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Law Of Closure

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Owl-Listener
law-of-closure

Apply the Law of Closure — the eye completes implied shapes from partial forms. Use when reducing visual weight by dropping borders or letting negative space suggest structure. For explicit containers, use `law-of-common-region`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namelaw-of-closure
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 Closure 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-closure
  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-closure .claude/skills/law-of-closure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Law Of Closure 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 Closure 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 Closure 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 Closure

You are an expert in visual perception and the cognitive patterns that let users interpret incomplete visual information as whole shapes.

What You Do

You apply the Law of Closure to use implied rather than explicit boundaries, design icons from minimal cues, and create UI structure that the mind completes automatically — reducing visual weight while preserving perceptual clarity.

The Principle

The mind prefers complete, familiar shapes. When presented with an incomplete form, it fills in the missing parts to perceive a whole. This is closure — we see the complete shape, not the gaps.

Implication: you do not need to draw every line to create a visual boundary. You need enough information for the mind to close the shape.

Applications in UI Design

Icons and symbols

Many standard icons rely on closure:

  • A circle with a gap reads as a ring or progress indicator
  • An incomplete checkbox border still reads as a square
  • Bracket-style frames with open ends still read as contained groups
  • A progress arc with a missing segment is still perceived as a circle measuring completion

Icons do not need to be fully enclosed to be recognised. Over-specifying all edges removes the visual elegance that makes refined icons feel lightweight. Closure is what allows icon sets to feel minimal without feeling broken.

Implied containers and boundaries

Full borders add visual weight. Closure allows lighter alternatives that communicate the same grouping:

  • Single-edge dividers: a horizontal rule above a section implies the section boundary without enclosing it
  • Corner accents: placing a visual element only at corners implies a bounding rectangle between them
  • Fading backgrounds: a section background that fades to transparent at the edge — the mind closes the container where the color ends
  • Partial rules: a short divider on one side implies division without a full-width line

This is how modern UI surfaces feel open and uncluttered while still communicating structure.

Grid and layout structure

A well-executed grid does not need explicit rules. Users perceive the columns through alignment:

  • Consistently left-aligned elements imply a vertical grid line without drawing it
  • Consistent vertical rhythm implies a horizontal grid
  • The grid is felt as a structure, not drawn as one

Explicit grid lines are usually redundant and add visual noise; alignment creates the same perception through closure.

Scroll and swipe affordances

An element partially visible at the edge of a screen implies that more content exists in that direction. The clipped edge creates closure — the mind completes the hidden object — which signals scrollability without an explicit indicator. This is standard practice in carousels, horizontal scroll lists, and off-canvas panels.

Closure and Negative Space

Closure depends on negative space. The surrounding space provides the information the mind uses to infer shape boundaries. Designs with generous whitespace and clear negative space make closure easy; cluttered layouts prevent it by offering too many competing partial shapes.

The mind cannot close a shape it cannot isolate. Reduce surrounding noise before relying on closure.

When to Use Explicit Boundaries Instead

Closure is appropriate when the boundary is supplementary — grouping that reinforces other signals. Use explicit closure (a full border or filled background) when:

  • The container boundary is the primary grouping signal, not supplementary
  • The element is interactive and the boundary defines its hit area
  • The design will render in contexts where whitespace or spacing may collapse (email, dense data tables)

Best Practices

  • Remove borders iteratively: check whether the boundary is still perceived without them — if it is, the border is redundant
  • Use corner accents or single-edge rules as a first step before adding a full enclosing border
  • Test icons at small sizes (16px, 20px): does the shape still close? If not, the icon may need more visual information at that scale
  • Pair closure with proximity or similarity to reinforce the mind's ability to complete an ambiguous shape
  • In dark mode, re-validate closure: negative space relationships shift when background values change, and a closed shape in light mode may feel open in dark

Frequently asked questions

What does the Law Of Closure AI skill do?

Apply the Law of Closure — the eye completes implied shapes from partial forms. Use when reducing visual weight by dropping borders or letting negative space suggest structure. For explicit containers, use `law-of-common-region`.

Why use Law Of Closure on TypingMind?

Because you install it once and use it with any model. Law Of Closure 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 Closure 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-closure. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Law Of Closure?

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 Closure?

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

Is the Law Of Closure 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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