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Information Architecture

Organization
memi-design
information-architecture

Organize product content, objects, navigation, search, filters, labels, and hierarchy around user mental models and retrieval tasks. Use when people cannot find, understand, or predict where information and actions belong.

Overview

Publishermemi-design
Repositorydesign-skills
Skill nameinformation-architecture
Stars
7
Forks
0
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by memi-design on GitHub. Read the source before you install it.

Installation

Install the Information Architecture 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/memi-design/design-skills/tree/main/skills/information-architecture
  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/memi-design/design-skills.git /tmp/design-skills
mkdir -p .claude/skills
cp -r /tmp/design-skills/skills/information-architecture .claude/skills/information-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Information Architecture 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 Information Architecture 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 Information Architecture 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.

Information Architecture

Create a structure that makes location, meaning, and movement predictable. Do not use visual hierarchy to conceal an unresolved taxonomy.

Inputs

Collect representative content and objects, top user tasks, entry points, current navigation, search behavior, terminology, permissions, scale expectations, and analytics or research evidence. Sample real long-tail content, not only ideal examples.

Workflow

  1. Inventory the domain. List content types, objects, attributes, relationships, owners, lifecycle, and access rules. Flag duplicates and orphaned material.
  2. Prioritize retrieval tasks. Identify what users need to find, compare, understand, or act on, including frequency and consequence.
  3. Elicit mental models. Use interviews, search queries, support language, card sorting, and observed behavior. Separate internal organization language from user language.
  4. Define the taxonomy. Create mutually understandable categories and facets. State inclusion rules, exclusions, synonyms, and governance ownership.
  5. Choose navigation patterns. Match global, local, contextual, utility, and sequential navigation to task relationships. Avoid using nesting as the default solution.
  6. Design labels. Prefer specific, familiar, parallel labels. Test ambiguous terms in context and provide disambiguation where categories overlap.
  7. Design retrieval. Decide when browsing, search, filtering, sorting, saved views, or recommendations are appropriate. Define searchable fields and useful zero-result recovery.
  8. Model growth and permissions. Test deep content, large counts, role differences, localization, archived states, and future object types.
  9. Validate. Use tree testing for findability, card sorting for grouping, first-click tests for orientation, and task-based usability tests for the complete experience.

Output contract

Return:

  • domain inventory and object relationships;
  • prioritized retrieval tasks;
  • taxonomy with category rules and synonyms;
  • navigation map;
  • search, filter, and sorting model;
  • labeling decisions;
  • validation plan and governance owner.

Verification

Test whether a new user can predict where an item belongs, whether one item has an intentional home, whether labels remain meaningful out of context, and whether permissions or scale create dead ends. Record contested classifications instead of forcing false consensus.

Use content-design for terminology systems and interaction-design for the behavior of navigation, filtering, and search controls.

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 Information Architecture AI skill do?

Organize product content, objects, navigation, search, filters, labels, and hierarchy around user mental models and retrieval tasks. Use when people cannot find, understand, or predict where information and actions belong.

Why use Information Architecture on TypingMind?

Because you install it once and use it with any model. Information Architecture 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 Information Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/memi-design/design-skills/tree/main/skills/information-architecture. 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 Information Architecture?

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 Information Architecture?

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

Is the Information Architecture AI skill free?

Yes. It is published on GitHub by memi-design 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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