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Information Architecture

Community
RealTask
information-architecture

Plan and improve navigation, information hierarchy, page structure, findability, and task flows around real user goals.

Overview

PublisherRealTask
RepositoryMAX-DESIGN
Skill nameinformation-architecture
Stars
4
Forks
0
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 RealTask 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/RealTask/MAX-DESIGN/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/RealTask/MAX-DESIGN.git /tmp/MAX-DESIGN
mkdir -p .claude/skills
cp -r /tmp/MAX-DESIGN/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

When to use

Use when creating a new product structure, reorganizing a website or app, designing navigation, improving discoverability, reducing cognitive load, planning search and filters, or reviewing a multi-step task. For visual polish alone, use frontend-design or design-review instead.

Workflow

1. Start from user jobs

Identify the main audiences, their top tasks, the vocabulary they use, the information needed for each task, and what a successful outcome looks like. Do not assume the company org chart is the right navigation model.

2. Inventory before reorganizing

Inspect routes, pages, sidebars, tabs, labels, breadcrumbs, search, filters, permissions, and existing analytics or feedback when available. Record current destinations and dependencies. Preserve deep links and familiar paths unless there is evidence to change them.

3. Build a simple content map

Group content by user intent and meaningful relationships. Distinguish global navigation, local navigation, in-page sections, actions, and utility links. Avoid duplicating destinations across several groups unless context genuinely helps.

4. Choose labels users can predict

Prefer familiar, concrete terms over internal team names, vague abstractions, or clever but ambiguous wording. Use one label for one concept consistently. Pair icons with text when meaning would otherwise be unclear; do not rely on icons alone for unfamiliar actions.

5. Optimize the primary task path

Map the shortest safe path from entry to completion. Remove unnecessary decisions, but retain explanations and confirmations that protect users from costly or irreversible mistakes. Use progressive disclosure when advanced options are useful but not needed by everyone.

6. Design discovery and recovery

For search, filters, categories, and lists, define no-query, results, no-results, loading, error, cleared-filter, and pagination states. Give users enough context to understand where they are and a clear way to recover when an assumption fails.

7. Validate across real conditions

Check navigation labels, long names, permissions, direct URLs, narrow screens, keyboard use, and likely first-time-user questions. Use tree tests, card sorting, analytics, or user testing where practical; mark hypotheses when no user evidence exists.

Deliverables

Produce a compact sitemap or content map, primary task paths, label decisions, open questions, and acceptance checks. Separate observed behavior from proposed changes.

Quality gates

  • Navigation reflects user tasks rather than internal org structure.
  • Labels are clear, consistent, and appropriately specific.
  • Primary tasks have a visible, predictable path.
  • Search, filters, empty results, permissions, and recovery are considered.
  • Existing deep links and useful conventions are preserved or migrated deliberately.
  • Assumptions and missing user evidence are disclosed.

Frequently asked questions

What does the Information Architecture AI skill do?

Plan and improve navigation, information hierarchy, page structure, findability, and task flows around real user goals.

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/RealTask/MAX-DESIGN/tree/main/skills/information-architecture. TypingMind reads its SKILL.md 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 RealTask 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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