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Group Related Elements

Community
gnurio
group-related-elements

Use proximity and spacing to show relationships

Overview

Publishergnurio
Repositoryrefactoring-ui-plugin
Skill namegroup-related-elements
Stars
453
Forks
16
Bundled files
Instructions only
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 gnurio on GitHub. Read the source before you install it.

Installation

Install the Group Related Elements 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/gnurio/refactoring-ui-plugin/tree/main/skills/10-group-related-elements
  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/gnurio/refactoring-ui-plugin.git /tmp/refactoring-ui-plugin
mkdir -p .claude/skills
cp -r /tmp/refactoring-ui-plugin/skills/10-group-related-elements .claude/skills/group-related-elements
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Group Related Elements 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 Group Related Elements 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 Group Related Elements 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.

Skill: Group Related Elements

Purpose

Use proximity and spacing to visually group related elements together and separate unrelated groups.

Type

Generative + Evaluative

Input

  • Element inventory
  • Relationship mapping (what belongs together)
  • Current spacing between elements

Output

  • Spacing recommendations to show relationships
  • Pass/Fail assessment of current grouping

Transformation Performed

Adjusts spacing so elements within a group are closer together than elements in different groups, using the Gestalt principle of proximity.

Decision Criteria

PASS (Good Grouping)

  • Related elements have small gap (8-16px)
  • Unrelated groups have larger gap (24-48px)
  • Form labels close to their inputs (4-8px)
  • Sections clearly separated
  • Hierarchy of space: tightest within component, looser between components, loosest between sections

FAIL (Poor Grouping)

  • Uniform spacing everywhere
  • Labels far from inputs
  • Related elements visually disconnected
  • Unrelated elements too close
  • Using borders instead of space to group

The Proximity Principle

Elements that are close together are perceived as more related than elements that are farther apart.

Spacing Hierarchy

Within component (label + input): 4-8px
Between related components: 16-24px
Between sections: 32-64px
Between major page areas: 64-96px

Common Grouping Patterns

PatternStructureSpacing
Form fieldLabel → Input4-8px between, 16-24px after
CardHeader → Content → Footer16-24px internal padding
List itemsItem 1 / Item 2 / Item 38-12px between items
Button groupPrimary + Secondary8-12px between
SectionsSection A (gap) Section B48-64px between

Common Failure Modes

FailureDescriptionFix
The Great DivideEqual spacing everywhereTighten within groups, expand between groups
Label DriftLabels 20px+ from inputsReduce to 4-8px
Section SmushSections barely separatedAdd 48px+ between major sections
Border DependencyBoxes around everythingUse space instead of borders
Card ClumpCards touching each otherAdd 16-24px gap between cards

Prerequisites

  • Spacing system defined
  • Content grouped logically

Dependencies

  • apply-consistent-spacing (uses spacing scale)

Refactoring UI References

  • "Group related information"
  • "Use whitespace to create relationships"
  • "Proximity beats similarity"

Example Assessment

Input: Form with: Title (24px margin), Label (8px margin), Input, Label (8px), Input, Section header (16px margin), Label (8px), Input

Evaluation: PARTIAL

  • Label to input spacing not specified
  • Section header too close to previous section
  • Consistent 8px but not creating hierarchy

Recommendation:

  • Label to input: 4px (tight coupling)
  • After input group: 24px
  • Section header margin-top: 48px (clear break)
  • Within section: 16px between fields

Frequently asked questions

What does the Group Related Elements AI skill do?

Use proximity and spacing to show relationships

Why use Group Related Elements on TypingMind?

Because you install it once and use it with any model. Group Related Elements 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 Group Related Elements in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gnurio/refactoring-ui-plugin/tree/main/skills/10-group-related-elements. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Group Related Elements?

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 Group Related Elements?

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

Is the Group Related Elements AI skill free?

It is published on GitHub by gnurio. 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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