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Canvas Accessibility Auditor

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vishalsachdev
canvas-accessibility-auditor

Accessibility audit and remediation for Canvas LMS courses. Scans content for WCAG-oriented issues, generates prioritized reports, guides fixes, and verifies remediation. Use when asked to "audit accessibility", "check WCAG", "fix accessibility issues", or "run accessibility review".

Overview

Publishervishalsachdev
Repositorycanvas-mcp
Skill namecanvas-accessibility-auditor
Stars
305
Forks
99
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Canvas Accessibility Auditor 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.
    https://github.com/vishalsachdev/canvas-mcp/tree/main/skills/canvas-accessibility-auditor
  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/vishalsachdev/canvas-mcp.git /tmp/canvas-mcp
mkdir -p .claude/skills
cp -r /tmp/canvas-mcp/skills/canvas-accessibility-auditor .claude/skills/canvas-accessibility-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Canvas Accessibility Auditor 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 Canvas Accessibility Auditor 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 Canvas Accessibility Auditor 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.

Canvas Accessibility Auditor

Full accessibility audit cycle for Learning Designers: scan course content, generate a prioritized report, guide remediation of fixable issues, re-scan to verify fixes, and produce an accessibility review summary.

Prerequisites

  • Canvas MCP server must be running and connected.
  • Authenticated user must have instructor, TA, or designer role in the target course.
  • For UFIXIT integration: the course must have a UFIXIT report page (generated by your institution's accessibility tool).

Steps

1. Identify Target Course

Ask the user which course to audit. Accept a course code, Canvas ID, or course name.

If not specified, prompt:

Which course would you like to audit for accessibility?

2. Scan Course Content

Run two scans in parallel if possible:

Scan A -- Direct content scan:

scan_course_content_accessibility(course_identifier, "pages,assignments")

This checks all page and assignment HTML for:

  • Images missing alt text (WCAG 1.1.1, Level A)
  • Empty headings (WCAG 2.4.6, Level AA)
  • Tables missing headers (WCAG 1.3.1, Level A)
  • Non-descriptive link text like "click here" (WCAG 2.4.4, Level A)

Scan B -- UFIXIT report (if available):

fetch_ufixit_report(course_identifier)

If the UFIXIT page exists, parse it:

parse_ufixit_violations(report_json)

3. Generate Prioritized Report

Combine results from both scans. Call format_accessibility_summary if using UFIXIT data.

Present issues sorted by priority:

## Accessibility Audit: [Course Name]

### Summary
- Content scanned: 20 pages, 15 assignments
- Total issues: 12
- Auto-fixable: 8 | Manual review needed: 4

### Level A Violations (must fix)
1. **Missing alt text** -- 5 images across 3 pages
   - Page "Week 1 Overview": 2 images
   - Page "Lab Instructions": 2 images
   - Assignment "Final Project": 1 image

2. **Tables missing headers** -- 2 tables
   - Page "Grade Scale": 1 table
   - Page "Schedule": 1 table

3. **Non-descriptive links** -- 3 instances of "click here"
   - Page "Resources": 2 links
   - Page "Week 3 Overview": 1 link

### Level AA Violations (should fix)
4. **Empty headings** -- 2 empty heading elements
   - Page "Week 5 Notes": 1 empty h3
   - Page "Midterm Review": 1 empty h2

### Manual Review Required
- Color contrast: Cannot be checked automatically (requires visual inspection)
- Video captions: Cannot be verified via API (check in Canvas media player)
- PDF accessibility: Cannot be parsed via API (use Adobe Acrobat checker)

4. Guided Remediation

For each auto-fixable issue, walk the user through the fix:

For missing alt text:

  1. Call get_page_content(course_identifier, page_url) to retrieve the HTML
  2. Identify the <img> tags without alt attributes
  3. Ask the user for alt text descriptions (or suggest based on surrounding context)
  4. Call edit_page_content(course_identifier, page_url, new_content) with corrected HTML

For non-descriptive links:

  1. Retrieve the page content
  2. Show the current link: <a href="...">click here</a>
  3. Suggest descriptive replacement: <a href="...">Download the syllabus (PDF)</a>
  4. Apply fix after user approval

For empty headings:

  1. Retrieve the page content
  2. Show the empty heading
  3. Ask: "Remove this heading, or add text to it?"
  4. Apply the chosen fix

For tables missing headers:

  1. Retrieve the page content
  2. Show the table structure
  3. Ask: "Which row/column should be headers?"
  4. Convert <td> to <th> elements and apply

Always ask for user confirmation before modifying any page.

5. Re-scan Modified Pages

After remediation, re-run the scan on modified pages only:

scan_course_content_accessibility(course_identifier, "pages")

Report: "Fixed 8/12 issues. 4 remaining require manual review."

6. Generate Accessibility Review Summary

Produce a final summary suitable for stakeholder reporting:

## Accessibility Review Summary

**Course:** [Course Name]
**Audit Date:** [date]
**Auditor:** AI-assisted audit via Canvas MCP

### Results
- Total content items scanned: 35
- Automated issues found: 12
- Issues remediated: 8
- Issues requiring manual review: 4
- Automated WCAG-oriented checks: Partial pass (manual review items remain)

### Remediation Actions Taken
- Added alt text to 5 images
- Fixed 3 non-descriptive links
- Removed 2 empty headings

### Outstanding Items
- Color contrast review needed (pages with colored text)
- Video caption verification (embedded videos)
- PDF accessibility check (uploaded PDFs)

### Recommendation
Course content meets automated WCAG 2.1 Level A criteria after remediation.
Manual review of color contrast and multimedia is recommended before publishing.

MCP Tools Used

ToolPurpose
scan_course_content_accessibilityScan pages and assignments for WCAG issues
fetch_ufixit_reportRetrieve institutional UFIXIT report
parse_ufixit_violationsExtract structured violations from report
format_accessibility_summaryFormat violations into readable report
get_page_contentRetrieve page HTML for remediation
edit_page_contentApply accessibility fixes to pages
list_coursesFind available courses

Example

User: "Run accessibility audit for CS 101"

Agent: Scans all content, generates the prioritized report.

User: "Fix the missing alt text issues"

Agent: Retrieves each affected page, asks for alt text descriptions, applies fixes, re-scans.

Limitations

  • Cannot check: Color contrast, video captions, PDF accessibility, audio descriptions, keyboard navigation
  • These are flagged as "manual review required" in every report
  • Remediation is per-page: Each fix requires a full page content read and write cycle
  • No undo: Page edits via the API do not create Canvas revision history -- back up pages before bulk fixes

Notes

  • Pairs well with canvas-course-qc for comprehensive pre-semester review.
  • Run after major content updates or course imports.
  • UFIXIT integration depends on your institution having UDOIT/UFIXIT installed.

Frequently asked questions

What does the Canvas Accessibility Auditor AI skill do?

Accessibility audit and remediation for Canvas LMS courses. Scans content for WCAG-oriented issues, generates prioritized reports, guides fixes, and verifies remediation. Use when asked to "audit accessibility", "check WCAG", "fix accessibility issues", or "run accessibility review".

Why use Canvas Accessibility Auditor on TypingMind?

Because you install it once and use it with any model. Canvas Accessibility Auditor 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 Canvas Accessibility Auditor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vishalsachdev/canvas-mcp/tree/main/skills/canvas-accessibility-auditor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Canvas Accessibility Auditor?

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 Canvas Accessibility Auditor?

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

Is the Canvas Accessibility Auditor AI skill free?

Yes. It is published on GitHub by vishalsachdev 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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