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Wcag Audit Patterns

Checked
rmyndharis·vlatest·frontend·MIT

Helps with auditing, patterns as part of agent workflows workflows.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add rmyndharis/antigravity-skills --skill wcag-audit-patterns -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

rmyndharis

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned rmyndharis/antigravity-skills at 3eff4af253.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Scanned
2 files, 546 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides patterns for conducting WCAG (Web Content Accessibility Guidelines) audits. It helps identify common accessibility issues and suggests remediation strategies based on established best practices. The tool aims to streamline the audit process, ensuring websites and digital content are more inclusive and accessible to users with disabilities.

When to use it

  • Website Audits: Evaluate a website's compliance with WCAG guidelines before launch or during maintenance.
  • Content Creation Review: Assess newly created web content (documents, videos, interactive elements) for accessibility issues.
  • Accessibility Remediation Planning: Generate a prioritized list of fixes based on audit findings.
  • Training & Education: Use the patterns as examples to educate developers and designers about accessible design principles.

Key capabilities

  • WCAG pattern identification
  • Remediation suggestions
  • Audit process streamlining
  • Accessibility best practices adherence

Example prompts

  • "Perform a WCAG audit on this webpage: [URL]"
  • "What are the common accessibility issues with image alt text?"
  • "Suggest remediation steps for insufficient color contrast."

Tips & gotchas

The skill's effectiveness depends on providing clear and complete information about the content being audited. While it provides suggestions, a human expert should always review the results to ensure accuracy and context-specific solutions.