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Figure Ground Cues

Community
Deibler
figure-ground-cues

Apply specific visual cues to control figure-ground perception — shadow, elevation, scale, contrast, saturation, opacity, and texture. Use when designing layered interfaces, focus states, hover treatments, or any context where one element should clearly stand forward against others. Each cue contributes a portion of the figure-ground signal; combining cues strengthens the effect. The skill is selecting and calibrating cues for the desired level of separation.

Overview

PublisherDeibler
Repositoryuniversal-design-principles
Skill namefigure-ground-cues
Stars
9
Forks
3
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Deibler on GitHub. Read the source before you install it.

Installation

Install the Figure Ground Cues 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/Deibler/universal-design-principles/tree/main/plugins/perception-and-hierarchy-principles/skills/figure-ground-cues
  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/Deibler/universal-design-principles.git /tmp/universal-design-principles
mkdir -p .claude/skills
cp -r /tmp/universal-design-principles/plugins/perception-and-hierarchy-principles/skills/figure-ground-cues .claude/skills/figure-ground-cues
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figure Ground Cues 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 Figure Ground Cues 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 Figure Ground Cues 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.

Figure-Ground — visual cues

The figure-ground sorting that the user's perception performs depends on specific visual cues. As a designer, you choose which cues to use and how strongly to apply them, calibrated to the desired separation between figure and ground.

The major cues, with notes on their relative strength:

Shadow / elevation

Strongest single cue for figure-ground in modern UI. A drop shadow under an element makes it appear closer to the viewer (figure), with the rest of the layout receding. Multiple levels of shadow create multiple levels of elevation.

Common implementations:

  • Cards with subtle shadow (2–4px blur) for moderate elevation.
  • Floating action buttons with stronger shadow (8–12px blur) for higher elevation.
  • Modals with very prominent shadow + scrim for highest elevation.

Material Design formalized this into a system of "elevation levels" (0, 1, 2, 4, 8, 16, 24 dp) each with a specific shadow recipe. The system makes consistent figure-ground separation across a product easier to maintain.

Scrim / overlay

A semi-transparent layer over the ground that makes the ground visibly recede. Most commonly used with modals: the modal sits above the scrim, the scrim sits above the page.

Calibration:

  • Light scrim (10–20% opacity) for soft separation.
  • Medium scrim (40–60% opacity) for clear modal-style separation.
  • Dark scrim (70%+ opacity) for full focus on the figure (image lightboxes, video players).

Scale

Larger elements tend to come forward as figure, especially when they're surrounded by smaller elements. A hero card among supporting cards reads as figure through scale.

But this can flip: a tiny element on a vast background can also read as figure (the small floating action button on a large canvas is figure despite its size). The cue depends on context.

Contrast and saturation

Higher contrast and more saturated colors come forward; lower contrast and muted colors recede. The active workspace at full saturation; inactive workspaces with muted colors.

This cue is subtle — it's often used in combination with others rather than alone. But it's powerful for distinguishing active from inactive content.

Opacity

Semi-transparent elements recede; fully opaque elements come forward. Reducing the opacity of inactive content (50–70% opacity) is a quick way to send it to the background.

Be careful not to reduce opacity to the point of illegibility. Even "ground" content should usually remain readable.

Sharpness vs. blur

Sharp, in-focus elements come forward; blurred elements recede. iOS uses background blur extensively — a modal's background is blurred, signaling that it's behind the modal.

Blur is a strong cue but expensive computationally. Use deliberately, not casually.

Texture and detail

More detailed elements come forward; smoother, less detailed elements recede. A photographic background with text overlaid feels different than a solid background with text — the texture of the photo competes with the text for figure status.

Outlines and borders

A clear, visible border around an element helps it read as a closed figure. Borderless elements may merge into their background.

But heavy borders can feel "heavy" or dated. Modern design often prefers shadow over border for figure separation.

Position in z-axis (layering)

Elements layered on top of others occlude them, which is a strong figure cue. The element on top is figure; the element behind is ground.

Implementation in CSS: z-index controls the layering order. In design systems: explicit layering levels (cards, popovers, modals, toasts each at their own z-axis level).

Combining cues

The cues compound. A modal that uses shadow AND scrim AND opacity reduction on the background is more clearly figure than a modal using only one of these.

For elements that need to clearly come forward (modals, popovers, focus states), combine cues. For elements that should subtly come forward (hover states, mild emphasis), use a single cue or a combination of subtle ones.

Avoid combining too many cues at high intensity — the result can feel theatrical. Match the cue strength to the actual importance of the figure.

Worked examples

A modal with full figure-ground

A modal opens. Cues applied:

  • Scrim: 50% opacity black overlay between modal and page.
  • Modal elevation: prominent shadow (16px blur, 40% opacity).
  • Modal background: pure white (sharp contrast against the dark scrim).
  • Page beneath: visible through scrim but clearly receding.

The user's perception is unambiguous: the modal is figure, the page is ground. Attention naturally goes to the modal.

A card hover with subtle figure-ground

A card in a grid is hovered. Cues applied:

  • Shadow increases (from 2px blur to 4px blur).
  • Slight scale increase (1.02×).
  • Subtle background shift (0.5% lighter).

The card subtly comes forward. Other cards remain at baseline. The user gets gentle feedback about where their attention is, without dramatic visual change.

Active workspace differentiated from inactive

In a multi-workspace app, the active workspace is rendered with full saturation; inactive workspaces are at 60% opacity with muted colors. The active workspace is clearly figure.

When the user switches workspaces, the cues swap: the new active workspace becomes figure, the previously active workspace becomes ground.

Background photo competing with text

A hero section uses a photo background with text overlaid. Without intervention, the photo's detail and color compete with the text — figure-ground is ambiguous.

Add a dark gradient over the photo (figure-ground cue: scrim) to push the photo further into the background. The text now clearly reads as figure.

A card with too many cues

A card has: a thick border, a heavy drop shadow, a colored background, a glow effect, AND scaling on hover. The card screams. It feels theatrical, almost cartoonish.

The fix: reduce to one or two strong cues. A subtle shadow + a clean white background + a slight border on hover is enough. The figure-ground separation works without the theatrics.

Anti-patterns

Insufficient cues. A modal that uses only a thin border and no shadow or scrim. The figure-ground separation is too weak; the modal blurs into the page.

Mixed cues that fight. A modal with shadow (suggests it's in front) but with reduced opacity (suggests it's receding). Users get conflicting signals.

Overdone cues. Heavy shadows on every card; high-contrast borders everywhere; aggressive scale changes on hover. The cues lose meaning when applied universally.

Inconsistent cues across surfaces. Modals styled with shadow + scrim in one place; modals styled with just a border in another. Users can't form expectations about modality.

Cues that hurt accessibility. Reducing opacity so low that text becomes hard to read; using color-only cues that fail for color-blind users.

Reversed cues. Chrome (sidebar, header) styled with stronger figure cues than the content. Users look at the chrome instead of focusing on their work.

Heuristic checklist

When designing figure-ground separation, ask: What should be figure, and what should be ground? Be deliberate. Which cues am I using to communicate the sorting? List them. Are the cues consistent within the product? Inconsistent cues confuse users. Is the separation strong enough? Test by glancing at the design — what comes forward? Is the separation too aggressive? Theatrical cues feel wrong; calibrate to the actual importance.

Related sub-skills

  • figure-ground-relationship — parent principle on figure-ground perception.
  • figure-ground-overlays-and-modals — sibling skill on the most common figure-ground problem.
  • hierarchy — figure-ground is a primary mechanism for hierarchy.
  • signal-to-noise — figure should be high-signal; ground low-noise.
  • color — color choice is one figure-ground cue.

See also

  • references/elevation-systems.md — practical guidance on building consistent elevation/figure-ground systems across a product.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Figure Ground Cues AI skill do?

Apply specific visual cues to control figure-ground perception — shadow, elevation, scale, contrast, saturation, opacity, and texture. Use when designing layered interfaces, focus states, hover treatments, or any context where one element should clearly stand forward against others. Each cue contributes a portion of the figure-ground signal; combining cues strengthens the effect. The skill is selecting and calibrating cues for the desired level of separation.

Why use Figure Ground Cues on TypingMind?

Because you install it once and use it with any model. Figure Ground Cues 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 Figure Ground Cues in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Deibler/universal-design-principles/tree/main/plugins/perception-and-hierarchy-principles/skills/figure-ground-cues. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Figure Ground Cues?

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 Figure Ground Cues?

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

Is the Figure Ground Cues AI skill free?

It is published on GitHub by Deibler. 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.

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