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Audit Reference Originality

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MengTo
audit-reference-originality

Audit a website or digital experience against its supplied source references for originality and plagiarism risk. Use when Codex must compare current or historical site output with reference pages, capture packs, screenshots, copy, brands, numbers, images, assets, videos, layouts, motion, or code; raise evidence-backed red flags; distinguish common visual grammar from distinctive copying; and propose concrete fixes without making unsupported legal claims.

Overview

PublisherMengTo
RepositorySkills
Skill nameaudit-reference-originality
Stars
6.1K
Forks
717
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Audit Reference Originality 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.
  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/MengTo/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/agent-skills/codex/audit-reference-originality .claude/skills/audit-reference-originality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Audit Reference Originality 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 Audit Reference Originality 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 Audit Reference Originality 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.

Audit Reference Originality

Compare the shipped experience with the complete reference corpus. Treat the audit as an evidence exercise, not a vibe check.

Preserve the audit boundary

  • Audit only unless the user also asks for fixes.
  • Call findings originality risks, overlaps, or red flags; do not declare legal plagiarism from visual similarity alone.
  • Pair every red flag with exact current-site evidence and exact reference evidence.
  • Keep fact, inference, and unknown access separate.
  • Do not clear a site after checking only its homepage screenshot.

1. Build the source registry

Start with the materials explicitly supplied in the task or recorded by the project:

  • manifests, prompt packs, and originality matrices
  • representative stills, full-page captures, and section crops
  • MP4s and extracted motion frames
  • reference URLs and named creators
  • source brand assets, copy, numbers, screenshots, and downloads
  • project briefs, IMAGE_CREDITS.md, licenses, and attribution files

Prefer local evidence captured at the time of the brief. Use a live reference only to fill a real gap, because it may have changed. Record each reference's path or URL, role, date when known, and which categories it can prove.

Stop and report an access gap when a promised reference is missing. Do not silently reduce a full-page or motion audit to one cover image.

2. Inventory the current site and its history

Inspect:

  • rendered text, metadata, navigation, calls to action, legal copy, and hidden accessible labels
  • brand names, wordmarks, logos, icons, people, companies, URLs, and product/interface data
  • prices, metrics, dates, counts, percentages, package names, and repeated proof claims
  • every rendered image, background, texture, screenshot, font, logo, icon, audio file, and downloadable asset
  • every video, poster, frame sequence, shot order, transition, duration, and playback treatment
  • layout hierarchy, section order, distinctive compositions, typography behavior, motion grammar, shaders, cursor effects, and interaction sequences
  • current source, built output when available, asset provenance, and repository history

Do not trust filenames as proof of originality. Inspect the bytes, visible result, and history. Search renamed, deleted, and replaced files with git log, git show, git log -S, and git log --all --name-status.

Run the deterministic inventory helper when local files are available:

bash
python <skill-dir>/scripts/build_evidence_inventory.py \
  --site <site-root> \
  --reference <reference-file-or-directory> \
  --reference <another-reference> \
  --output <temporary-output.json>

The helper finds current and historical exact-file matches, suspicious basename reuse, normalized text overlap, and repeated number tokens. Treat its output as leads for human review, not an automatic verdict.

3. Compare category by category

Read references/audit-rubric.md before judging findings.

Audit at least these categories:

  1. Text — headlines, body copy, labels, CTAs, captions, legal text, alt text, metadata, and decorative wording.
  2. Brands — names, marks, wordmarks, proprietary icons, people, companies, partnerships, URLs, and distinctive verbal identity.
  3. Numbers — metrics, percentages, prices, dates, counts, plan structures, durations, and interface values.
  4. Images — exact files, crops, generated derivatives, screenshots, people, poses, objects, signature compositions, and color treatment.
  5. Assets — fonts, icons, logos, textures, mockups, downloads, code bundles, and third-party media with unclear provenance.
  6. Videos — exact files, frames, shots, timing, camera moves, edit rhythm, transitions, overlays, posters, and audio.
  7. Structure and motion — section order, unusual layout devices, pinned sequences, cursor interactions, shaders, and combinations of signature elements.
  8. History — copied material that was later renamed, recolored, cropped, hidden, deleted, or replaced.

Common patterns such as black backgrounds, large sans-serif type, ordinary pricing tables, standard fade-ins, or a conventional footer are not red flags by themselves. Escalate combinations of distinctive elements or direct evidence.

4. Triangulate every red flag

For each candidate:

  1. Identify the current-site artifact and location.
  2. Identify the exact reference artifact and location.
  3. State the observable overlap without guessing intent.
  4. State what differs.
  5. Assign severity using the rubric.
  6. Propose the smallest fix that breaks the overlap while preserving the site's goal.

Use hashes for exact files, normalized excerpts for copy, side-by-side crops for imagery, and matched timestamps or frames for video. A source-brand string found only in a test that explicitly forbids it is not a shipped-copy violation; explain context.

5. Propose fixes

Prefer concrete replacements:

  • rewrite source-like copy from the new brand's audience, offer, and vocabulary
  • replace names, URLs, logos, people, metrics, dates, plan names, and legal text
  • regenerate or license new imagery with a materially different subject, composition, and motif arrangement
  • replace copied assets and document provenance
  • re-cut videos with new shots, timing, transitions, overlays, and audio
  • reorder or redesign distinctive section and motion sequences
  • remove stale source material from current output and, when required, repository history or published artifacts

Do not recommend cosmetic recoloring as a fix for copied identity, copy, media, or composition.

6. Report the result

Lead with one verdict:

  • Clear in checked scope
  • Clear with low-risk similarities
  • Changes recommended
  • Block release
  • Blocked by missing evidence

Then provide:

  1. checked source registry
  2. red-flag table ordered by severity
  3. category pass list
  4. history findings
  5. access gaps and unproven areas
  6. prioritized fix plan

Include a row even when a high-risk category could not be checked. Never turn missing evidence into a pass.

Completion checks

  • Every supplied reference form was inspected.
  • Rendered output and source were both checked.
  • Text, brands, numbers, images, assets, videos, structure/motion, and history were covered.
  • Every red flag cites two evidence locations.
  • Exact matches are distinguished from stylistic similarity.
  • Proposed fixes replace the copied element rather than disguising it.
  • The report states what remains unverified.

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 Audit Reference Originality AI skill do?

Audit a website or digital experience against its supplied source references for originality and plagiarism risk. Use when Codex must compare current or historical site output with reference pages, capture packs, screenshots, copy, brands, numbers, images, assets, videos, layouts, motion, or code; raise evidence-backed red flags; distinguish common visual grammar from distinctive copying; and propose concrete fixes without making unsupported legal claims.

Why use Audit Reference Originality on TypingMind?

Because you install it once and use it with any model. Audit Reference Originality 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 Audit Reference Originality in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MengTo/Skills/tree/main/agent-skills/codex/audit-reference-originality. 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 Audit Reference Originality?

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 Audit Reference Originality?

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

Is the Audit Reference Originality AI skill free?

Yes. It is published on GitHub by MengTo 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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