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Component Library Patterns

Community
vibeeval
component-library-patterns

Design system token management, component API design, Storybook, and visual regression testing patterns

Overview

Publishervibeeval
Repositoryvibecosystem
Skill namecomponent-library-patterns
Stars
533
Forks
44
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 vibeeval on GitHub. Read the source before you install it.

Installation

Install the Component Library Patterns 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/vibeeval/vibecosystem/tree/main/skills/component-library-patterns
  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/vibeeval/vibecosystem.git /tmp/vibecosystem
mkdir -p .claude/skills
cp -r /tmp/vibecosystem/skills/component-library-patterns .claude/skills/component-library-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Component Library Patterns 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 Component Library Patterns 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 Component Library Patterns 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.

Component Library Patterns

Design Tokens

typescript
// tokens.ts - Single source of truth
export const tokens = {
  color: {
    primary: { 50: '#eff6ff', 500: '#3b82f6', 900: '#1e3a5f' },
    semantic: { success: '#22c55e', error: '#ef4444', warning: '#f59e0b' }
  },
  spacing: { xs: '4px', sm: '8px', md: '16px', lg: '24px', xl: '32px' },
  font: {
    size: { sm: '0.875rem', base: '1rem', lg: '1.125rem', xl: '1.25rem' },
    weight: { normal: 400, medium: 500, semibold: 600, bold: 700 }
  },
  radius: { sm: '4px', md: '8px', lg: '12px', full: '9999px' },
  shadow: {
    sm: '0 1px 2px rgba(0,0,0,0.05)',
    md: '0 4px 6px rgba(0,0,0,0.1)'
  }
} as const

Component API Design

typescript
// Composition pattern > prop explosion
// YANLIS:
<Button icon="check" iconPosition="left" loading loadingText="..." />

// DOGRU:
<Button>
  <Button.Icon><CheckIcon /></Button.Icon>
  <Button.Text>Save</Button.Text>
  <Button.Spinner />
</Button>

// Polymorphic component
interface ButtonProps<T extends ElementType = 'button'> {
  as?: T
  variant: 'primary' | 'secondary' | 'ghost'
  size: 'sm' | 'md' | 'lg'
}

Storybook Patterns

typescript
// Component.stories.tsx
const meta: Meta<typeof Button> = {
  component: Button,
  argTypes: {
    variant: { control: 'select', options: ['primary', 'secondary'] },
    size: { control: 'radio', options: ['sm', 'md', 'lg'] }
  }
}

export const Playground: Story = { args: { variant: 'primary', children: 'Click' } }
export const AllVariants: Story = {
  render: () => (
    <div style={{ display: 'flex', gap: 8 }}>
      {(['primary', 'secondary', 'ghost'] as const).map(v => (
        <Button key={v} variant={v}>{v}</Button>
      ))}
    </div>
  )
}

Visual Regression Testing

typescript
// Chromatic veya Percy ile
test('Button variants match snapshot', async ({ page }) => {
  await page.goto('/iframe.html?id=button--all-variants')
  await expect(page).toHaveScreenshot('button-variants.png', {
    maxDiffPixelRatio: 0.01
  })
})

Theming

css
/* CSS Custom Properties */
:root {
  --color-primary: #3b82f6;
  --color-bg: #ffffff;
  --color-text: #1f2937;
}

[data-theme="dark"] {
  --color-primary: #60a5fa;
  --color-bg: #111827;
  --color-text: #f9fafb;
}

Checklist

  • Design tokens single source of truth
  • Component API composition-based (slot pattern)
  • Her component'te Storybook story var
  • Visual regression test aktif
  • Accessibility: keyboard nav + ARIA + contrast
  • Theming: CSS variables ile dark/light
  • Semantic versioning + CHANGELOG
  • Prop documentation (JSDoc veya MDX)
  • Bundle size per-component tracked

Anti-Patterns

  • Prop explosion (10+ prop → composition kullan)
  • Token'sız hardcoded değerler
  • Component'te global CSS
  • Story'siz component
  • Theme switch'te flash (FOUC)
  • Circular dependency between components

Frequently asked questions

What does the Component Library Patterns AI skill do?

Design system token management, component API design, Storybook, and visual regression testing patterns

Why use Component Library Patterns on TypingMind?

Because you install it once and use it with any model. Component Library Patterns 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 Component Library Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vibeeval/vibecosystem/tree/main/skills/component-library-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Component Library Patterns?

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 Component Library Patterns?

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

Is the Component Library Patterns AI skill free?

Yes. It is published on GitHub by vibeeval 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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