Accessibility Audit logo

Accessibility Audit

Organization
AccessLint
accessibility-audit

Whole site or product — a full web accessibility (a11y) audit against WCAG 2.2, following the WCAG-EM methodology. Defines scope, samples representative pages and flows, runs the automated tier (`accessibility-scan`) and the hands-on manual tier (`accessibility-inspect`), and produces one conformance report. Grades each finding by severity and evidence basis, and states per-criterion conformance as pass, fail, or undetermined (needs a human). Use it for 'audit my site for accessibility', 'is this product accessible', 'a11y audit', 'WCAG or Section 508 conformance report', or any multi-page assessment. Assesses; does not fix (use `accessibility-fix`) or diff (use `accessibility-diff`). For a single page use `accessibility-scan`; for hands-on keyboard and screen-reader checks use `accessibility-inspect`.

Overview

PublisherAccessLint
Repositoryskills
Skill nameaccessibility-audit
Stars
99
Forks
14
Bundled files
Instructions only
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 AccessLint on GitHub. Read the source before you install it.

Installation

Install the Accessibility Audit 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-audit .claude/skills/accessibility-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Accessibility Audit 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 Audit 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 Audit 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 a full WCAG 2.2 accessibility audit using WCAG-EM. It defines scope, samples representative pages and flows, runs both evaluation tiers, and produces one conformance report:

  • Automated tier: accesslint:accessibility-scan (the rule engine).
  • Semi-automated manual tier: accesslint:accessibility-inspect (keyboard, focus, state, reflow, and the rest the engine can't decide).

Assess; don't fix (accesslint:accessibility-fix) or diff (accesslint:accessibility-diff). One page with no sampling is a accessibility-scan, not an audit. This skill delegates each sampled page to its own subagent (step 4), so it stays light on large samples.

The full doctrine — WCAG-EM in detail, the severity rubric with examples, the no-proxy boundary, grounding — is in ../shared/methodology.md. The rules needed to run this skill are below.

Grading

Each finding carries a severity and an evidence basis. Keep them separate.

  • Evidence basis: ● verified (deterministic, with cited proof) · ◐ flagged (evidence captured, a person decides) · ○ human-required (needs assistive technology or lived experience; handed off, not emulated).
  • Severity: critical (blocks a core task) · serious (major barrier) · moderate (friction, still completable) · minor (polish).

WCAG-EM steps

Run in order and state what you did at each.

  1. Scope. State the target and its boundary, the goal (default WCAG 2.2 AA; --level AAA adds AAA), the technologies in use, and the assistive-technology baseline the human handoff should cover. You scope that baseline; you don't test it.

  2. Explore. Use Glob/Grep to find routes, templates, and shared components. Note key flows, content types, and stateful UI (modals, wizards, empty and error states). accesslint.config.json targets are a starting point.

  3. Sample. Choose a structured set (entry page, each key flow end to end, every page with a new template or complex widget, and the important states) and a small random set. Say what's in each and why.

  4. Evaluate. Delegate each sampled page or state to its own Task so it runs in its own context and returns its findings; independent pages can run in parallel. Each Task:

    • runs accesslint:accessibility-scan (--format json) first, then accesslint:accessibility-inspect against the same rendered state — same URL, --selector, --wait-for — passing scan's results (or at least the list of SCs the engine covered) into the inspect run;
    • dedups by SC ownership before driving, not after: accessibility-scan owns rule-detectable criteria, accessibility-inspect owns interaction and judgment criteria; inspect never re-checks an engine-owned SC, and where both still cover the same SC at the same element, accessibility-scan's result wins;
    • returns a structured block: per finding, the SC, severity, evidence basis (●/◐/○), location, tier, evidence, and fix or handoff, plus this page's per-SC ledger (verified / flagged / engine-owned / N/A / not exercised).

    Aggregate the returned blocks in step 5. For a one- or two-page scope, run the tiers inline instead of spawning a Task.

    A shared browser is optional and improves selector matching across tiers, but it's a pre-wired precondition, not something this skill sets up at runtime: the browser MCP binds to its Chrome at server start (--autoConnect or --browser-url), with the engine pointed at the same port. Without it (the default), each Task runs both tiers against the same URL and --wait-for gate and dedups by SC ownership.

  5. Report. Aggregate into the format below. Conformance has three states: pass or fail only for ● findings; everything ◐ or ○ — and every SC no page exercised — is undetermined and goes to a human. One sampled page failing an SC fails it for the whole scope at that level. Don't report conformance you can't support, and don't let a not-exercised SC read as a pass. Keep the ledger to counts and bare SC lists — group undetermined SCs by shared reason, one clause per group — and spend the report's words on failures, flags, and handoffs: a pass is its SC number in the list, with at most one sentence of narration for the whole passing set.

Report format

# Accessibility audit — <product / scope>
WCAG 2.2 Level AA · WCAG-EM · <N> pages/states sampled

## Scope
- Target & boundary: <…>     Goal: WCAG 2.2 AA
- Technologies in use: <…>
- AT baseline (for the human handoff, not tested here): <SR+browser pairs, keyboard-only, …>

## Sample
- Structured: <page/state> — <why>   (×N)
- Random: <page/state>

## Conformance (per success criterion)
- Pass ●: <n>  ·  Fail ●: <n>  ·  Undetermined (◐/○/not exercised): <n>  ·  N/A: <n>
- Fail ●: <SCs>   Pass ●: <SCs>   N/A: <SCs>
- Undetermined: <SCs (shared reason)> · <SCs (shared reason)>
- Pass/fail is asserted only for ● criteria; ◐/○ and not-exercised are undetermined.

## Findings — by severity, tagged by evidence basis
### Critical
- [●] <barrier> — SC x.x.x — where: <selector / file:line> — tier: scan|inspect — → `accessibility-fix`
- [◐] <barrier> — SC x.x.x — evidence: <screenshot / measurement> — confirm: <what a person checks>
### Serious / Moderate / Minor
[same shape]

## Human-required (○) — the testing handoff
- <what only AT or lived experience reveals> — SC x.x.x
    needs: <functional ability + AT, per Section 508 FPC>   flow: <sampled flow>

## Recommendations
- Root-cause / pattern fixes (one change that clears many instances) → hand to `accessibility-fix`.
- What to send to human and AT testing, and on which flows.
- Wire `accesslint:accessibility-diff` into CI for the sampled targets.

Notes

  • Assess, don't fix (use accessibility-fix) or diff (use accessibility-diff). Don't emulate human experience: usability is ◐, lived experience is ○ and handed off.
  • Conformance is per SC across the whole sample. Don't average failures away.
  • Two browsers can drift selectors; prefer a shared browser for ●-precision, otherwise note that dedup is best-effort.
  • State what wasn't covered (pages outside the sample, ○ criteria). Omitting it reads as "all clear".
  • Use list_rules and explain_rule for engine-rule metadata.

Frequently asked questions

What does the Accessibility Audit AI skill do?

Whole site or product — a full web accessibility (a11y) audit against WCAG 2.2, following the WCAG-EM methodology. Defines scope, samples representative pages and flows, runs the automated tier (`accessibility-scan`) and the hands-on manual tier (`accessibility-inspect`), and produces one conformance report. Grades each finding by severity and evidence basis, and states per-criterion conformance as pass, fail, or undetermined (needs a human). Use it for 'audit my site for accessibility', 'is this product accessible', 'a11y audit', 'WCAG or Section 508 conformance report', or any multi-page...

Why use Accessibility Audit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AccessLint/skills/tree/main/plugins/accesslint/skills/accessibility-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Accessibility Audit?

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 Audit?

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

Is the Accessibility Audit 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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