Daisyui Components

Overview

daisyUI adds component classes to Tailwind CSS — buttons, cards, modals, dropdowns, etc. — without JavaScript. Uses Tailwind's plugin system with theme customization via CSS variables.

Capabilities

• 50+ component classes (btn, card, modal, dropdown, etc.)
• Built-in themes (light, dark, cupcake, corporate, etc.)
• Responsive components with Tailwind breakpoints
• No JavaScript required — pure CSS components
• Theme customization via CSS variables
• Color utilities (primary, secondary, accent, etc.)

When to Use

Trigger phrases:

• "daisyui components"

• "daisyUI component library for Tailwind — themed components, colors, responsive, "

• Want pre-built UI components with Tailwind workflow

• Need themed components without writing custom CSS

• Building prototypes or MVPs quickly

• Want consistent design without a full design system

When NOT to Use

• Task is about content strategy, not creation (use strategy skills)
• Task is about content distribution (use distribution skills)
• You need to analyze content performance (use analytics skills)
• Task is about content moderation (use moderation tools)
• You don't have content guidelines
• Task requires domain expertise (consult experts)

Pseudo Code

The daisyui-components workflow follows a standard pipeline pattern.

Core flow:

# daisyui-components primary flow
input = prepare(raw_data)
result = process(input, config={colors, component, components, daisyui, dark})
validate(result)
deliver(result)

Error handling:

on error:
  log(error_details)
  retry_with_backoff(max=3)
  if still_failing: alert_and_escalate()

Core Workflow

# daisyui-components primary flow
input = prepare(raw_data)
result = process(input, config={colors, component, components, daisyui, dark})
validate(result)
deliver(result)

Error Handling

on error:
  log(error_details)
  retry_with_backoff(max=3)
  if still_failing: alert_and_escalate()

Installation

npm install daisyui

// tailwind.config.js
module.exports = {
  plugins: [require("daisyui")],
  daisyui: {
    themes: ["light", "dark", "corporate"],
    darkTheme: "dark",
  },
}

Component Usage

<!-- Button -->
<button class="btn btn-primary">Click Me</button>
<button class="btn btn-outline btn-secondary">Outline</button>

<!-- Card -->
<div class="card bg-base-100 shadow-xl">
  <figure><img src="/photo.jpg" alt="Photo" /></figure>
  <div class="card-body">
    <h2 class="card-title">Title</h2>
    <p>Content</p>
    <div class="card-actions justify-end">
      <button class="btn btn-primary">Action</button>
    </div>
  </div>
</div>

<!-- Modal -->
<dialog id="my_modal" class="modal">
  <div class="modal-box">
    <h3 class="font-bold text-lg">Hello!</h3>
    <p class="py-4">Modal content</p>
    <div class="modal-action">
      <form method="dialog"><button class="btn">Close</button></form>
    </div>
  </div>
</dialog>

Dark Mode Toggle

<label class="swap swap-rotate">
  <input type="checkbox" data-toggle-theme="dark,light" />
  <div class="swap-on">🌙</div>
  <div class="swap-off">☀️</div>
</label>

Common Patterns

• Color modifiers: btn-primary, btn-secondary, btn-accent, btn-ghost, btn-link
• Size modifiers: btn-xs, btn-sm, btn-md, btn-lg
• State modifiers: btn-active, btn-disabled, loading
• Responsive: btn md:btn-lg — larger on medium screens
• Themes: Apply per-section with data-theme="dark" attribute

How to Use

1. Define content goal (traffic, engagement, conversion, brand awareness)
2. Research target audience pain points and search intent
3. Generate content using appropriate AI tools
4. Edit and humanize output for authenticity
5. Optimize for target platform (SEO, hashtags, format)
6. Schedule and distribute across channels
7. Measure performance and iterate

Red Flags

• AI-generated content sounds robotic: Always run through humanizer before publishing
• Engagement dropping week-over-week: Content fatigue or algorithm change — vary formats
• Duplicate content across platforms: Adapt content per platform, don't just cross-post
• No content calendar: Sporadic posting kills audience retention
• Ignoring analytics: Content without measurement is just publishing, not marketing

Verification

• Skill output matches expected behavior

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table