Borrowing it
Nothing to install: this file belongs to Community-Access/accessibility-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Community-Access/accessibility-agents/main/.gemini/extensions/a11y-agents/skills/desktop-a11y-testing-coach/SKILL.mdgit clone --depth 1 https://github.com/Community-Access/accessibility-agentsWrote 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/community-access/accessibility-agents/desktop-a11y-testing-coach)<a href="https://agentmods.dev/skills/community-access/accessibility-agents/desktop-a11y-testing-coach"><img src="https://agentmods.dev/badge/skills/community-access/accessibility-agents/desktop-a11y-testing-coach/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/community-access/accessibility-agents/desktop-a11y-testing-coach"><img src="https://agentmods.dev/badge/skills/community-access/accessibility-agents/desktop-a11y-testing-coach.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 | $0.00047 | $0.00358 |
| Opus 5 | $0.00023 | $0.00179 |
| Sonnet 5 | $0.00009 | $0.00072 |
| Haiku 4.5 | $0.00005 | $0.00036 |
Grade A, and why
Desktop A11y Testing Coach 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 10d 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.
What it actually says
Desktop Accessibility Testing Coach
You are a desktop accessibility testing coach -- an expert in verifying that desktop applications work correctly with assistive technology. You teach and guide testing practices for NVDA, JAWS, Narrator, VoiceOver, Accessibility Insights, and automated UIA testing frameworks.
Knowledge domains: Python Development
Core Principles
- Test with real AT. Automated checks are necessary but insufficient. Always verify with actual screen readers.
- Keyboard-first testing. Every feature must be fully operable without a mouse.
- Platform coverage. Test on the platforms your users actually use.
- Structured test plans. Follow repeatable checklists, not ad-hoc exploration.
- Regression prevention. Document test cases so accessibility doesn't regress.
Authoritative Sources
- NVDA User Guide — https://www.nvaccess.org/files/nvda/documentation/userGuide.html
- JAWS Documentation — https://www.freedomscientific.com/training/jaws/
- Accessibility Insights for Windows — https://accessibilityinsights.io/docs/windows/overview/
- VoiceOver User Guide (macOS) — https://support.apple.com/guide/voiceover/welcome/mac
- UI Automation Testing — https://learn.microsoft.com/en-us/windows/win32/winauto/accessibility-testingtools
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.
- 10d ago First seen · 31 lines · 47 tokens per session scan A 77efc3ca9c87
Desktop A11y Testing Coach is a skill published in the GitHub repository Community-Access/accessibility-agents (405 stars, last pushed 29d ago), licensed MIT. It adds 47 tokens to every session and 358 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
Accessibility Auditor
Comprehensive WCAG 2.1 AA compliance testing combining automated axe-core scans with manual keyboard navigation, screen reader compatibility, and focus management verification.
Accessibility A11y Enhanced
Comprehensive WCAG compliance and accessibility testing covering ARIA, keyboard navigation, screen readers, color contrast, and automated a11y validation.
a11y-audit
Runs automated accessibility scans with axe-core, pa11y, Lighthouse, or eslint-plugin-jsx-a11y. Interprets results, prioritizes violations, and generates fix recommendations. Use when asked to audit, scan, or check accessibility of a page, component, or codebase.
testing-strategy
Guides accessibility testing strategy — what to test automatically vs manually vs with assistive technology. Provides keyboard testing checklists, screen reader testing basics, and acceptance criteria templates. Use when planning testing, writing acceptance criteria, or deciding how to verify accessibility.
accessibility
Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries…
Axe-core Accessibility Testing
Accessibility testing skill using axe-core and Playwright for automated WCAG 2.1 compliance auditing, custom rules, and accessibility reporting.