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Visual Review

Community
lucasfcosta
visual-review

Use when a backpressured loop has a front-end/UI change and needs to compare the rendered result against its design reference (a Figma frame or images on a Linear ticket) — catching layout, spacing, contrast, and visual-consistency defects automated tests and happy-path clicking miss.

Overview

Publisherlucasfcosta
Repositorybackpressured
Skill namevisual-review
Stars
63
Forks
2
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 lucasfcosta on GitHub. Read the source before you install it.

Installation

Install the Visual 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/lucasfcosta/backpressured/tree/main/skills/visual-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/lucasfcosta/backpressured.git /tmp/backpressured
mkdir -p .claude/skills
cp -r /tmp/backpressured/skills/visual-review .claude/skills/visual-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Visual 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 Visual 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 Visual 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.

Visual Review

Overview

You are the machine the producer ran so a visual defect wouldn't reach a human — or a user — first. Lint, types, and tests say nothing about whether the page looks right: layout, spacing, color contrast, and overall aesthetics live in a gap that automated checks and a quick manual click-through don't cover. This review is backpressure for that gap.

Core principle: structured region-by-region comparison against the real design, not one gestalt glance. A single "looks fine to me" glance reliably catches gross breakage and reliably misses the spacing, weight, and color mismatches that make a UI feel off. Break it down so the subtle issues have nowhere to hide.

When to Use

  • A backpressured loop has a front-end/UI change and a design reference to check it against — per-iteration on the changed view, and on the whole feature before done.

Not for: non-UI changes (skip it); pixel-perfect automated regression diffing (this is a structured human-style review, not a pixel diff). If there is no design reference available, say so and don't fake a pass — a review against nothing is not a review.

Step 1 — Capture the current UI

Run the app locally and drive a real browser with the Playwright MCP. Navigate to the changed view and screenshot it at the viewport(s) the design targets (e.g. 1280/1440 desktop; the design's mobile width if it specifies one). Take a full-page screenshot plus per-region screenshots so you can look at each area closely — a full-page shot alone is too coarse to catch local issues. Capture each region with a per-element screenshot (Playwright's locator(...).screenshot() / element clip), not by cropping the full-page image by eye — a real element shot is what lets you measure against the design.

Step 2 — Get the design reference

Obtain the source of truth and actually look at it: the Figma frame, or the images attached to the Linear ticket (pull them via the Linear MCP). Read the image files so you can see them — you cannot compare against a reference you haven't loaded. If you genuinely can't get one, stop and report that; do not approve by default. If a Figma frame and a ticket image disagree, treat Figma as authoritative unless the ticket explicitly overrides it.

Step 3 — Compare region by region

Walk the page in pieces — header → main content → footer, then components within each — comparing your screenshot to the design for that region. For each region, check:

  • Layout & structure: correct elements, in the right order and proportions.
  • Spacing: padding, margins, and gaps between fields/sections — the most common and most-missed mismatch.
  • Alignment: fields, labels, and edges line up; nothing visually drifts.
  • Typography: size, weight, family, and case match for headings, labels, and body.
  • Color & contrast: backgrounds, text, accents/primary; flag text below WCAG AA (4.5:1 normal text, 3:1 large) rather than judging "low contrast" by eye.
  • Components & completeness: every element in the design is present (and nothing extra); icons, buttons, and inputs styled to match.
  • Interactive states: hover, focus, disabled, and error styling — not just the default state.
  • Responsive: if the design specifies breakpoints, check them; don't silently verify only one width.

Step 4 — Report, fix, re-verify

Report each discrepancy with a severity — [BLOCKER] wrong/missing element, wrong color, broken layout · [SHOULD] clear spacing/typography/alignment mismatch · [NIT] sub-pixel or barely perceptible — plus the region and the specific difference (not "spacing looks off" but "32px gap above the form vs ~16px in the design"). Get those numbers from real measurements — the rendered element's computed box (browser devtools / Playwright boundingBox()) against Figma's measured values — not by eyeballing pixels off a screenshot, or your "specific" finding is just a dressed-up guess. Fix the BLOCKER/SHOULD findings, re-screenshot, and re-compare — a fix you didn't re-capture isn't verified.

Passing = every region matches the reference with no outstanding BLOCKER/SHOULD discrepancies, verified at the design's target viewport(s).

Common rationalizations

RationalizationReality
"It looks fine to me at a glance"A glance catches gross breakage and misses everything subtle. Go region by region.
"I checked the one default viewport"If the design specifies breakpoints, an unchecked width is an unverified design.
"I couldn't find the Figma, but it's probably right"A review against no reference is not a review. Get the reference or report that you can't.
"Close enough on spacing/weight""Close enough" with a number ("16 vs 32px") is a finding; "close enough" without one is a guess.
"I only need the default state"Hover/focus/disabled/error are part of the design too. Check them.
"I fixed it, no need to screenshot again"An un-recaptured fix is an assertion. Re-screenshot and re-compare.

Red flags — STOP

  • Approving from a single full-page glance with no per-region comparison.
  • Passing with no design reference actually loaded and viewed.
  • Reporting "spacing looks off" with no specific before/after measurement.
  • Verifying only the default state or a single viewport the design didn't limit you to.
  • Calling a fix done without re-screenshotting it.

Frequently asked questions

What does the Visual Review AI skill do?

Use when a backpressured loop has a front-end/UI change and needs to compare the rendered result against its design reference (a Figma frame or images on a Linear ticket) — catching layout, spacing, contrast, and visual-consistency defects automated tests and happy-path clicking miss.

Why use Visual Review on TypingMind?

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

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

Which AI models can use Visual 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 Visual Review?

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

Is the Visual Review AI skill free?

Yes. It is published on GitHub by lucasfcosta 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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