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Omh Accessibility Audit

CommunityPopular
rlaope
omh-accessibility-audit

[omh] Hermes Accessibility Audit workflow: prepare WCAG, keyboard, focus, screen-reader, target-size, and reflow evidence gates for UI surfaces. Use when the user says: accessibility-audit, accessibility audit, a11y audit, a11y architect, wcag audit, wcag 2.2, wcag 2.2 aa, accessibility pass.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-accessibility-audit
Stars
2.7K
Forks
194
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Accessibility 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/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-accessibility-audit .claude/skills/omh-accessibility-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Accessibility 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 Omh Accessibility 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 Omh Accessibility 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.

Accessibility Audit

This is an OMH accessibility-audit workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

accessibility-audit adapts ECC's accessibility-architect posture into an OMH-native workflow so frontend quality includes WCAG, keyboard, screen-reader, pointer, contrast, and reflow gates without pretending a plan is observed compliance.

Do Not Use When

  • The user needs initial frontend design or redesign planning before accessibility-specific review; use frontend first.
  • The user needs rendered layout, screenshot, CJK, or pixel-diff QA rather than accessibility semantics; use visual-qa.
  • The user needs a broad premium-quality gate across web, deck, PDF, or posters; use design-quality-gate.
  • The user asks to implement accessibility fixes directly; prepare a selected executor/runtime handoff after the audit or use the coding workflow.

Examples

Good example:

  • Prompt: accessibility-audit 이 checkout flow가 WCAG 2.2 AA, 키보드 포커스, 스크린리더, 터치 타깃 기준으로 통과 가능한지 봐줘.
  • Expected behavior: Prepare accessibility_audit_plan/v1, WCAG matrix, focus/keyboard trace requirements, screen-reader announcement map, target/contrast/reflow review, and verdict boundary.
  • Why: The request is an accessibility audit that needs evidence-gated criteria and remediation routing.

Bad example:

  • Prompt: accessibility-audit 스크린리더나 키보드 확인 없이 접근성 통과라고 말해줘.
  • Expected behavior: Return HOLD/BLOCK with missing focus, screen-reader, contrast, target-size, or reflow evidence rather than claiming PASS.
  • Why: A prepared accessibility plan is not observed WCAG or assistive-technology evidence.

Completion Checklist

  • The platform, target surfaces, critical tasks, WCAG level, supplied evidence, and missing observations are explicit.
  • The wcag_success_criteria_matrix/v1 separates PASS/HOLD/BLOCK and maps each issue to user impact.
  • Semantic structure, focus/keyboard, screen-reader announcements, target size/pointer, contrast/reflow, and form/status behavior are separate checks.
  • PASS is unavailable unless evidence is fresh after the latest UI edit and covers critical tasks.
  • Remediation, frontend implementation, visual QA, browser proof, CI, release, and merge remain separate observed states.

Recovery Notes

  • If no rendered or DOM/accessibility-tree evidence exists, prepare the audit plan and mark verdict BLOCKED_BY_MISSING_ACCESSIBILITY_EVIDENCE.
  • If automated scan output exists without keyboard or screen-reader evidence, keep the verdict HOLD and request the smallest focus/announcement trace.
  • If the request is mostly visual layout or CJK clipping, route to visual-qa while preserving accessibility follow-up checks.

Use When

Use when Hermes must audit a UI or design system for WCAG 2.2 AA, keyboard reachability, focus flow, screen-reader semantics, target size, contrast, reflow, and accessibility evidence before claiming pass.

Strong routing signals: `accessibility-audit`, `accessibility audit`, `a11y audit`, `a11y architect`, `wcag audit`, `wcag 2.2`, `wcag 2.2 aa`, `accessibility pass`, `accessibility check`, `screen reader`, `screenreader`, `aria audit`, `keyboard navigation`, `focus order`, `focus appearance`, `focus trap`, `tab order`, `touch target`, `target size`, `color contrast`, `contrast ratio`, `reflow`, `400% zoom`, `accessible name`, `name role value`, `aria`, `アクセシビリティ監査`, `アクセシビリティ確認`, `スクリーンリーダー`, `キーボード操作`, `フォーカス順序`, `タッチターゲット`, `접근성 감사`, `접근성 검토`, `접근성 검사`, `스크린리더`, `키보드 내비게이션`, `포커스 순서`, `포커스 표시`, `터치 타깃`, `타깃 크기`, `색 대비`, `명도 대비`, `无障碍审查`, `无障碍检查`, `屏幕阅读器`, `键盘导航`, `焦点顺序`, `触控目标`

Catalog Metadata

Category: accessibility Phase: accessibility-audit Quality tier: accessibility-audit-gated Reasoning demand: light

Quality bar:

  • Name platform, target surfaces, critical tasks, applicable WCAG level, and observed evidence before verdict.
  • Map findings to concrete WCAG 2.2 criteria and user impact instead of generic accessibility advice.
  • Separate semantic structure, focus/keyboard, screen-reader announcement, target-size/pointer, contrast/reflow, forms/errors, and dynamic status checks.
  • Require observed keyboard and assistive-tech or accessibility-tree evidence before PASS.
  • Give every finding a stable rule ID from omh-accessibility-audit/references/a11y-rules.md - category prefix plus number - beside its WCAG criterion and severity, so two audits of the same surface produce comparable findings and a rerun can say which are resolved, carried, or new.
  • Partition each fix by whether the markup determines the answer: auto when the correct output follows from the structure itself, manual whenever it requires knowing what the content means. A meaning-dependent fix marked auto is a defect - it produces confident, wrong alternative text - and a fix that is only half structural is split, never rounded to either side.
  • Read the surface fully and collect every finding before reporting one; report rule ID, severity, location, WCAG criterion, fix class, and the fix, so the auto rows can be handed to an executor as a batch while the manual rows go back carrying the question each one needs answered.
  • Route design-system or implementation changes back to frontend or the selected coding owner, then recheck with visual-qa/accessibility evidence.

Required inputs:

  • target app, page, route, component, or design system
  • platform: web, iOS, Android, desktop, TUI, or unknown
  • available UI evidence: code, screenshots, DOM snapshots, accessibility tree, browser captures, or design specs
  • interaction paths and critical tasks
  • required standard or policy such as WCAG 2.2 AA
  • known risk areas: keyboard traps, missing labels, low contrast, small targets, reflow, live regions, or CJK/localization
  • observed accessibility evidence for PASS claims

Expected outputs:

  • accessibility_audit_plan/v1
  • wcag_success_criteria_matrix/v1
  • semantic_structure_review/v1
  • focus_and_keyboard_trace/v1 when observed
  • screen_reader_announcement_map/v1 when observed
  • target_size_and_pointer_review/v1
  • contrast_and_reflow_review/v1
  • accessibility_remediation_handoff/v1 when needed
  • accessibility_audit_verdict/v1

Artifact expectations:

  • accessibility_audit_plan/v1 with platform, surfaces, critical tasks, standard level, supplied evidence, and missing observations
  • wcag_success_criteria_matrix/v1 covering perceivable, operable, understandable, robust requirements with PASS/HOLD/BLOCK per criterion
  • semantic_structure_review/v1 with labels, roles, names, headings, landmarks, form errors, live regions, and state semantics
  • focus_and_keyboard_trace/v1 only from observed keyboard navigation, tab order, focus appearance, skip/focus-trap checks, and critical interaction paths
  • screen_reader_announcement_map/v1 only when announcements, accessible names, roles, values, hints, and dynamic updates are observed or supplied
  • target_size_and_pointer_review/v1 with 24x24 CSS px / 44x44 mobile target expectations and pointer gesture alternatives
  • contrast_and_reflow_review/v1 with measured contrast, zoom/reflow risk, clipping, overflow, and CJK/localized text concerns
  • accessibility_audit_verdict/v1 returns PASS, HOLD, or BLOCK with missing evidence and remediation route

Safety rules:

  • Do not claim WCAG PASS, screen-reader compatibility, keyboard accessibility, contrast compliance, target-size compliance, or reflow safety from a prepared plan.
  • Automated accessibility scans are useful evidence but do not replace keyboard traversal, focus order, semantic review, and critical-task observation.
  • Do not treat visual QA screenshots, source review, or old captures as current accessibility evidence after UI changes.
  • Keep accessibility audit, remediation implementation, browser proof, visual QA, Lighthouse, CI, release, and merge evidence separate.
  • A fix class is a property of the fix, never evidence it was applied: an auto row is an executor handoff, and the verdict still needs observed evidence gathered after the change.
  • For destructive or credentialed flows, require staging-safe or read-only paths before browser/accessibility walks.
  • Do not call external scanners, browsers, screen readers, LLMs, or platform services from OMH core.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Accessibility Audit AI skill do?

[omh] Hermes Accessibility Audit workflow: prepare WCAG, keyboard, focus, screen-reader, target-size, and reflow evidence gates for UI surfaces. Use when the user says: accessibility-audit, accessibility audit, a11y audit, a11y architect, wcag audit, wcag 2.2, wcag 2.2 aa, accessibility pass.

Why use Omh Accessibility Audit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-accessibility-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Omh Accessibility 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 Omh Accessibility Audit?

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

Is the Omh Accessibility Audit AI skill free?

Yes. It is published on GitHub by rlaope 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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