Accessibility Audit

Overview

In today’s digital landscape, building inclusive user experiences isn’t just ethical—it’s essential. The Accessibility Audit AI skill empowers developers, designers, and product teams to automatically detect and resolve accessibility barriers in web interfaces by aligning with WCAG 2.2 (Web Content Accessibility Guidelines) standards.

This intelligent workflow integrates directly into your development process, scanning UI components or code for common a11y (accessibility) issues such as insufficient color contrast, broken keyboard navigation, missing ARIA labels, and screen reader incompatibilities. It doesn't stop at detection—Accessibility Audit goes a step further by generating clear, contextualized repair suggestions that are easy to implement and review.

Whether you're auditing a legacy application or ensuring new features meet compliance requirements from day one, this skill bridges the gap between automated testing tools and expert-level accessibility reviews—saving time, reducing risk, and expanding your product’s reach to users with disabilities.

With rising legal expectations (like ADA, EN 301 549, and Section 508) and growing demand for inclusive design, integrating an AI-powered accessibility audit into your toolkit is no longer optional. It's a strategic advantage.

Key Benefits

Here’s what makes Accessibility Audit a must-have for modern web development:

✅ Early Detection of Critical Issues

Catch accessibility problems during development—not after launch. By identifying issues like low text-to-background contrast ratios early, you avoid costly redesigns and ensure compliance before deployment.

Scenario: A developer submits a button component with light gray text on a white background. The AI flags it with a failed AA/AAA rating and suggests a compliant alternative color using WCAG luminance formulas.

🔧 Actionable, Contextual Fixes

Unlike generic linters, Accessibility Audit provides human-readable remediation steps tailored to the specific context—whether it’s adding aria-label attributes, fixing focus order, or restructuring semantic HTML.

Scenario: A modal dialog lacks proper keyboard trap logic. The AI detects this and recommends JavaScript enhancements to manage Tab key looping and Escape key dismissal.

🧩 Screen Reader Compatibility Insights

It analyzes content structure for assistive technology usability, checking heading hierarchy, landmark roles (<main>, <nav>), and dynamic announcements for screen readers like NVDA, JAWS, and VoiceOver.

Scenario: After detecting empty <button> elements used for icons, the AI proposes aria-hidden="true" paired with visually hidden <span> labels for screen reader clarity.

⚙️ Seamless Integration Across Stacks

Built as a universal AI skill, Accessibility Audit works across frameworks (React, Vue, Angular, plain HTML/CSS/JS), CMS platforms, and static sites—making it ideal for both greenfield projects and large-scale refactors.

Scenario: Integrated into a CI pipeline, it runs on every pull request, blocking merges if critical a11y violations are introduced.

📊 Consistent Compliance Reporting

Generate standardized reports aligned with WCAG 2.2 success criteria (Level A, AA, AAA). These can be shared with auditors, stakeholders, or legal teams to demonstrate proactive accessibility efforts.

Scenario: Before a vendor audit, a team exports a full accessibility scorecard showing resolved issues and ongoing improvements—all powered by AI insights.

Core Features

Feature	Description	WCAG Criteria Covered
Color Contrast Analyzer	Automatically evaluates foreground/background color pairs against minimum contrast thresholds; highlights failing elements and suggests accessible alternatives.	1.4.3, 1.4.11
Keyboard Navigation Inspector	Maps tab flow, detects skipped interactive elements, identifies missing focus indicators, and verifies operability without a mouse.	2.1.1, 2.4.3, 2.4.7
Screen Reader Readiness Check	Validates use of ARIA roles, labels, states, and live regions; ensures logical DOM structure and semantic markup.	1.3.1, 4.1.2, 2.4.6
Automated Issue Remediation Generator	Produces ready-to-implement code fixes, including attribute additions, CSS adjustments, and JS handlers.	All applicable
Compliance Dashboard & Export	Summarizes findings in a structured report format (JSON, Markdown, PDF) suitable for audits and documentation.	1.1.1 - 4.1.3

These capabilities transform accessibility from a manual, reactive chore into a continuous, automated quality gate—just like unit tests or linting.

How to Get & Install

The Accessibility Audit skill is designed for broad compatibility and ease of adoption. While not tied to any single platform, it leverages open-source tooling and AI integrations to deliver powerful results. Here’s how to install and begin using it today:

Option 1: Use via GitHub-Powered Tools (Universal Setup)

Since this is a universal AI skill, you can integrate Accessibility Audit using community-driven tools and AI assistants that support custom rulesets and plugins.

Step 1: Clone or Reference the Official Repository

Start by exploring curated resources and automation scripts maintained by the web accessibility community:

git clone https://github.com/topics/web-accessibility.git

While this topic page aggregates leading tools (like Axe, Pa11y, Lighthouse), we recommend combining them with AI-enhanced workflows.

Step 2: Integrate with AI Coding Assistants

For Cursor.sh Users:

Add the Accessibility Audit rule set to enable real-time feedback inside your editor.

1. Navigate to your project root.

2. Create a .cursorrules file:

   rules:
     - name: "Accessibility Audit (WCAG 2.2)"
       description: "Enforce color contrast, keyboard nav, and screen reader best practices"
       triggers:
         - "*.html"
         - "*.jsx"
         - "*.tsx"
         - "*.vue"
       commands:
         - run: npx axe-cli {file} --wcag 2.2
           output: ai-suggestions.md
         - prompt: |
             Analyze the following accessibility scan results and generate concise,
             developer-friendly fix recommendations based on WCAG 2.2 guidelines.
             Focus on color contrast, keyboard operability, and screen reader UX.
           model: claude-3-opus
           context: ai-suggestions.md
   

3. Save the file and restart Cursor. Now, whenever you edit front-end files, AI-powered accessibility suggestions appear inline.

For Claude Code / Anthropic Studio Users:

Use the /plugin install command to add accessibility auditing capabilities.

1. Open your codebase in Claude Code.

2. Run:

   /plugin install accessibility-audit
   

   Note: If unavailable in marketplace, manually link via GitHub:

   /plugin install https://github.com/dequelabs/axe-core
   

3. Once installed, invoke the plugin with:

   /analyze --skill=accessibility-audit src/components/Header.vue
   

   You’ll receive a detailed breakdown of issues and suggested patches.

For VS Code + GitHub Copilot + Linters Combo:

Combine traditional tooling with AI augmentation.

1. Install key extensions:

   code --install-extension deque-systems.vscode-axe-linter
   code --install-extension ms-ceintl.vscode-language-pack-zh
   

2. Install CLI tools:

   npm install -g axe-cli pa11y
   

3. Add a script to package.json:

   "scripts": {
     "audit:a11y": "axe --reporter=json | tee a11y-report.json && echo 'Run /prompt a11y-report.json for AI analysis'"
   }
   

4. After running npm run audit:a11y, paste the JSON output into your AI assistant with the prompt:

   “Based on this WCAG 2.2 audit data, list each issue, its impact, and provide exact code changes to fix it.”

You now have a semi-automated Accessibility Audit pipeline.

Option 2: Deploy in CI/CD Pipelines

For teams practicing DevOps, embed accessibility checks directly into your build process.

Example .github/workflows/accessibility.yml:

name: Accessibility Audit
on: [pull_request]

jobs:
  a11y-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Node
        uses: actions/setup-node@v3
        with:
          node-version: 18
      - run: npm install -g axe-cli
      - run: axe --url=https://staging.example.com --output=json
      - name: Generate AI Fix Summary
        run: |
          curl -X POST https://api.anthropic.com/v1/messages \
            -H "x-api-key: ${{ secrets.CLAUDE_API_KEY }}" \
            -H "anthropic-version: 2023-06-01" \
            -d '{
              "model": "claude-3-opus-20240229",
              "max_tokens": 1024,
              "messages": [
                {
                  "role": "user",
                  "content": "Analyze this accessibility report and generate prioritized, technical fix instructions: '"$(cat results.json)"'"
                }
              ]
            }' > ai-fixes.md
      - uses: actions/upload-artifact@v3
        with:
          name: accessibility-review
          path: |
            results.json
            ai-fixes.md

Now every PR includes an AI-assisted accessibility review.

Use Cases

Here are five ideal scenarios where Accessibility Audit delivers maximum value:

1. Design Handoff Validation

Before engineering begins, run the audit on exported HTML prototypes to verify that visual designs meet baseline accessibility standards—especially color usage and interaction patterns.

2. Legacy Website Modernization

Audit aging websites with outdated markup. Let the AI identify hundreds of contrast and semantics issues, then batch-generate fixes to accelerate migration.

3. Component Library Governance

Integrate the skill into Storybook or Bit to enforce a11y rules across reusable UI components used enterprise-wide.

4. Government & Education Sector Compliance

Meet strict regulatory requirements (e.g., Section 508 refresh, EN 301 549) with documented, repeatable audit trails enhanced by AI interpretation.

5. Developer Training & Onboarding

New engineers get instant feedback when they introduce a11y anti-patterns—turning mistakes into teachable moments with guided corrections.

Tips

To get the most out of the Accessibility Audit AI skill:

💡 Combine Automated Scans with Human Testing
While AI catches ~70% of issues, always supplement with manual testing using actual screen readers and keyboard-only navigation for nuanced edge cases.

💡 Prioritize by Impact
Use the AI’s severity tagging to focus first on Level A failures (which block access entirely), then progress to AA and AAA enhancements.

💡 Customize Rules Per Project
Adjust tolerances—for example, allowing temporary contrast exceptions in hero banners during design iterations—but flag them for follow-up.

Disclaimer

The Accessibility Audit AI skill is intended to assist with identifying potential accessibility issues and suggesting remediations based on WCAG 2.2 guidelines. However, automated tools cannot guarantee full compliance. Final validation should include manual evaluation by accessibility experts and testing with real assistive technologies. Implementation results may vary depending on environment, framework, and content complexity. This guide references third-party tools and services; their availability and functionality are subject to change.