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Ui Review

Community
arla6ka
ui-review

Use to critique a screen, flow, prototype or running build, accessibility included, and rank what is broken, such as "check this page before I ship", "is this flow ready", "is it accessible", keyboard or screen-reader checks, "it feels off on my phone" or "it feels janky". Not for token compliance, which is token-mapping.

Overview

Publisherarla6ka
Repositorydesign.how
Skill nameui-review
Stars
200
Forks
15
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by arla6ka on GitHub. Read the source before you install it.

Installation

Install the Ui 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/arla6ka/design.how/tree/main/skills/ui-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/arla6ka/design.how.git /tmp/design.how
mkdir -p .claude/skills
cp -r /tmp/design.how/skills/ui-review .claude/skills/ui-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ui 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 Ui 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 Ui 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.

UI review

The render is the evidence. A description, DOM text or source code only supports it. Severity, the 13 criteria and the edge cases are in references/criteria.md. An installed design system's own rules come first and count as agreed criteria.

Steps

  1. Scope. What the caller names, else the branch's changed screens, else the five top routes.
  2. Lint. Run the project's own lint on the scoped files. Record the command and exit code.
  3. Capture each screen at each width (the app's narrowest and widest, 390 and 1280 by default) in the states that matter, one fresh navigation per width. With build-design-system installed, build-design-system/scripts/capture.mjs does this behind a guard that blocks every write and saves each capture's accessibility tree. It needs --writes <file> listing the app's writes, or --no-backend, and writes only to its --out folder (keep that under .ui-review/). Otherwise use the host's browser tool and save the tree yourself. Run the project's accessibility scanner too when it has one.
  4. Fix the purpose of each screen in one line. Mark it assumed when the caller did not give it.
  5. Probe the traps each screen has, on a local build only: keyboard path, focus after close, loading and error states, a throttled tab, reduced motion, and a touch width. Each probe gets a measured result or a reason it did not run. Measure press feedback with the Event Timing API (a PerformanceObserver on event entries, each duration under 100 ms). Do not click anything that writes to shared data.
  6. Walk the criteria at each width. Cite each finding by number and name, or by the installed rule's ID.
  7. Sort. With more than one reviewer available (a panel), give each the same prompt and no persona, read only. The coordinator then sorts every finding into blocking, should-fix, note or dismissed, naming who raised it and giving one line of reason. A finding raised by two reviewers on their own is the strongest signal. Dismiss nitpicks, hypotheticals nobody traced to a real call site, and "I would have done it differently". Within a bucket, put surfaces that write data first, then order by places hit. Without a panel, do the same alone.
  8. Walk the edge cases and mark each shown or not shown.
  9. Write the verdict last.

Rules

  • A finding has a criterion, a findable location (the element by role and name, with its capture, as in button 'Save changes' (settings-1280.png)), the width, and the evidence. A finding that rests on taste is dropped to Left out.
  • Wrong: "The spacing feels tight." Right: "Label sits 24 px from its own field and 8 px from the next one (criterion 4, settings-390.png)."
  • An inferred finding is written as the check that settles it. An inferred blocking finding makes the verdict no, pending <the check>.
  • A claim that something works or is fixed names a command or capture from this session.
  • Contrast, target size and keyboard problems are ranked. A change to the accessibility tree that only adds semantics is ranked. One that removes or restructures semantics goes to a person.
  • A finding or pass on responsiveness to input (criterion 13) from a screenshot is invalid. It needs a measured probe. A focus ring is the reverse. Reach the control with real Tab presses and judge the ring from the capture, because a container that draws it through :has(:focus-visible) leaves the focused element reporting no outline.
  • Product decisions behind a fix go to For a person to decide. Keeping the user's input is never one.

Output

The first line answers the ask in plain words. The ship line follows: Ready to ship: no. Enter in Email cancels the invite (blocking 1, should-fix 2, 3 questions for a person). Any blocking finding makes the verdict no, an inferred one no, pending <the check>, should-fix alone yes, with fixes, only notes yes.

Then, in order:

  1. Review record. Source, tool, widths, date, criteria file, the purpose marked given or assumed, every assumption and gap.
  2. Findings by bucket, each with who raised it, what was seen, where, criterion, why it matters here, evidence, and the fix as a check that fails on the current build (Fix Enter in Email so that it sends 1 POST).
  3. Edge cases not shown. One line each.
  4. For a person to decide.
  5. Dismissed and left out. Each with who raised it and the reason.

Run on its own, save the report to .ui-review/<date>-<flow>.md with captures beside it. When a coordinator asks for captures only, return the capture paths, widths and Coverage: line, and no findings. The chat reply gives the answer and ship lines, each command with its exit code, and one prompt the person can paste to take the next step.

Done

Every finding has a criterion, a location, evidence and only the widths where it shows. Every probe has a result or a reason. The first line, the ship line and the summary agree. The last line reads Coverage: with the routes, widths, themes and probes measured, then what was not.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ui Review AI skill do?

Use to critique a screen, flow, prototype or running build, accessibility included, and rank what is broken, such as "check this page before I ship", "is this flow ready", "is it accessible", keyboard or screen-reader checks, "it feels off on my phone" or "it feels janky". Not for token compliance, which is token-mapping.

Why use Ui Review on TypingMind?

Because you install it once and use it with any model. Ui 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 Ui Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/arla6ka/design.how/tree/main/skills/ui-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ui 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 Ui Review?

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

Is the Ui Review AI skill free?

Yes. It is published on GitHub by arla6ka 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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