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Law Of Figure Ground

CommunityPopular
Owl-Listener
law-of-figure-ground

Apply the Law of Figure-Ground — establish which layer is foreground and actionable versus background. Use when designing modals, overlays, and depth. For emphasising one element among peers, use `von-restorff-effect`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namelaw-of-figure-ground
Stars
3.1K
Forks
422
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 Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Law Of Figure Ground 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/Owl-Listener/designer-skills/tree/main/ui-design/skills/law-of-figure-ground
  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/Owl-Listener/designer-skills.git /tmp/designer-skills
mkdir -p .claude/skills
cp -r /tmp/designer-skills/ui-design/skills/law-of-figure-ground .claude/skills/law-of-figure-ground
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Law Of Figure Ground 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 Law Of Figure Ground 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 Law Of Figure Ground 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.

Law of Figure-Ground

You are an expert in visual attention and the perceptual hierarchy of UI surfaces.

What You Do

You apply the Law of Figure-Ground to ensure users can instantly identify what is foreground (the content or action) and what is background (the context or surface), and to control this relationship deliberately at every layer of the interface.

The Principle

The mind automatically separates visual fields into a subject (the figure) and a context (the ground). Figure is perceived as being in front, bounded, and the focus of attention. Ground is perceived as behind, unbounded, and receding.

This parsing is not a choice — it is a perceptual reflex. Every UI surface triggers figure-ground separation. The question is whether you designed it deliberately or left it to chance.

Characteristics of Figure vs. Ground

Figure (foreground)Ground (background)
Appears in frontAppears behind
Bounded — perceived as having edgesUnbounded — perceived as extending beyond the figure
Focus of attentionContext for attention
Higher contrast, richer texture or detailLower contrast, flatter, more uniform
Typically smaller areaTypically larger area

Establishing Clear Figure-Ground in UI

Elevation and shadow

Elevation is the primary tool for figure-ground in layered design systems. A card elevated above a page surface is figure; the page is ground. The shadow signals depth, and depth signals foreground. Dropdowns, sheets, modals, and tooltips must appear above the surface they are called from — depth signals primacy.

Overlays and scrims

A modal requires the background to recede. A scrim — a semi-transparent dark overlay — reduces the ground's visual presence so the modal can be unambiguous figure. Without a scrim, figure-ground is unclear and attention is split between the modal and the page beneath it.

Contrast

High-contrast elements are perceived as figure; low-contrast elements as ground. Text on a surface works through figure-ground: the text is figure (high contrast, bounded by its line), the surface is ground (lower contrast, unbounded). When text and background share too similar a luminance value, figure-ground collapses and the text is no longer legible.

Active and selected states

In navigation or lists, the selected item becomes figure; unselected items become ground. The selected state — a background fill, a bold type treatment, a color change — must make the figure-ground shift unambiguous. If the selected and unselected states are too similar, users cannot tell which item is active.

Ambiguous Figure-Ground

Ambiguous figure-ground occurs when the same element can be read as either figure or ground — the visual equivalent of the Rubin vase. In fine art and illustration this is sometimes intentional. In UI, it is almost always a failure.

If users cannot immediately parse what is content and what is surface, they cannot act with confidence.

Common Figure-Ground Failures

  • Insufficient scrim: a modal on a white page without a scrim requires users to parse figure-ground from edges alone — always provide a background-dimming layer
  • Nested elevation without contrast: cards inside cards without clear luminance difference between levels produce ambiguous depth
  • Text on photography: the image competes as figure; separate text from images with a color overlay, blur, or gradient layer
  • Flat design without surface differentiation: removing elevation signals entirely makes foreground/background relationships invisible — some depth signal is necessary

Dark Mode Considerations

Light and dark modes invert the typical luminance relationship between figure and ground. What was dark figure on a light ground becomes light figure on a dark ground. Shadows that read as elevation in light mode may become invisible in dark mode — use subtle light-colored borders or reduced-luminance fills to maintain figure-ground clarity when shadows disappear.

Best Practices

  • Define your surface stack in design tokens: base, raised, overlay, modal — each level should have a clear and consistent contrast relationship to the layers below it
  • Never place text directly on photography or complex imagery without a separation layer
  • Test figure-ground by removing color: can you still identify foreground from background using only shape and contrast?
  • Use elevation sparingly — the more surface layers you stack, the harder it becomes to maintain an unambiguous hierarchy
  • Validate scrims in both themes: a scrim that works in light mode may need adjusting in dark mode where the base surface is already dark

Frequently asked questions

What does the Law Of Figure Ground AI skill do?

Apply the Law of Figure-Ground — establish which layer is foreground and actionable versus background. Use when designing modals, overlays, and depth. For emphasising one element among peers, use `von-restorff-effect`.

Why use Law Of Figure Ground on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/designer-skills/tree/main/ui-design/skills/law-of-figure-ground. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Law Of Figure Ground?

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 Law Of Figure Ground?

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

Is the Law Of Figure Ground AI skill free?

Yes. It is published on GitHub by Owl-Listener 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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