Accessibility Inspect logo

Accessibility Inspect

Organization
AccessLint
accessibility-inspect

One page, hands-on manual tier — drive a live page through the web accessibility (a11y) checks a rule engine can't decide: keyboard operation and focus order, screen-reader names, roles and states from the accessibility tree, reflow and zoom, reduced motion, form errors, and target size. Grades each finding by evidence basis (verified / confirm-with-a-human / human-required) and severity, and closes every criterion in a ledger: verified, flagged, not exercised, or N/A. Locates and assesses; does not fix (use `accessibility-fix`). Use it for keyboard testing, focus-order checks, screen-reader or a11y-tree review, reflow and zoom at 200%, or 'is this operable, not just lint-clean'. The automated tier is `accessibility-scan`; `accessibility-audit` runs both across a sampled site.

Overview

PublisherAccessLint
Repositoryskills
Skill nameaccessibility-inspect
Stars
99
Forks
14
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by AccessLint on GitHub. Read the source before you install it.

Installation

Install the Accessibility Inspect 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.
  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/AccessLint/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/accesslint/skills/accessibility-inspect .claude/skills/accessibility-inspect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Accessibility Inspect 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 Inspect 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 Inspect 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.

This is the semi-automated manual tier of a WCAG assessment: the checks that need interaction (keyboard, focus, state changes, reflow) or human review (focus visibility, error recovery, reading order), which a static rule engine can't decide. Work against the running page; use source only to map a finding to file:line. Locate and assess — don't fix (that's accesslint:accessibility-fix). The automated tier is accesslint:accessibility-scan; accesslint:accessibility-audit runs both under WCAG-EM.

The shared rules — severity, the no-proxy boundary, high-risk patterns, conformance, grounding — are in ../shared/methodology.md. Read it when a call needs judgment. The rules that always apply are below.

Grading

Tag each finding with a severity (user impact) and an evidence basis (what you can support). Keep the two separate.

  • ● Verified — deterministic and reproducible. Cite the selector, the interaction, and the observed a11y-tree or DOM fact. Without that proof it is not ●.
  • ◐ Flagged — you have evidence, but the decision needs a person. Attach the evidence and your opinion; don't decide it yourself.
  • ○ Human-required — needs assistive technology or lived experience. Hand it off; don't emulate it.

When unsure between two evidence grades, use the lower one. The icons show evidence basis by fill, not color.

A finding that stacks a deterministic fact on an interpretive call takes the lower grade. The machine-checkable half (e.g. no programmatic status in the a11y tree — a 1.3.1 fact) is ● evidence inside a ◐ finding; the interpretive conclusion (e.g. that color is the sole carrier of the meaning — 1.4.1) stays ◐. Citing the ● half does not upgrade the whole.

Severity (user impact, separate from evidence basis):

  • Critical — blocks a core task, with no workaround.
  • Serious — a major barrier; the task is possible but difficult.
  • Moderate — noticeable friction; the task still completes.
  • Minor — a small inefficiency or polish issue.

The ledger — a denominator, not a script

The checkpoint areas below are the run's denominator: every criterion in them ends the run in exactly one state, and the report says which.

  • ● verified or ◐/○ flagged — you drove the check and have the evidence.
  • N/A — the triggering feature isn't on the page (no form, no media, no drag UI). Decided from the snapshot; free.
  • Not exercised — the feature is present but you didn't drive it. Reported as undetermined, never silently dropped, and never as a pass.

Not-exercised is a legitimate, honest outcome and it costs nothing. Drive only what the page's features and the engine's gaps demand; don't work through checkpoints to make the report look thorough. An "undetermined — not exercised" line and a driven-and-verified line differ in evidence, not in honesty.

Dedup before driving, not after: the engine (accessibility-scan) owns the statically-detectable criteria — name/label presence, lang, autocomplete, semantic markup, contrast of text on flat backgrounds. If a scan result set or engine-owned SC list was provided (as accessibility-audit does), never re-check those criteria; ledger them as engine-owned. Without one, read what you can from the initial snapshot rather than re-deriving what the engine would catch.

AreaSCsTrigger to drive
Keyboard & focus2.1.1, 2.1.2, 2.4.3, 2.4.7, 2.4.11Always — one batched walk (below)
Structure & semantics1.3.1, 1.3.2, 2.4.1, 2.4.6Always — read from the initial snapshot; no extra driving
Names, roles, states4.1.2, 2.5.3, 4.1.3Custom widgets / stateful controls present (static name presence is engine-owned)
Visual adaptation1.4.1, 1.4.4, 1.4.10, 1.4.12, 1.3.4, 2.3.3, 1.4.11Reflow/zoom on every page (one resize each); motion checks only if motion is present
Forms & errors3.3.1–3.3.3, 1.3.5, 3.3.7, 3.3.8A form is present (label/autocomplete presence is engine-owned)
Media & timing1.2.x, 2.2.1, 2.2.2<video>/<audio>, autoplay, or timeouts present
Pointer & target2.5.1, 2.5.7, 2.5.8Target size comes free from the batched walk; drag checks only if drag UI
Content & navigation3.1.1/2, 2.4.4, 3.2.3/4, 3.2.6lang and link purpose from the snapshot; cross-page consistency is accessibility-audit's (not exercised here)

Per-checkpoint procedure and default grades: references/checkpoints.md. Read it when a triggered area needs its detailed steps, or when the user asks for a deep pass (--deep: drive every triggered area through its full procedure).

Evidence budget — cap spend by grade

Calibrated uncertainty must be cheaper than false certainty. The grade a finding can reach bounds the evidence worth gathering:

  • — full proof, gathered once: selector, interaction, observed DOM or a11y-tree fact. Don't repeat an interaction you already recorded.
  • — hard cap: one selector, one screenshot (only if the question is visual), your opinion, and what a person should confirm. Then stop. A ◐ is re-decided by a human whichever way you lean; more evidence doesn't upgrade it to ●, it just costs more.
  • — zero driving: name the functional ability, the assistive technology, and the flow you already exercised. Never drive the page to "strengthen" a ○.

Prerequisite: a browser to drive

This tier runs through a browser MCP: chrome-devtools (recommended), playwright, or puppeteer. If none is connected, run only the static checks, report the rest as ○ handoffs, and tell the user:

bash
claude mcp add chrome-devtools npx -- -y chrome-devtools-mcp@latest

Target

$ARGUMENTS is a URL, an accesslint.config.json target name, or empty for the default target. --selector <css> scopes to a component; --wait-for <css> waits for async content.

navigate_page needs a URL, so resolve first:

  • A URL: use it.
  • A target name or empty: read accesslint.config.json (and the gitignored accesslint.config.local.json overlay) and resolve the name (or default) to its url; also take its waitFor and selector.
  • No config: ask for a URL, or suggest npx @accesslint/cli init.

Then navigate to the URL and wait for the gate (--wait-for if given, otherwise the target's waitFor) before testing.

Driving efficiently

One snapshot, then scoped reads. Take one full snapshot after the wait gate; it is the basis for structure, names, roles, states, and the N/A decisions. After a state change, don't re-snapshot the page — read only the widget that changed, via evaluate_script scoped to its selector (or a snapshot of that subtree). A full-page re-snapshot per interaction is the single largest avoidable cost in this tier.

One batched keyboard walk. Traverse focus in a single evaluate_script call, not one press_key/snapshot cycle per stop: compute the tabbable sequence, focus() each element in order, and return compact JSON per stop — selector, role/name, document.activeElement confirmation, computed outline/box-shadow on :focus, bounding box (which settles 2.5.8 for free), and whether the element sits under a sticky/overlay rect (2.4.11). The walk is deterministic DOM fact, so its results are ●-citable.

The walk finds candidates; real key events confirm behavior. Scripted focus() doesn't run an app's keydown handlers, so operability (Enter/Space/arrows) and trap claims still need press_key — but only at the walk's suspects: composite widgets (roving tabindex, aria-activedescendant), elements whose handlers plausibly capture Tab/Esc, anything the walk couldn't reach. A ● keyboard-trap finding needs the real Tab/Esc presses at that widget; it doesn't need them at every widget on the page.

High-risk patterns

For drag-and-drop, rich-text editors, tree views, data grids, custom comboboxes or menus, carousels, and toast or live-region-heavy UIs, heuristic checks are unreliable. Name the APG pattern, verify what you can (●/◐), and hand off the rest as ○ with the assistive-technology steps to run. Use explain_rule for engine rules and the APG for widget contracts.

Report

Group findings by evidence basis; mark severity inline. Close the ledger at the top — every SC in the denominator lands in exactly one bucket — and keep it compact: counts, then bare SC numbers. Group not-exercised SCs by shared reason, one parenthetical clause per group, never a line per SC.

The report spends its words on failures, flags, and handoffs. A pass is its SC number in the ledger — not a paragraph: no "what passed" narration beyond at most one sentence, and no restating in the recommendations a fix already given on its finding.

# Manual inspection — <target>  ·  semi-automated tier
Severity: <c> critical · <s> serious · <m> moderate    Basis: ● <v> · ◐ <f> · ○ <h>
Ledger: pass ● <SCs> · fail ● <SCs> · flagged ◐/○ <SCs> · engine-owned <SCs> · N/A <SCs>
        not exercised → undetermined: <SCs (shared reason)> · <SCs (shared reason)>

## ● Verified
- [serious] Keyboard trap in date picker — SC 2.1.2
    where: div.datepicker[role=dialog]   repro: Tab into grid, focus never exits via Tab or Esc
    fix: <mechanical> | NEEDS HUMAN

## ◐ Flagged
- [moderate] Focus indicator may be too faint — SC 2.4.7
    where: button.ghost   evidence: focus-ghost.png; outline = 1px rgba(0,0,0,.2)
    opinion: likely fails 3:1 non-text contrast — confirm visually

## ○ Human-required
- Live-region announcement on add-to-cart — SC 4.1.3
    aria-live="polite" present (●); actual NVDA/JAWS/VoiceOver output unverified
    needs: screen-reader users (blind / low-vision, per Section 508 FPC)
    flow: add-to-cart → toast (exercised above)

Ground each entry by selector and visible text. Add file:line (symbol) only when accessibility-scan's source maps provide it; don't guess. Each ○ entry is a handoff: the functional ability and assistive technology needed, plus the flow you exercised.

Notes

  • The a11y tree shows machine state, not what a screen reader announces. aria-live being present does not mean it announces.
  • Browser zoom isn't exposed; CSS-zoom approximations are ◐.
  • Wait for async content before the initial snapshot; after that, prefer selector-scoped reads over re-snapshots.
  • Composing the tiers (dedup against accessibility-scan, one shared browser) is accessibility-audit's job. On its own, this skill reports what its checks find — and its ledger says what they didn't.

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 Inspect AI skill do?

One page, hands-on manual tier — drive a live page through the web accessibility (a11y) checks a rule engine can't decide: keyboard operation and focus order, screen-reader names, roles and states from the accessibility tree, reflow and zoom, reduced motion, form errors, and target size. Grades each finding by evidence basis (verified / confirm-with-a-human / human-required) and severity, and closes every criterion in a ledger: verified, flagged, not exercised, or N/A. Locates and assesses; does not fix (use `accessibility-fix`). Use it for keyboard testing, focus-order checks, screen-reade...

Why use Accessibility Inspect on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AccessLint/skills/tree/main/plugins/accesslint/skills/accessibility-inspect. 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 Inspect?

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 Inspect?

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

Is the Accessibility Inspect AI skill free?

It is published on GitHub by AccessLint. Check the repository for licensing terms. 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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