Pandacss Styling

Overview

Panda CSS is a zero-runtime CSS-in-JS framework with a token system, patterns, recipes, and JSX style props. Generates static CSS at build time with full TypeScript support.

Capabilities

• Zero-runtime CSS generation
• Design token system with semantic tokens
• Patterns for common layout primitives
• Recipes for component variants
• Conditions for responsive and state styles
• JSX style props for inline styling
• Works with React, Vue, Solid, Qwik

When to Use

Trigger phrases:

• "pandacss styling"

• "Panda CSS zero-runtime styling — token system, patterns, recipes, conditions, JS"

• Want type-safe styling with design tokens

• Need component variants without runtime cost

• Building design systems with consistent tokens

• Using JSX style props for rapid development

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 pandacss-styling workflow follows a standard pipeline pattern.

Core flow:

# pandacss-styling primary flow
input = prepare(raw_data)
result = process(input, config={conditions, panda, pandacss, patterns, recipes})
validate(result)
deliver(result)

Error handling:

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

Core Workflow

# pandacss-styling primary flow
input = prepare(raw_data)
result = process(input, config={conditions, panda, pandacss, patterns, recipes})
validate(result)
deliver(result)

Error Handling

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

Configuration

// panda.config.ts
import { defineConfig } from "@pandacss/dev"

export default defineConfig({
  preflight: true,
  include: ["./src/**/*.{ts,tsx}"],
  exclude: [],
  theme: {
    extend: {
      tokens: {
        colors: {
          primary: { value: "#3b82f6" },
          secondary: { value: "#8b5cf6" },
        },
      },
    },
  },
  outdir: "styled-system",
})

Token Usage

import { css } from "styled-system/css"

const styles = css({
  color: "primary",
  bg: "gray.100",
  p: "4",
  borderRadius: "md",
  _hover: { bg: "gray.200" },
})

Recipes (Variants)

// styled-system/recipes/button.ts
import { cva } from "styled-system/css"

export const button = cva({
  base: {
    display: "inline-flex",
    alignItems: "center",
    fontWeight: "semibold",
    borderRadius: "md",
  },
  variants: {
    variant: {
      primary: { bg: "primary", color: "white" },
      outline: { border: "1px solid", borderColor: "primary", color: "primary" },
    },
    size: {
      sm: { px: "3", py: "1.5", fontSize: "sm" },
      md: { px: "4", py: "2" },
      lg: { px: "6", py: "3", fontSize: "lg" },
    },
  },
  defaultVariants: { variant: "primary", size: "md" },
})

JSX Style Props

import { styled } from "styled-system/jsx"

export function Card({ children }) {
  return (
    <styled.div
      p="6"
      bg="white"
      rounded="lg"
      shadow="md"
      _dark={{ bg: "gray.800" }}
    >
      {children}
    </styled.div>
  )
}

Patterns (Layout Primitives)

import { Stack, HStack, Grid } from "styled-system/jsx"

<Stack gap="4">
  <HStack justify="space-between">
    <styled.span>Title</styled.span>
    <styled.button>Action</styled.button>
  </HStack>
  <Grid columns={3} gap="4">{items}</Grid>
</Stack>

Common Patterns

• Conditions: _hover, _focus, _dark, _motionReduce, _sm, _md, _lg
• Semantic tokens: colors.primary → colors.primary.default, colors.primary.subtle
• Pattern library: Stack, HStack, VStack, Grid, Flex, Box, Container
• Recipe composition: cx(button({ variant: "primary" }), extraClass)
• Static extraction: Run panda codegen to generate styled-system types

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