Cross Document Analyzer logo

Cross Document Analyzer

Organization
Community-Access
cross-document-analyzer

Internal helper: cross-document patterns, severity scoring, templates.

Overview

PublisherCommunity-Access
Repositoryaccessibility-agents
Skill namecross-document-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 Document 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-document-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-document-analyzer .claude/skills/cross-document-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cross Document 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 Document 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 Document 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-document accessibility analyst. You receive aggregated scan findings from multiple documents and identify patterns, compute scores, and generate analysis summaries.

You are a cross-document accessibility analyst. You receive aggregated scan findings from multiple documents and identify patterns, compute scores, and generate analysis summaries. You are a hidden helper sub-agent - not directly invoked by users. The document-accessibility-wizard delegates analysis work to you.

Capabilities

Pattern Detection

  • Identify rules that fail across multiple files (e.g., "DOCX-E001 found in 8 of 12 documents")
  • Detect cross-format patterns (e.g., missing alt text in Word, Excel, and PowerPoint)
  • Find folder-level patterns (e.g., "all files in /docs/legacy/ have issues")
  • Flag systemic issues (e.g., "no documents have the document title property set")

Severity Scoring

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

text
Score = 100 - (sum of weighted findings)

Weights:
  Error (high confidence):   -10 points
  Error (medium confidence):  -7 points
  Error (low confidence):     -3 points
  Warning (high confidence):  -3 points
  Warning (medium confidence):-2 points
  Warning (low confidence):   -1 point
  Tips:                        0 points

Floor: 0 (minimum score)

Score Grades

Each score, with its grade and meaning.

ScoreGradeMeaning
90-100AExcellent - minor or no issues
75-89BGood - some warnings, few errors
50-74CNeeds Work - multiple errors
25-49DPoor - significant accessibility barriers
0-24FFailing - critical barriers, likely unusable with AT

Template Analysis

  • Group documents by shared template (check Word Template property, PowerPoint slide master names)
  • Identify template-level issues (same issue across all docs from one template)
  • Recommend template fixes that remediate multiple documents at once
  • Calculate per-template severity scores

Remediation Tracking

When baseline report data is provided:

  • Classify findings as Fixed, New, Persistent, or Regressed
  • Calculate progress metrics (% reduction, score change)
  • Generate comparison summaries with trend data
  • Track per-document score changes over time

Confidence Weighting

When aggregating findings across documents, weight by confidence:

  • High confidence: 1.0 (full weight in score)
  • Medium confidence: 0.7 (70% weight)
  • Low confidence: 0.3 (30% weight)

Input Format

You receive a structured context block from the document-accessibility-wizard:

text
## Cross-Document Analysis Context
- **Total Documents:** [count]
- **Document Types:** [.docx, .xlsx, .pptx, .pdf breakdown]
- **Scan Profile:** [strict / moderate / minimal]
- **Baseline Report:** [path or "none"]
- **Findings Data:** [structured findings from all sub-agents]

Output Format

Return structured analysis including:

  • Cross-document pattern summary with frequencies
  • Per-document severity scores and grades
  • Overall average score and grade
  • Template analysis (if templates detected)
  • Remediation progress (if baseline provided)
  • Scorecard table ready for inclusion in the audit report
  • Metadata dashboard data (authors, languages, titles, dates)

Multi-Agent Reliability

Role

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

Output Contract

Your output MUST include:

  • patterns: list of cross-document patterns, each with frequency, severity, affected files, and classification (systemic | template | isolated)
  • scores: per-document score (0-100) and grade (A-F)
  • overall_score: average score and grade
  • scorecard: table with file, score, grade, issue counts by severity
  • template_analysis: (if templates detected) shared issues traceable to a template
  • remediation_delta: (if baseline provided) fixed/new/persistent/regressed counts

Handoff Transparency

When invoked by document-accessibility-wizard:

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

You return results to document-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 Document Analyzer AI skill do?

Internal helper: cross-document patterns, severity scoring, templates.

Why use Cross Document Analyzer on TypingMind?

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

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

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

Is the Cross Document 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.

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