Frontend Review Testing logo

Frontend Review Testing

Community
mizchi
frontend-review-testing

Use when auditing test infrastructure — vitest coverage, playwright configuration, VRT setup, coverage merging. Produces recommendations for Week 2 testing phase. Runs `scripts/audit-coverage.sh`.

Overview

Publishermizchi
Repositoryskills
Skill namefrontend-review-testing
Stars
333
Forks
4
Bundled files
Instructions only
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 mizchi on GitHub. Read the source before you install it.

Installation

Install the Frontend Review Testing 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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/frontend-review-testing .claude/skills/frontend-review-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend Review Testing 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 Frontend Review Testing 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 Frontend Review Testing 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.

Frontend Review — Testing

You are auditing the testing posture of a frontend project. The phase is Week 2: establish vitest + playwright with synthetic coverage, keeping in mind:

  • E2E granularity: initially, one case per Router / server controller branch (skeleton-first, not exhaustive).
  • Unit granularity: component coverage is prioritized.
  • Coverage merge: vitest V8 + playwright V8 combined via monocart-coverage-reports or istanbul-merge.

Procedure

  1. Run scripts/audit-coverage.sh --repo <client-repo>.
  2. Read coverage/coverage-summary.json if it exists.
  3. Inspect:
    • vitest.config.* — is coverage configured? provider v8?
    • playwright.config.* — projects, webServer, sharding
    • tests/, e2e/, __tests__/ — current test count and shape
    • package.json scripts — test, test:coverage, test:e2e

Output

Write <client-repo>/.frontend-review/report/latest/md/testing-review.md with:

  • Current state: vitest configured? playwright configured? how many tests? what coverage %?
  • Gaps: missing config, missing scripts, no coverage merge
  • Recommended PRs (3-5 max): each with title, affected files, expected coverage delta
  • Branch coverage checklist for the router/controller branches that should get the first E2E tests

Component Testing — Testing Library First

For React component tests, prefer @testing-library/react over testing internal implementation:

  • Query by role / label / text (getByRole, getByLabelText, getByText) rather than by class name or component internals.
  • User interactions via @testing-library/user-eventuserEvent.click, userEvent.type — not direct DOM event dispatch.
  • Async assertions via waitFor / findBy* for state updates after async operations.
tsx
// Bad: testing implementation details
const wrapper = render(<LoginForm />);
wrapper.find('button.submit').simulate('click');
expect(wrapper.state('isLoading')).toBe(true);

// Good: testing observable behaviour
render(<LoginForm />);
await userEvent.type(screen.getByLabelText('Email'), 'user@example.com');
await userEvent.click(screen.getByRole('button', { name: /sign in/i }));
expect(await screen.findByText('Welcome')).toBeInTheDocument();

When @testing-library/react is absent from package.json, flag it as a gap and recommend adding it alongside @testing-library/user-event and @testing-library/jest-dom (or @testing-library/vitest-dom).

For atom / store tests, use the library's own test utilities (e.g. Jotai createStore()) rather than rendering a component — keep component tests and state logic tests separate.

In-Source Testing Pattern

For logic-heavy .ts files, co-locating tests in the same file (via if (import.meta.vitest) in Vite projects, or a build-time dead-code strip equivalent) is often preferable to separate test files:

  • AI agents read the source and the spec in one file context, improving generation accuracy.
  • Pure functions stay close to their invariants.
  • Production builds strip the test block via "import.meta.vitest": "undefined" define.

Recommend this pattern when proposing new unit tests for utility/lib files.

Test Failure Triage Protocol

When a test fails, the correct action is not to mechanically rewrite the test to pass. Prompt the human to decide:

  1. Spec changed — the implementation is now correct; update the test.
  2. Implementation bug — the test is correct; fix the implementation.
  3. Test was wrong — the test never matched the intended spec; rewrite the test.

Document this triage in the output report. Flag any existing tests that appear to be "implementation echoes" (testing the exact internal path rather than observable behaviour) as candidates for case 3.

MSW (Mock Service Worker) Principle

When the codebase fetches from external APIs, recommend MSW over jest-style module mocks:

  • Mock only at the network boundary (http.get, http.post handlers).
  • Keep the actual state management / component wiring intact — only the HTTP response is stubbed.
  • Place shared handlers in src/test-utils/handlers.ts or equivalent.

This avoids tests that pass even when the integration contract breaks.

Coverage Guidance

  • Target: 80%+ for pure lib/utility files; 60%+ for UI components.
  • Anti-goal: Do NOT inflate tests to reach 100%. Prefer fewer tests that encode real specs over many tests that only enumerate implementation details.
  • Generated UI components (e.g. shadcn/ui output) are coverage-exempt.

VRT Stability Tips

  • Generate reference snapshots inside a Linux container (same OS as CI) to eliminate font-rendering and antialiasing differences between machines.
  • Inject web fonts globally via a shared fixture rather than per-test — font unavailability causes pixel diff false positives.

Boundaries

  • Do NOT write actual test code — propose structure and counts only.
  • Do NOT run vitest or playwright from this skill; the scripts don't execute tests, only read existing reports.

Reference

  • Checklist: 07-unit-test.md, 08-e2e-playwright.md, 15-vrt.md, 13-kpi-tracking.md
  • Phase: week-2-testing.md, week-3-security-vrt.md

Frequently asked questions

What does the Frontend Review Testing AI skill do?

Use when auditing test infrastructure — vitest coverage, playwright configuration, VRT setup, coverage merging. Produces recommendations for Week 2 testing phase. Runs `scripts/audit-coverage.sh`.

Why use Frontend Review Testing on TypingMind?

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

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

Which AI models can use Frontend Review Testing?

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 Frontend Review Testing?

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

Is the Frontend Review Testing AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇