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Visual Qa

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DanielKerridge
visual-qa

AI-powered visual QA testing that walks through an app in the browser, records every action with annotated captions (what was done, what should happen), captures screenshots/GIFs, and sends the evidence to Gemini for automated review. Catches UX misalignments, broken flows, missing states, and edge cases that traditional tests miss. Can use Agent Teams for parallel test coverage. Triggers on: "visual test", "visual qa", "test the app", "qa review", "check the ui", "record a test", "walk through the app", "e2e test", "end to end test", "catch edge cases", "gemini review", "screen test", "ux test", "visual regression".

Overview

PublisherDanielKerridge
Repositoryclaude-code-power-platform-skills
Skill namevisual-qa
Stars
63
Forks
16
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Visual Qa 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/DanielKerridge/claude-code-power-platform-skills.git /tmp/claude-code-power-platform-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-power-platform-skills/visual-qa .claude/skills/visual-qa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Visual Qa 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 Qa 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 Qa 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 QA — AI-Powered Visual Testing Skill

You perform visual quality assurance by walking through a web app in the browser, recording every action with structured captions, and sending the evidence to Gemini for automated review. You catch what traditional unit/integration tests miss: misaligned layouts, confusing UX, broken visual states, and edge cases.

CRITICAL RULES

  1. Take a screenshot BEFORE and AFTER every action. This creates the visual evidence chain.
  2. Log every action with the structured caption format. Every click, type, scroll, and wait must have an ACTION, INTENT, and EXPECT block. Read resources/caption-format.md.
  3. Never skip the edge case checklist. After the happy path, run through edge cases. Read resources/edge-cases.md.
  4. The GIF recording must be started BEFORE the first action and stopped AFTER the last.
  5. Gemini reviews the FULL evidence — screenshots + captions + GIF. Not just one piece.

How It Works

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────────┐
│ 1. Plan the │───▶│ 2. Walk the  │───▶│ 3. Collect   │───▶│ 4. Send to   │
│    test run  │    │    app       │    │    evidence  │    │    Gemini     │
└─────────────┘    └──────────────┘    └─────────────┘    └──────────────┘
  What to test       Click, type,       Screenshots,        AI reviews
  and expect         scroll, wait       GIF, captions       actual vs expected

Workflow

Phase 1 — Plan the Test Run

Before touching the browser, define the test plan:

  1. What app? Get the URL from the user
  2. What flows? List the user journeys to test (e.g., "create a record, edit it, delete it")
  3. What to check? Define expected visual states for each step

Write the test plan as a caption script. Read resources/caption-format.md for the format.

Phase 2 — Execute the Test Run

Use Claude in Chrome tools to walk through the app:

1. Navigate to the app URL (mcp__claude-in-chrome__navigate)
2. Start GIF recording (mcp__claude-in-chrome__gif_creator: start_recording)
3. Take initial screenshot (mcp__claude-in-chrome__computer: screenshot)
4. For each test step:
   a. Log the caption (ACTION, INTENT, EXPECT)
   b. Take a "before" screenshot
   c. Perform the action (click, type, scroll)
   d. Wait for the page to settle
   e. Take an "after" screenshot
   f. Note any discrepancies from expected behavior
5. Stop GIF recording (mcp__claude-in-chrome__gif_creator: stop_recording)
6. Export GIF (mcp__claude-in-chrome__gif_creator: export)

Phase 3 — Edge Case Testing

After the happy path, run through the edge case checklist in resources/edge-cases.md. For each applicable edge case:

  • Attempt the action
  • Screenshot the result
  • Log whether it passed or failed

Phase 4 — Compile Evidence

Gather all evidence into a structured report:

  • The caption script (expected behavior)
  • Screenshots at each step (actual behavior)
  • The GIF recording (full flow)
  • Edge case results

Phase 5 — Gemini Review (Optional)

If the user has a Gemini API key, send the evidence for AI review. Read resources/gemini-review.md for the integration approach.

Gemini analyzes:

  • Visual alignment (are elements properly positioned?)
  • Content accuracy (do labels/values match expectations?)
  • State consistency (do UI states match the action taken?)
  • Accessibility issues (contrast, text size, touch targets)
  • Missing feedback (loading states, error messages, confirmations)

Phase 6 — Report

Present findings in this format:

## Visual QA Report — [App Name]
Date: [date]
Flows Tested: [count]
Edge Cases Checked: [count]

### Results Summary
PASS: [count]    FAIL: [count]    PARTIAL: [count]

### Findings

FINDING #1 [SEVERITY: Critical]
STEP: [which step in the flow]
EXPECTED: [what should have happened]
ACTUAL: [what actually happened]
SCREENSHOT: [reference to screenshot]
RECOMMENDATION: [how to fix]

Agent Team Mode (Optional)

For large apps, spawn a team for parallel test coverage:

RoleAgent NameTests
Happy Path Testerhappy-pathCore user flows, CRUD operations
Edge Case Hunteredge-hunterEmpty states, long text, permissions, error handling
Visual Inspectorvisual-inspectorLayout, alignment, responsive, accessibility

Each agent walks the app independently and produces their own findings. The Lead merges results into a single report.

Read resources/team-testing.md for agent team test orchestration.

Without Claude in Chrome

If the Chrome extension isn't available, the skill can still generate:

  • A structured test plan with the caption format
  • An edge case checklist customized to the app
  • A manual testing script the user can follow

The user would then record their own screen and send the video + captions to Gemini.

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 Visual Qa AI skill do?

AI-powered visual QA testing that walks through an app in the browser, records every action with annotated captions (what was done, what should happen), captures screenshots/GIFs, and sends the evidence to Gemini for automated review. Catches UX misalignments, broken flows, missing states, and edge cases that traditional tests miss. Can use Agent Teams for parallel test coverage. Triggers on: "visual test", "visual qa", "test the app", "qa review", "check the ui", "record a test", "walk through the app", "e2e test", "end to end test", "catch edge cases", "gemini review", "screen test", "ux...

Why use Visual Qa on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DanielKerridge/claude-code-power-platform-skills/tree/master/visual-qa. 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 Visual Qa?

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 Qa?

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

Is the Visual Qa AI skill free?

Yes. It is published on GitHub by DanielKerridge 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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