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Accesslint Audit

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sickn33
accesslint-audit

Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill nameaccesslint-audit
Stars
46.5K
Forks
6.8K
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 sickn33 on GitHub. Read the source before you install it.

Installation

Install the Accesslint 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/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/accesslint-audit .claude/skills/accesslint-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Accesslint 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 Accesslint 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 Accesslint 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.

You audit accessibility and optionally fix what's broken.

When to Use

  • Use this skill when the task matches this description: Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.

Pick a mode from the user's intent

  • Report mode — "audit my codebase", "review src/components/", "what's wrong with this page?", "give me an a11y report". You audit + write a report. You do not edit files.
  • Fix mode — "fix the a11y issues in X", "audit and fix", "make this accessible", "verify the contrast fix landed", or hands you a violation report and asks to apply it. You audit → edit → verify.

If unsure, ask. Don't default-to-fix when the user only asked for an audit.

For very large sweeps where main-thread context cost matters, you can be invoked via Task (general-purpose agent) for context isolation. The recipe is the same either way.

Picking a flow

Three flows, in order of preference.

  1. audit_live — try first for any URL. Connects to a running Chrome debug session, or auto-launches Chrome minimized — no user setup needed. Single call; IIFE bytes don't enter your context.
  2. audit-live-page prompt — use when the user needs their existing browser session audited (authenticated app, specific state) and a browser MCP (chrome-devtools-mcp, playwright-mcp, puppeteer-mcp) is connected. Invoke via Skill with mode: "fix" or mode: "plan".
  3. audit_html — for raw HTML strings, files (Read first, then audit_html), or JSX you've rendered to a string. Pair with audit_diff({ html }) for fix-mode verification.

For non-URL targets, skip straight to flow 3. For URLs, try flow 1; on auto-launch failure, try flow 2 if a browser MCP is connected; otherwise fall back to flow 3 with a note that live-DOM coverage is limited.

Scope handling (report mode)

  • Directory path — analyze all relevant files within.
  • Multiple files — analyze the listed files plus imports they reach.
  • A URL — audit it. If it's a dev-server URL, that's flow 1 or 2.
  • No arguments — ask the user to narrow scope. Whole-codebase sweeps are rarely the right thing.

State the scope explicitly at the start of your report.

Approach (report mode)

  1. Map the surface. Glob/Grep to enumerate components, templates, styles. Sample representative files; don't open everything blindly.
  2. Audit live where possible — the rendered DOM catches issues source can't show. Use the flow picker above.
  3. Look for patterns. If one component fails a rule, similar components likely do too. Group by rule ID and component family — don't list 30 instances of the same issue 30 times.
  4. Prioritize by user impact. Critical/serious first. Many low-impact violations of one rule are often a single root-cause fix.
  5. Use format: "compact" for sweep-time calls. Reserve verbose output for rules you'll expand in the report.
  6. Trust Source: lines. Live-DOM audits against React dev builds attach Source: <file>:<line> (Symbol) per violation via DevTools fibers. Use it as the file pointer instead of grepping selectors. Fall back to stable hooks → visible text → tree position when absent.
  7. Stop and ask if a single audit returns more than ~50 violations — a 200-violation report isn't actionable.

The engine catches what's mechanically detectable. Manual judgment is needed for content clarity, screen-reader announcement quality, keyboard flow coherence, and complex visual contrast — flag those for human review, don't guess.

Report format

# Accessibility audit — <scope>

## Summary
- N critical, M serious, K moderate, J minor (after deduplication)
- Most impactful patterns: <one-line each, max 3>

## Critical (blocks access)
For each pattern:
- **Pattern**: <one-line description>
- **WCAG**: <ID> — <name>
- **Affected files**: <file:line> (×N if repeated)
- **Fix**: <directive from engine output, or specific code change>
- **Why critical**: <user impact>

## Serious
[same shape]

## Moderate / Minor
[Bullet list, deduplicated by rule. Skip per-instance detail unless the fix differs.]

## Recommendations
- Architectural / pattern-level changes that would prevent recurrence.
- Tooling or component abstractions worth introducing.
- What to verify manually (screen reader, keyboard, low-vision testing).

## Positive findings
What the codebase does well — short, factual, reinforces practices to keep.

Include rule IDs in every entry. Quote the Fix: directive verbatim for mechanical rules. For visual / contextual, leave a TODO with the rule ID; don't invent content.

Recipe (fix mode)

  1. Baseline. Audit with name: "before" and format: "compact".
  2. Plan + apply. For each violation:
    • Source: line present → open that file at that line. If multiple are listed (separated by ), the first is the JSX literal; the rest are enclosing components. Use Symbol to disambiguate.
    • No Source: → grep stable hooks (data-testid, id, aria-label), then visible text, then tree position.
    • The violation's Fixability: and Fix: fields are authoritative — apply mechanical fixes verbatim, leave TODOs with the rule ID for contextual / visual. Never invent content.
    • Group same-file edits into one operation.
    • Confirm scope with the user before touching files outside the obvious target, or before more than ~10 mechanical fixes.
  3. Verify. Run audit_diff({ audit_name: "before" }) against the baseline (or re-baseline with a new name). Confirm -fixed covers your targets and +new is empty.

Source: lines come from React DevTools fibers and only appear in live-DOM audits against React dev builds. Static audits won't have them — fall back to selectors.

When unsure about a rule, call explain_rule({ id: "<rule-id>" }) for guidance and browserHint.

When to bail (fix mode)

  • A violation has no Fix: directive — leave a TODO, don't guess.
  • Verification fails (anything in +new, or a targeted rule missing from -fixed) — name it and stop. Do not iterate silently.

Output (fix mode)

Per cycle: flow used, violations by impact, what was applied (file + rule), what was deferred (TODOs + reasons), final diff.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Frequently asked questions

What does the Accesslint Audit AI skill do?

Find and fix WCAG 2.2 accessibility issues. Two modes — report (sweep a codebase or page, produce a prioritized written report, no edits) and fix (audit→edit→verify loop on a target). Prefers direct-CDP live-DOM auditing; falls back to a browser-MCP composition or HTML-string audits.

Why use Accesslint Audit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/accesslint-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Accesslint 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 Accesslint Audit?

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

Is the Accesslint Audit AI skill free?

Yes. It is published on GitHub by sickn33 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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