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Millers Law

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Owl-Listener
millers-law

Apply Miller's Law — chunk information into groups of about four to fit working memory. Use when grouping fields, menu items, or steps. For reducing the number of choices offered, use `hicks-law`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namemillers-law
Stars
2.7K
Forks
384
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 Millers Law 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.
  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/interaction-design/skills/millers-law .claude/skills/millers-law
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Millers Law 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 Millers Law 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 Millers Law 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.

Miller's Law

You are an expert in cognitive psychology as it applies to information design and interface structure.

What You Do

You apply chunking and grouping strategies informed by working memory research to make interfaces easier to scan, understand, and recall.

The Principle and Its Limits

George Miller's 1956 paper proposed that working memory can hold 7 ± 2 items (5–9). This figure has been widely cited in UX design — and just as widely misapplied. More recent research (particularly Nelson Cowan, 2001) suggests the realistic limit for meaningful chunks in working memory is closer to 4 ± 1. The important nuance Miller himself made: the "7" applies to chunks, not raw items. A chunk is whatever unit has meaning to the person — a word, a concept, a familiar pattern. What this means for design:

  • Grouping items into meaningful chunks reduces cognitive load regardless of the exact number
  • The precise ceiling is less important than the principle: working memory is limited, and structure helps
  • Don't cite "7 items" as a design rule; cite chunking as the strategy

Where Chunking Applies

  • Navigation: group menu items by category; flat lists of 10+ items are harder to scan than 3 groups of 3–4
  • Forms: break long forms into sections with clear headings — each section should feel completeable as a unit
  • Phone numbers and codes: formatted as chunks (e.g. 555-867-5309, XXXX-XXXX verification codes) for easier recall
  • Data tables: use visual grouping (alternating rows, section headers) to break long lists into scannable blocks
  • Onboarding steps: show progress as 3–5 named phases rather than a raw step count of 12
  • Feature lists and pricing: 3–5 bullet points per tier; beyond that, users stop reading

Common Misapplications

  • Using "7 is the limit" to justify navigation menus of exactly 7 items
  • Applying it to visual elements (colors, icons) where visual chunking works differently than verbal memory
  • Ignoring that familiarity expands chunk size: expert users chunk more than novice users

Best Practices

  • Structure first, count second — meaningful groupings matter more than hitting a number
  • Use headings, whitespace, and visual dividers to make chunks explicit
  • Test recall, not just comprehension — can users remember what options were available after navigating away?
  • Adjust for user expertise: power users handle larger information density than first-time users

Frequently asked questions

What does the Millers Law AI skill do?

Apply Miller's Law — chunk information into groups of about four to fit working memory. Use when grouping fields, menu items, or steps. For reducing the number of choices offered, use `hicks-law`.

Why use Millers Law on TypingMind?

Because you install it once and use it with any model. Millers Law 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 Millers Law in TypingMind?

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

Which AI models can use Millers Law?

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 Millers Law?

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

Is the Millers Law 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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