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Color Palette Generator from Brand Identity

Generate a full color system from one brand color: semantic colors, tint and shade scales, dark-mode variants, and accessibility-checked pairings.

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Your Prompt
prompt.txt
Generate a comprehensive color system for Vortex starting from the primary brand color #6366F1 (Indigo 500). The brand operates in the developer tools and API management space and targets software engineers and technical leads. Build the complete palette: 1) Create the primary color scale — generate 11 (50 through 950) shades from 50 (lightest) to 950 (darkest) using perceptually uniform steps. For each step, provide the hex value, HSL values, and describe its intended use (50 for backgrounds, 500 for buttons, 900 for text). 2) Derive a complementary secondary color using split-complementary color harmony theory — generate its full scale and explain why this pairing works for the brand personality (innovative, trustworthy, and energetic). 3) Create 4 accent colors for specific purposes: success (green spectrum), warning (amber spectrum), error (red spectrum), and info (blue spectrum) — each with 3 variants (light background, default, dark text). 4) Design neutral/gray scale with cool (slight blue undertone) undertone (warm, cool, or true neutral) — 10 steps from white to near-black, ensuring the grays harmonize with the primary color rather than appearing disconnected. 5) Validate all text/background combinations against WCAG AA (AAA preferred for body text) standards — create a contrast matrix showing which color combinations pass for normal text (4.5:1), large text (3:1), and UI components (3:1). 6) Generate dark mode variants by defining surface colors (3 elevation levels), adjusting primary/secondary saturation and lightness for dark backgrounds, and ensuring the same semantic meaning is preserved. 7) Export the palette as CSS custom properties, Tailwind config, Figma tokens JSON, and Swift UIColor with design tokens using the naming convention: --color-{role}-{shade} (e.g., --color-primary-500).

What this prompt does

This prompt generates a complete color system derived from a single brand color, with semantic colors, tint and shade scales, dark-mode variants, and accessibility-checked pairings. You provide [brand_name], the [primary_color], the [industry], and the [target_audience]. ChatGPT then builds the primary scale, a complementary secondary, semantic accent colors, a neutral gray scale, a WCAG contrast matrix, dark-mode variants, and exportable design tokens.

The variables shape the system. [scale_steps] sets how many shades each color gets (50 through 950), [color_harmony] decides how the secondary is derived, and [accent_count] controls the semantic colors for success, warning, error, and info. [neutral_tone] gives the grays a warm, cool, or true-neutral undertone so they harmonize with the primary, [wcag_level] sets the contrast standard for the validation matrix, and [export_formats] determines the token output (CSS variables, Tailwind config, Figma tokens, SwiftUI). Because every step ties back to the one [primary_color], the system stays coherent rather than feeling assembled from unrelated swatches.

When to use it

  • You have one brand color and need a full, consistent palette built around it.
  • You are theming an app or dashboard and want tint/shade scales as design tokens.
  • You need semantic colors (success, warning, error, info) that fit the brand rather than default reds and greens.
  • You want a neutral gray scale that harmonizes with the primary instead of looking disconnected.
  • You need a WCAG contrast matrix showing which text/background pairings pass.
  • You want dark-mode variants derived systematically rather than guessed.

Example output

The output is a complete color-system specification. It gives the primary scale across [scale_steps] steps with hex, HSL, and intended use per step, a secondary color derived via [color_harmony] with its own scale, [accent_count] semantic colors each in three variants, a neutral scale with a [neutral_tone] undertone, a contrast matrix validating pairings against [wcag_level], dark-mode surface and adjusted colors, and design tokens exported in your [export_formats] using a --color-{role}-{shade} naming convention. It is ready to paste into a theme config.

Pro tips

  • Give [primary_color] as a precise hex value so the entire scale is derived accurately from your real brand color.
  • Choose [neutral_tone] deliberately — a cool gray under a warm primary can look off, so match the undertone to the brand.
  • Set [wcag_level] to AA at minimum and aim for AAA on body text, then verify the matrix with a real contrast checker.
  • Pick [export_formats] that match your stack so the tokens drop straight into your Tailwind config or CSS without manual conversion.
  • Treat the generated hex values as a strong starting point, but eyeball the scale in context, since perceptual uniformity can still need small hand-tuning.
  • Re-run with a different [color_harmony] if the secondary feels wrong; split-complementary and triadic produce noticeably different brand moods.

Frequently Asked Questions

Does this prompt guarantee accessible color combinations?
It builds a contrast matrix validating pairings against the `[wcag_level]` you set, but the values are calculated from the generated hex codes. You should re-check the final colors with a real contrast tool before shipping, since small adjustments can change whether a pairing passes.
Can I export the palette as design tokens?
Yes, the `[export_formats]` variable lets you output CSS custom properties, a Tailwind config, Figma tokens JSON, or SwiftUI colors using a `--color-{role}-{shade}` naming convention. Pick the formats matching your stack so the tokens drop in without manual conversion.
How does it derive the secondary color?
It uses the `[color_harmony]` method you specify, such as split-complementary or triadic, to derive a complementary secondary and generates its full scale. Different harmony methods produce noticeably different brand moods, so try a few if the first secondary feels off.
Will the generated shades be perfectly uniform?
The prompt aims for perceptually uniform steps, but generated hex values can still need small hand-tuning in real contexts. Treat the scale as a strong, coherent starting point and eyeball it against your UI rather than assuming every step is pixel-perfect.
Engr Mejba Ahmed

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Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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