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Visual Diff

Community
Houseofmvps
visual-diff

Visual Regression Testing — screenshot comparison before and after changes. Use when user wants to check for visual regressions, compare UI changes, or verify CSS/layout changes didn't break anything.

Overview

PublisherHouseofmvps
Repositoryultraship
Skill namevisual-diff
Stars
122
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 Houseofmvps on GitHub. Read the source before you install it.

Installation

Install the Visual Diff 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.
  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/Houseofmvps/ultraship.git /tmp/ultraship
mkdir -p .claude/skills
cp -r /tmp/ultraship/skills/visual-diff .claude/skills/visual-diff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Visual Diff 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 Diff 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 Diff 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 Regression Testing

Automated screenshot comparison using Playwright. Catch visual bugs before they ship.

Process

Phase 1: Determine Test Scope

Ask the user:

  1. What URL(s) to test? (localhost, staging, or production)
  2. What changed? (CSS update, component refactor, dependency upgrade, etc.)

If the user already described what changed, skip asking.

Phase 2: Take "Before" Screenshots

If comparing against the current state (before making changes):

Use the Playwright MCP to capture screenshots:

  1. Navigate to the URL:

    • Use browser_navigate to go to the target URL

  2. Take full-page screenshot:

    • Use browser_take_screenshot to capture the current state

  3. Capture key viewports:

    • Desktop (1920x1080): browser_resize then browser_take_screenshot
    • Tablet (768x1024): browser_resize then browser_take_screenshot
    • Mobile (375x812): browser_resize then browser_take_screenshot

  4. Save screenshots with descriptive names noting they are "before" state.

Phase 3: Make Changes

Let the user make their changes, or make them yourself if that's the task.

Phase 4: Take "After" Screenshots

Repeat the same screenshot process for the same URLs and viewports.

Phase 5: Visual Comparison

Compare before and after screenshots:

  1. Layout shifts — did any elements move unexpectedly?
  2. Color changes — did colors, gradients, or shadows change?
  3. Typography — did font sizes, weights, or spacing change?
  4. Responsive issues — does it look correct on all viewports?
  5. Missing elements — did anything disappear?
  6. Overflow issues — is content clipping or overflowing?

Use browser_snapshot to get the accessibility tree and compare DOM structure between before/after.

Phase 6: Report

Present findings in a clear format:

No Visual Regressions Found:

  • "All pages look identical across desktop, tablet, and mobile viewports."

Regressions Detected: For each regression:

  • Page: URL where the issue appears
  • Viewport: Which screen size is affected
  • What changed: Description of the visual difference
  • Severity: Critical (broken layout), High (noticeable shift), Medium (minor difference), Low (pixel-level)
  • Suggested fix: How to resolve the regression

Phase 7: Targeted Testing

For specific component changes, also test:

  • Hover states (use browser_hover)
  • Click interactions (use browser_click)
  • Form states (use browser_fill_form)
  • Dark mode (if applicable)
  • Loading states
  • Error states
  • Empty states

Key Pages to Always Test

When the user doesn't specify pages, test these by default:

  1. Homepage / Landing page (/)
  2. Login/signup page (if exists)
  3. Main app page (dashboard, etc.)
  4. Any page the user recently modified

Key Principle

Trust screenshots, not assumptions. CSS changes cascade unpredictably. A "small tweak" in one component can break layouts across the entire app. Always verify visually.

Frequently asked questions

What does the Visual Diff AI skill do?

Visual Regression Testing — screenshot comparison before and after changes. Use when user wants to check for visual regressions, compare UI changes, or verify CSS/layout changes didn't break anything.

Why use Visual Diff on TypingMind?

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

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

Which AI models can use Visual Diff?

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 Diff?

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

Is the Visual Diff AI skill free?

Yes. It is published on GitHub by Houseofmvps 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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