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Heuristic Evaluation

Community
mshadmanrahman
heuristic-evaluation

Audit a design against Nielsen's ten usability heuristics, tying every finding to a specific element with a specific fix. Use on a built screen or a detailed mockup.

Overview

Publishermshadmanrahman
Repositoryclaude-for-designers
Skill nameheuristic-evaluation
Stars
40
Forks
10
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 mshadmanrahman on GitHub. Read the source before you install it.

Installation

Install the Heuristic Evaluation 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/mshadmanrahman/claude-for-designers/tree/main/.claude/skills/heuristic-evaluation
  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/mshadmanrahman/claude-for-designers.git /tmp/claude-for-designers
mkdir -p .claude/skills
cp -r /tmp/claude-for-designers/.claude/skills/heuristic-evaluation .claude/skills/heuristic-evaluation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Heuristic Evaluation 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 Heuristic Evaluation 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 Heuristic Evaluation 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.

You are running a structured usability audit. Do not produce a general list of suggestions. Every finding must reference something specific and visible in the design the student has shared.

Ask the user to provide: (1) the design output (HTML file, Figma link, or screenshot), and (2) the target user and their primary task. If either is missing, ask for it before starting.

Review against Nielsen's 10 usability heuristics. For each heuristic, give:

  • Rating: Pass / Needs work / Fail
  • Evidence: one specific example from this design. Name the element, the screen, the exact behaviour
  • Fix: one concrete recommendation (not "improve feedback", but "add a spinner to the Submit button when the form is processing so the user knows the action was received")

1. Visibility of system status Does the interface always tell the user what is happening? Are loading states, success states, and error states communicated in real time?

2. Match between system and real world Does the language match what the user already knows? Are icons and metaphors familiar? Does the flow follow conventions the user has encountered elsewhere?

3. User control and freedom Can the user undo, go back, or exit without being punished? Are there clear escape routes from every state?

4. Consistency and standards Do similar elements behave the same way across the product? Does the design follow platform conventions (mobile tap targets, web link underlines, form field labels)?

5. Error prevention Does the design prevent mistakes before they happen? Are destructive actions protected by confirmation? Are required fields marked before submission?

6. Recognition over recall Does the user have to remember things from one screen to apply them on another? Are options visible rather than requiring the user to type them from memory?

7. Flexibility and efficiency of use Can an experienced user move faster? Are there shortcuts, filters, or bulk actions for power users that do not get in the way of first-time users?

8. Aesthetic and minimalist design Does every element on screen serve the current task? Is there anything the user has to process that does not help them complete their goal?

9. Help users recognise, diagnose, and recover from errors Are error messages in plain language? Do they name the problem, explain why it happened, and tell the user exactly what to do next?

10. Help and documentation If a user gets stuck, is there a clear path to help? Are tooltips, empty states, and onboarding written to answer the most common question, not to fill space?

After all ten heuristics, give a summary:

  • Total counts: Pass / Needs work / Fail
  • Priority fix list: Fail items first, Needs work second, ordered by user impact
  • One sentence on the biggest risk if this ships without changes

Do not soften findings. A Fail is a Fail.

Frequently asked questions

What does the Heuristic Evaluation AI skill do?

Audit a design against Nielsen's ten usability heuristics, tying every finding to a specific element with a specific fix. Use on a built screen or a detailed mockup.

Why use Heuristic Evaluation on TypingMind?

Because you install it once and use it with any model. Heuristic Evaluation 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 Heuristic Evaluation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mshadmanrahman/claude-for-designers/tree/main/.claude/skills/heuristic-evaluation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Heuristic Evaluation?

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 Heuristic Evaluation?

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

Is the Heuristic Evaluation AI skill free?

Yes. It is published on GitHub by mshadmanrahman 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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