Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add h4vzz/awesome-ai-agent-skills --skill accessibility-testinggit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/accessibility-testing)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/accessibility-testing"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/accessibility-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/accessibility-testing"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/accessibility-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.02223 |
| Opus 5.5 | $0.00012 | $0.00889 |
| Sonnet 5.5 | $0.00006 | $0.00445 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
Accessibility Testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to accessibility-testing — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Accessibility Testing
This skill enables the agent to perform thorough accessibility audits of web pages and components against the Web Content Accessibility Guidelines (WCAG) 2.1 at AA and AAA conformance levels. The agent identifies violations across four principles — Perceivable, Operable, Understandable, Robust — and generates structured compliance reports with specific code fixes. It covers automated checks (color contrast, missing alt text, ARIA misuse), semi-automated checks (keyboard navigation flows, focus management), and manual check guidance (screen reader announcements, cognitive load).
Workflow
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Define Audit Scope and Conformance Target: Determine which pages, components, or user flows to audit, and whether the target is WCAG 2.1 AA (most common legal requirement) or AAA (highest conformance). Identify the assistive technologies to consider: screen readers (NVDA on Windows, VoiceOver on macOS/iOS, TalkBack on Android), keyboard-only navigation, and magnification tools.
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Run Automated Scans: Execute automated accessibility analysis using axe-core or Lighthouse. These tools detect approximately 30-40% of WCAG violations automatically, including missing alt attributes, insufficient color contrast ratios, missing form labels, duplicate IDs, invalid ARIA roles, and missing document language. Capture each violation with its WCAG criterion reference (e.g., 1.4.3 Contrast Minimum), severity (critical, serious, moderate, minor), the affected HTML element, and the CSS selector path.
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Perform Keyboard Navigation Testing: Manually trace every interactive flow using only Tab, Shift+Tab, Enter, Space, Escape, and arrow keys. Verify that focus order matches visual order, that focus indicators are visible (minimum 2px outline with 3:1 contrast), that modal dialogs trap focus correctly, and that no keyboard traps exist. Document any flow where a keyboard user cannot complete a task that a mouse user can.
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Test with Screen Readers: Navigate the page with at least one screen reader to verify that all content is announced correctly. Confirm that images have meaningful alt text (or
alt=""for decorative images), that headings form a logical hierarchy (no skipped levels), that landmarks (<nav>,<main>,<aside>) are present, and that dynamic content updates are announced viaaria-liveregions.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 134 lines · 30 tokens per session scan A 5c6869f7ccbd
Accessibility Testing is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 30 tokens to every session and 2,223 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to accessibility-testing, differing in 4 lines, and is treated as a copy.
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