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Edge Case Identification

Community
Owl-Listener
edge-case-identification

Identify and design for edge cases that disproportionately affect users with disabilities. Use when reviewing designs for completeness, planning test cases, or when someone says 'that's an edge case.' Triggers on: edge case, corner case, that's unlikely, only a few users, exception, unusual scenario, we'll handle that later, happy path, unhappy path, error state, empty state.

Overview

PublisherOwl-Listener
Repositoryinclusive-design-skills
Skill nameedge-case-identification
Stars
108
Forks
28
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 Edge Case Identification 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/Owl-Listener/inclusive-design-skills/tree/main/inclusive-personas/skills/edge-case-identification
  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/inclusive-design-skills.git /tmp/inclusive-design-skills
mkdir -p .claude/skills
cp -r /tmp/inclusive-design-skills/inclusive-personas/skills/edge-case-identification .claude/skills/edge-case-identification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Edge Case Identification 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 Edge Case Identification 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 Edge Case Identification 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.

Edge Case Identification

Identify the scenarios that teams dismiss as "edge cases" but that are everyday reality for users with disabilities. An edge case for the team is often a primary case for someone.

Core Principle

"That's an edge case" is the most common way accessibility gets deprioritised. If it affects people with disabilities, it's not an edge case — it's a design requirement you haven't met.

Categories of Overlooked Edge Cases

Input Edge Cases

  • User cannot use a mouse (keyboard only)
  • User cannot use a keyboard (voice or switch only)
  • User cannot use both hands (one-handed operation)
  • User pastes content instead of typing it
  • User uses autocomplete or password manager to fill fields
  • User enters content in a different script or language
  • User inputs using dictation with punctuation spoken aloud

Output Edge Cases

  • User cannot see the screen (screen reader)
  • User sees only a small portion of the screen (magnification)
  • User cannot distinguish colours
  • User cannot hear audio
  • User has screen in greyscale mode or high contrast mode
  • User has text scaled to 200%
  • User has animations disabled

Content Edge Cases

  • Extremely long names (hyphenated, multi-word, cultural naming)
  • Non-Latin characters in name or address fields
  • Empty states where no data exists yet
  • Error states when everything goes wrong at once
  • First-time use with no prior context
  • Returning after months away with forgotten context
  • Content in the wrong language for the user

Context Edge Cases

  • Session timeout during a long form
  • Connection lost mid-task
  • Battery dying during a critical flow
  • Screen rotation mid-interaction
  • System font size set to maximum
  • Browser zoom at 200% or higher
  • Ad blocker or privacy extension modifying the page

Timing Edge Cases

  • User is much slower than expected (switch user, cognitive delay)
  • User is much faster than expected (screen reader power user)
  • User needs to pause mid-task for hours or days
  • Timed session expires before user finishes
  • Content updates while user is reading it

How to Find Edge Cases

The "What If" Exercise

For each step in a flow, ask:

  • What if the user can't see this?
  • What if the user can't click this?
  • What if the user can't understand this?
  • What if the user takes 10x longer than expected?
  • What if the user leaves and comes back tomorrow?
  • What if this field contains unexpected content?

The Inversion Test

Take your happy path scenario. Invert each assumption:

  • "User is on a desktop" → mobile, or tablet, or screen reader
  • "User has fast internet" → slow, intermittent, or offline
  • "User reads English fluently" → low literacy or second language
  • "User can see the colour change" → colour blind or blind
  • "User completes this in one sitting" → interrupted, returns later

Response to "That's an Edge Case"

When someone says this, ask:

  1. How many users does this affect? (often more than assumed)
  2. How severe is the impact? (a blocked user is worse than a mildly inconvenienced one)
  3. Is this a permanent condition for some users? (then it's their daily experience, not an edge case)
  4. Would fixing this improve the experience for other users too? (almost always yes)

Assessment Questions

  1. Has the team mapped edge cases beyond the happy path?
  2. Have input, output, content, context, and timing edge cases been considered?
  3. Are there dismissed "edge cases" that are daily reality for users with disabilities?
  4. Does the test plan include scenarios for assistive technology users?

Frequently asked questions

What does the Edge Case Identification AI skill do?

Identify and design for edge cases that disproportionately affect users with disabilities. Use when reviewing designs for completeness, planning test cases, or when someone says 'that's an edge case.' Triggers on: edge case, corner case, that's unlikely, only a few users, exception, unusual scenario, we'll handle that later, happy path, unhappy path, error state, empty state.

Why use Edge Case Identification on TypingMind?

Because you install it once and use it with any model. Edge Case Identification 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 Edge Case Identification in TypingMind?

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

Which AI models can use Edge Case Identification?

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 Edge Case Identification?

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

Is the Edge Case Identification 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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