AccessLint: Accessibility Scan

AccessLint/skills

Locate mechanically detectable accessibility issues on one rendered page, with DOM selectors, evidence and source locations when available.

Conteúdo atualizado:

Ver no GitHub
npx skills add AccessLint/skills
120Estrelas15ForksÚltimo push do repositório

Sobre este Skill

Locate mechanically detectable accessibility issues on one rendered page, with DOM selectors, evidence and source locations when available.

Por que escolhemos

Scan a live page and produce a worklist without editing source.

No que ajuda

  • Scan a live page and produce a worklist without editing source.
  • Retain evidence and distinguish automated findings from checks needing a person.

Workflow and expected result

  1. Step 1

    Resolve the target URL, scope and relevant baseline or assessment inputs.

  2. Step 2

    Scan a live page and produce a worklist without editing source.

  3. Step 3

    Record findings, verification limits and the next action for each unresolved issue.

Reviewed upstream task instructions

Source review

Reviewed the task instructions and repository inventory at commit 2e9d73366783. This listing describes the upstream workflow; it does not claim that a full product implementation or accessibility audit was executed during intake.

Reviewed upstream task instructionsOfficial repository and installation guide

Antes de usar

  • A coding assistant with local file access and Node.js for the upstream installer. Review the installed instructions before use.
  • Node.js, network access and Chrome are needed by @accesslint/cli and @accesslint/chrome. The page must be running; source mapping depends on available framework metadata.
  • A successful automated check does not certify full accessibility conformance.

Agentes compatíveis

Claude CodeCodex CLICursorGitHub CopilotDeepSeek HarnessGemini CLIGrok BuildHermes AgentOpenCodePiKiroRovo DevQoderMistral VibeGoogle Antigravity

Fontes e referências

As fontes originais estão nos links abaixo. As sugestões práticas são recomendações editoriais do SkillHey.