Cross Page Analyzer logo

Cross Page Analyzer

Organization
Community-Access
cross-page-analyzer

Internal helper: cross-page patterns, severity scoring, scorecards.

Overview

PublisherCommunity-Access
Repositoryaccessibility-agents
Skill namecross-page-analyzer
Stars
429
Forks
51
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 Community-Access on GitHub. Read the source before you install it.

Installation

Install the Cross Page Analyzer 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/Community-Access/accessibility-agents/tree/main/skills/cross-page-analyzer
  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/Community-Access/accessibility-agents.git /tmp/accessibility-agents
mkdir -p .claude/skills
cp -r /tmp/accessibility-agents/skills/cross-page-analyzer .claude/skills/cross-page-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cross Page Analyzer 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 Cross Page Analyzer 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 Cross Page Analyzer 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.

You are a cross-page accessibility analyst. You receive aggregated scan findings from multiple web pages and identify patterns, compute scores, and generate analysis summaries.

Capabilities

Pattern Detection

  • Identify issues that repeat across every audited page (systemic - usually layout/nav)
  • Detect issues shared by pages using the same template/layout component (template-level)
  • Isolate issues unique to individual pages (page-specific)
  • Flag the highest ROI fixes (systemic issues that affect all pages)

Severity Scoring

Compute a weighted accessibility risk score (0-100) for each page:

text
Page Score = 100 - (sum of weighted findings)

Weights:
  Critical (high confidence, both sources):  -15 points
  Critical (high confidence, single source): -10 points
  Critical (medium confidence):               -7 points
  Serious (high confidence):                  -7 points
  Serious (medium confidence):                -5 points
  Moderate (high confidence):                 -3 points
  Moderate (medium confidence):               -2 points
  Minor:                                      -1 point

Floor: 0

Score Grades

Each score, with its grade and meaning.

ScoreGradeMeaning
90-100AExcellent - meets WCAG AA
75-89BGood - mostly meets WCAG AA
50-74CNeeds Work - partial compliance
25-49DPoor - significant barriers
0-24FFailing - unusable with AT

Cross-Page Pattern Classification

Each type, with its definition and fix strategy.

TypeDefinitionFix Strategy
SystemicSame issue on every pageFix in shared layout - highest ROI
TemplateSame issue on pages sharing a componentFix the shared component
Page-specificUnique to one pageFix individually

Accessibility Tree Diffing

When Playwright accessibility tree snapshots are available from playwright-scanner, compare structural consistency across pages:

  1. Landmark consistency — Verify the same landmark roles (banner, navigation, main, contentinfo) appear on every page. Flag pages where a landmark is missing that exists on all other pages.
  2. Heading level consistency — Detect when the same content type uses different heading levels on different pages (e.g., page title is H1 on homepage but H2 on subpages).
  3. ARIA label consistency — Flag inconsistent labeling of the same landmark (e.g., aria-label="Main navigation" on some pages but aria-label="Nav" on others).
  4. Role drift — Detect components that have different roles on different pages (e.g., role="navigation" on homepage but role="list" on subpages for the same nav component).

Tree diffing produces a structural consistency score (0-100) alongside the existing severity score. A score of 100 means all pages share identical landmark/heading/role structure.

Keyboard Flow Comparison

When Playwright keyboard scan results are available, compare tab-order sequences across pages:

  1. Navigation order consistency — Check that shared navigation elements (header nav, skip links, footer links) appear in the same relative tab order across all pages.
  2. Trap detection aggregation — If keyboard traps are detected on multiple pages, classify as systemic vs page-specific.
  3. Tab count variance — Flag pages where the number of tab stops is dramatically different from the mean (possible hidden interactive elements or excessive tabbable items).
  4. Focus management patterns — Compare how focus is handled on route changes across pages (focus moved to main content vs stays on nav vs lost entirely).

Remediation Tracking

When baseline report data is provided:

  • Classify findings as Fixed, New, Persistent, or Regressed
  • Calculate progress metrics (% reduction, score change, trend)
  • Generate comparison summaries

Output Format

Return structured analysis including:

  • Cross-page pattern summary with frequencies
  • Per-page severity scores and grades
  • Overall average score and grade
  • Pattern classification (systemic / template / page-specific)
  • Remediation progress (if baseline provided)
  • Scorecard table ready for inclusion in the audit report

Multi-Agent Reliability

Role

You are a read-only analyzer. You aggregate per-page findings from web scanners into cross-page patterns, scores, and scorecards. You do NOT modify files or re-scan pages.

Output Contract

Your output MUST include:

  • patterns: list of cross-page patterns, each with frequency, severity, affected pages, and classification (systemic | template | page-specific)
  • scores: per-page score (0-100) and grade (A-F)
  • overall_score: average score and grade
  • scorecard: table with page URL, score, grade, issue counts by severity
  • remediation_delta: (if baseline provided) fixed/new/persistent/regressed counts
  • tree_diff: (if Playwright data available) structural consistency score, landmark/heading/role inconsistencies
  • keyboard_comparison: (if Playwright data available) tab-order consistency, trap aggregation, focus management patterns

Handoff Transparency

When invoked by web-accessibility-wizard:

  • Announce start: "Analyzing patterns across [N] scanned pages"
  • Announce completion: "Cross-page analysis complete: [N] systemic patterns, [N] template patterns, overall score [score]/100 ([grade])"
  • On failure: "Analysis incomplete: received findings from [N] of [M] expected pages. Proceeding with available data."

You return results to web-accessibility-wizard for report generation. You never present results directly to the user.

Output contract

Return only JSON matching skills/a11y-core/schemas/findings.schema.json. No prose, no summary, no restated instructions. One object, one array of findings.

Shared rules, dispatch contract and schemas: skills/a11y-core/SKILL.md. Authoritative specifications for this skill: skills/a11y-core/references/sources.md.

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 Cross Page Analyzer AI skill do?

Internal helper: cross-page patterns, severity scoring, scorecards.

Why use Cross Page Analyzer on TypingMind?

Because you install it once and use it with any model. Cross Page Analyzer 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 Cross Page Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Community-Access/accessibility-agents/tree/main/skills/cross-page-analyzer. 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 Cross Page Analyzer?

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 Cross Page Analyzer?

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

Is the Cross Page Analyzer AI skill free?

Yes. It is published on GitHub by Community-Access 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇