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 oyi77/1ai-skills --skill accessibility-auditgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/accessibility-audit)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/accessibility-audit"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/accessibility-audit/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/oyi77/1ai-skills/accessibility-audit"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/accessibility-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.01123 |
| Opus 5.5 | $0.00015 | $0.00449 |
| Sonnet 5.5 | $0.00007 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
accessibility-audit 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Accessibility (a11y) audit framework for WCAG 2.1 compliance. Covers semantic HTML, ARIA attributes, keyboard navigation, color contrast, focus management, and screen reader testing.
Capabilities
- Audit pages against WCAG 2.1 AA criteria
- Check semantic HTML structure and heading hierarchy
- Verify keyboard navigation and focus management
- Test color contrast ratios (4.5:1 normal, 3:1 large text)
- Validate ARIA attributes and screen reader experience
When to Use
Trigger phrases:
-
"accessibility audit"
-
"WCAG compliance audit — semantic HTML, ARIA, keyboard navigation, color contrast"
-
Before launching any public-facing UI
-
Legal compliance requirements (ADA, EAA)
-
User complaints about accessibility
-
Adding keyboard navigation support
When NOT to Use
- Task is about content strategy, not creation (use strategy skills)
- Task is about content distribution (use distribution skills)
- You need to analyze content performance (use analytics skills)
- Task is about content moderation (use moderation tools)
- You don't have content guidelines
- Task requires domain expertise (consult experts)
Pseudo Code
The accessibility-audit workflow follows a standard pipeline pattern.
Core flow:
# accessibility-audit primary flow
input = prepare(raw_data)
result = process(input, config={accessibility, aria, audit, color, compliance})
validate(result)
deliver(result)
Error handling:
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Core Workflow
# accessibility-audit primary flow
input = prepare(raw_data)
result = process(input, config={accessibility, aria, audit, color, compliance})
validate(result)
deliver(result)
Error Handling
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Automated Audit
def audit_accessibility(url):
results = {
"headings": check_heading_hierarchy(url), # h1 > h2 > h3, no skips
"images": check_alt_text(url), # All images have alt
"contrast": check_color_contrast(url), # 4.5:1 minimum
"keyboard": check_tab_order(url), # All interactive elements reachable
"aria": check_aria_labels(url), # Buttons, links have accessible names
"forms": check_form_labels(url), # All inputs have labels
}
return generate_report(results, level="AA")
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 · 164 lines · 37 tokens per session scan A 480850362e78
accessibility-audit is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,123 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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