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Accessibility Verification

Community
nahid-sparktales
accessibility-verification

Prove an accessibility claim instead of asserting it — automated scan plus the keyboard, focus, zoom and screen-reader passes a scanner structurally cannot make. Fires before anyone says a screen is accessible or WCAG AA, when signing off UI work, or when an audit result needs checking. Not for making the fixes (accessibility), and it never upgrades a clean scan into a conformance claim.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameaccessibility-verification
Stars
58
Forks
5
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 nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Accessibility Verification 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/nahid-sparktales/agent-dispatcher/tree/main/skills/design/accessibility-verification
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/design/accessibility-verification .claude/skills/accessibility-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Accessibility Verification 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 Accessibility Verification 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 Accessibility Verification 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.

Accessibility verification

A clean automated scan proves that a set of machine-checkable rules found nothing. It says nothing about whether a name is meaningful, an order is logical, an announcement is useful, or a flow can be completed without a mouse. Those need a person driving the interface.

When this fires

Before any claim that a screen, component or flow is accessible, keyboard-operable, screen-reader usable or WCAG-conformant — your own work or someone else's, and when re-checking after fixes. It does not fire to write the fixes; that is accessibility.

Procedure

  1. Fix the scope in writing first. Which routes, which states (empty, loading, error, logged in), which viewport, which browser. A claim without a named scope is not a claim — everything outside it stays explicitly unverified.
  2. Render the real thing. The running app at the route that changed, not the source, not Storybook alone if the route composes differently. A stale build verifies nothing.
  3. Run an automated scan and record it exactly. Use whatever the project already has — an axe-based checker, the browser's accessibility audit, a CI accessibility step. Record the tool and version, the URL, the rule ids that failed and the count. Treat the result as a floor: scanners find only a minority of real barriers, and they cannot judge meaning or order at all.
  4. Do the keyboard pass with the pointer unused. Tab from the top: write down the order, and whether it matches the visual order. Check the skip link, every control being reachable and operable with Enter/Space/arrows, Escape closing overlays, no trap, focus returning after a dialog closes, and the focus indicator being visible and not covered by sticky headers or banners.
  5. Do the screen-reader pass on at least one real pairing — VoiceOver with Safari, or NVDA with Firefox. Read the flow start to finish, then complete the task with the screen buffer as your only information. Check: does every control announce a name, a role and its state; do headings and landmarks describe the page; do errors and status changes get announced when they happen. See references/screen-reader-passes.md for how to drive each one.
  6. Check zoom, reflow and text spacing. 200% browser zoom, and a 320 CSS px wide viewport: no horizontal scrolling, no content lost, no overlap. Then apply the text-spacing condition (line height 1.5×, paragraph spacing 2×, letter spacing 0.12em, word spacing 0.16em) by injecting CSS, and confirm nothing is clipped or overlapped.
  7. Measure contrast on the rendered pixels, in every state — default, hover, focus, selected, error, and text over images or gradients. A token that passes in the palette can fail in place.
  8. Reach the non-happy states deliberately — submit the form empty, trigger the error, load the empty list — and verify each announces itself rather than only changing colour.
  9. Re-run the whole pass after fixes. A fix is verified by repeating step 3 onward, never by the reasoning that produced it. Regressions from accessibility fixes are common, especially in focus handling.
  10. Stop and ask before anything outward-facing. Publishing an accessibility statement, filling a VPAT or answering a customer conformance questionnaire is a formal representation about the product. This procedure supplies evidence for that; it does not authorize making it.

Checklist

  • Scope written down: routes, states, viewport, browser
  • The running app was exercised, not the source read
  • Automated scan run; tool, version and exact failures recorded
  • Full keyboard pass done with no pointer use; tab order written down
  • Focus visible at every stop and not obscured by overlaying chrome
  • Screen-reader pass on a named reader + browser pairing, completing the real task
  • Names, roles and states confirmed as announced — not assumed from the markup
  • 200% zoom and 320px reflow checked; text-spacing condition applied
  • Contrast measured on rendered pixels in every interactive state
  • Error and status messages heard, not just seen
  • Everything out of scope or unchecked is named in the report

Failure handling

  • No screen reader available — report that the screen-reader checks were not performed, list what was. The claim then stops at "keyboard-operable and scan-clean on ". Do not substitute the accessibility tree for a screen-reader pass: the tree shows what is exposed, not what is announced, in what order, or whether it makes sense.
  • The scan reports violations in third-party code — record them separately with their source. They are still barriers for users; they are just not fixable in this diff.
  • A barrier will not reproduce — say so with what you tried. Screen-reader behaviour varies by reader, browser and version; name the exact combination.
  • Scan is clean but the keyboard pass fails — the keyboard result wins. Report the scan as uninformative here, not as a pass.
  • Time ran out mid-pass — report what was covered and what was not. A partial pass honestly scoped is useful; a partial pass reported as complete is worse than none.

What this refuses to conclude

  • Zero automated violations is not "accessible" and not "WCAG 2.2 AA".
  • A passing keyboard pass is not a passing screen-reader pass.
  • One route verified is not the product verified.
  • Code that was written following accessible patterns is not verified code — it is unverified code that will probably pass.
  • Conformance is a claim about a defined scope against defined criteria. Without the scope and the per-criterion evidence, this procedure produces findings, not conformance.

Evidence to report

Scope; the scan tool, version and output; the tab order as observed, with any traps or invisible stops; the reader + browser pairing and what it actually announced at each step, quoted; the zoom, reflow and text-spacing results; contrast ratios measured, with the states they were measured in; screenshots where a picture carries what words cannot. Then the list of criteria, routes and states not checked. "Accessibility verified" without those is an assertion, not evidence.

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 Accessibility Verification AI skill do?

Prove an accessibility claim instead of asserting it — automated scan plus the keyboard, focus, zoom and screen-reader passes a scanner structurally cannot make. Fires before anyone says a screen is accessible or WCAG AA, when signing off UI work, or when an audit result needs checking. Not for making the fixes (accessibility), and it never upgrades a clean scan into a conformance claim.

Why use Accessibility Verification on TypingMind?

Because you install it once and use it with any model. Accessibility Verification 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 Accessibility Verification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/design/accessibility-verification. 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 Accessibility Verification?

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 Accessibility Verification?

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

Is the Accessibility Verification AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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