against taste rules, Nielsen heuristics, and accessibility floor; severity-ranked findings with specific fixes; before/after verification. Use when asked to critique, review, or improve an existing UI.

Overview

Publishermadebymustafa
Repositorydesign-taste
Skill nametaste-design-review
Stars
198
Forks
10
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by madebymustafa on GitHub. Read the source before you install it.

Installation

Install the Taste Design Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/madebymustafa/design-taste/tree/main/skills/taste-design-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/madebymustafa/design-taste.git /tmp/design-taste
mkdir -p .claude/skills
cp -r /tmp/design-taste/skills/taste-design-review .claude/skills/taste-design-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Taste Design Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Taste Design Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Taste Design Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

taste-design-review

Audit like a design director: screenshot first, then list findings by severity, then fix only what earns it.

1. THE PROCESS (Audit → Rank → Fix → Verify)

Never fix while reviewing. The pass order:

  1. Capture: full-page screenshots of every viewport (desktop 1440, tablet 768, mobile 390). Review what is, not what you think is.
  2. First read (8 seconds): what does this page say it is? Can you name the product, audience, and primary action without scrolling?
  3. Structured pass: apply the checks below, in order.
  4. Rank findings: P0 (broken/legal/blocking) / P1 (seriously undermines quality) / P2 (polish) / P3 (preference).
  5. Fix in ranked order with atomic changes; re-screenshot and verify each fix didn't break the design argument.
  6. Report: findings table with screenshot references, severity, and the one-sentence fix for each.

2. THE TASTE PASS (Visual Quality)

  • The kill question: could this page have been generated by a default agent (slop patterns: purple gradients, centered hero + 3 cards, emoji icons, Inter-only, rounded-2xl, browser-chrome screenshot)? Every "yes" is a P1.
  • Hierarchy: one dominant element per viewport; does the eye land where the business wants it?
  • Type: any text under 10px, more than 3 weights, no display contrast, body full-width, 6-line paragraphs, widows in headings.
  • Color: more than ~3 meaningful colors, gradients doing decoration, accent used >4 times per viewport, white text on pale fills, contrast failures (exact: 4.5:1 body / 3:1 large / 3:1 UI).
  • Alignment: elements sharing an edge line up (check visually); hairline grid vs. floating cards scattered; spacing scale integrity (any 7px or 13px paddings?).
  • Depth: shadows as the only depth source; glass everywhere; cards inside cards inside cards.

3. THE HEURISTIC PASS (Usability — Nielsen's 10, Applied)

  1. Visibility of system status (2–3s): every action produces feedback; no dead buttons; loading states exist.
  2. Match with the real world: language is the user's, not the company's ("deploy instance" on a consumer site = fail).
  3. User control & freedom: exit routes everywhere; undo exists for destructive acts.
  4. Consistency: same action looks the same everywhere; one style of button; one way to reach the same destination.
  5. Error prevention: destructive actions confirmed; submit buttons guarded; autocomplete on forms.
  6. Recognition over recall: no invented jargon; navigation names are self-explanatory.
  7. Flexibility: shortcuts for repeat users (search, keyboard) without cluttering the novice path.
  8. Aesthetic & minimalist: anything that hides the primary action is noise. If an element doesn't serve a job, it's deleted in the report.
  9. Errors communicated well: every error states what happened, why, and what to do — never just "Error".
  10. Help & documentation: help exists where users get stuck, not in a help center nobody visits.

4. THE ACCESSIBILITY PASS (Short Version — See taste-accessibility)

  • Tab through: focus visible at every stop; no traps; logical order.
  • Every input labeled; every button/link has text; alt text is meaningful or empty.
  • Contrast spot-check on every distinct surface (heading/body/button/placeholder).
  • prefers-reduced-motion handled; anything autoplaying or auto-advancing paused.

5. THE CONVERSION PASS (If Asked to Improve, Not Just Audit)

  • One primary CTA above the fold; secondary actions visually quieter or deferred.
  • The value proposition lands in ≤ 8 seconds (headline does it, not the sub-sub-line).
  • Friction inventory: fields, steps, and clicks between "interested" and "started" — each one justified.
  • Trust signals exist where decisions happen (near CTA, in pricing): social proof, guarantees, no-fine-print.

6. REPORT FORMAT (Fixed)

text
## Audit — <page> (<viewport set>)

### P0 — Blocking
- [screenshot ref] finding + one-sentence fix

### P1 — Quality
...

### P2 — Polish
...

### P3 — Preference
...

### Verdict (one paragraph)
Is the design argument clear? What single change would improve the page most?

Severity discipline: P3 items are listed but never fixed in the same session as P1s. The verdict names ONE change — if you can't name one, the audit didn't find the real issue.

7. CHECKLIST (Fail the Audit If…)

  • No screenshots were taken/reviewed (never audit from code alone).
  • Findings lack severities or screenshot references.
  • Slop patterns dismissed as "style preference" (they're P1).
  • Anything on the page was fixed before the full audit pass completed.
  • The verdict doesn't name a single most-important change.

Frequently asked questions

What does the Taste Design Review AI skill do?

Structured visual and usability audit of existing interfaces — landing pages, SaaS apps, portfolios. Screenshot-first review against taste rules, Nielsen heuristics, and accessibility floor; severity-ranked findings with specific fixes; before/after verification. Use when asked to critique, review, or improve an existing UI.

Why use Taste Design Review on TypingMind?

Because you install it once and use it with any model. Taste Design Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Taste Design Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/madebymustafa/design-taste/tree/main/skills/taste-design-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Taste Design Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Taste Design Review?

As many as you like. As long as a model supports skills, you can use Taste Design Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Taste Design Review AI skill free?

Yes. It is published on GitHub by madebymustafa under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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