Paper Signal Audit

Community

jiahuiqu17

paper-signal-audit

Inspect and diagnose minimal-zine posters, photo restyles, editorial images, and social-card series for subject preservation, factual and text integrity, composition, paper realism, print-process coherence, color signal, anti-AI craft, platform fit, and series continuity. Use when the user asks whether an image has高级感, feels AI-generated, matches Paper Signal or gc-minimal-zine quality, needs improvement, or should be regenerated. Returns evidence-based findings and a targeted correction prompt; regenerates only when the user also asks for a fix.

Overview

Publisher	jiahuiqu17
Repository	`paper-signal`
Skill name	`paper-signal-audit`
Stars	108
Forks	1
Bundled files	1
License	MIT
Links	Skill source Repository

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 jiahuiqu17 on GitHub. Read the source before you install it.

Installation

Install the Paper Signal Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/jiahuiqu17/paper-signal/tree/main/skills/paper-signal-audit
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.

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/jiahuiqu17/paper-signal.git /tmp/paper-signal
mkdir -p .claude/skills
cp -r /tmp/paper-signal/skills/paper-signal-audit .claude/skills/paper-signal-audit

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Signal Audit 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 Paper Signal Audit 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 Paper Signal Audit 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.

Paper Signal Audit

Judge the image that exists, not the prompt that intended it.

Load the audit basis

Read from ../paper-signal/:

references/subject-routing.md
references/visual-language.md
references/quality-contract.md
references/restyle-contract.md for edits

Read the project prompt, manifest, preservation record, outline, and evidence ledger when available.

Audit workflow

Inspect every local image with view_image at original detail.
Identify the delivery route, subject route, and intended composition mode from artifacts; infer them explicitly when artifacts are missing.
Compare the raster against protected subject details, exact text, factual claims, and requested aspect ratio.
Inspect at full size for identity, geometry, paper, image integration, type, print defects, and AI artifacts.
Inspect at thumbnail size for subject recognition, chromatic signal, hierarchy, and series recognition.
For a series, compare the whole sequence for material continuity, composition variety, pacing, and cloned-layout drift.
Rank findings by severity and give one correction path per failed image.

Report format

Lead with the verdict:

Pass — ready to use
Pass with refinements — usable; small craft changes would improve it
Regenerate — one or more blocking failures

Then report only observed issues:

Severity	Image	Observation	Why it matters	Targeted correction

Separate:

blocking: subject drift, false/wrong text, wrong ratio, unreadable essential content, missing output
major: generic template composition, weak subject scale, incoherent print process, pseudo-language, muddy hierarchy
minor: slightly weak signal, texture repetition, edge or safe-zone refinement

Do not praise or criticise with unsupported adjectives. Point to visible evidence.

Anti-AI diagnosis

Check specifically for:

meaningless microtext or faux Asian characters
generic birds, moons, stairs, doors, flowers, and registration marks unrelated to content
uniformly applied vintage noise
beige template plus clean modern typography
perfect vector spacing under fake paper texture
waxy faces, altered identity, impossible hands, warped buildings, or invented product details
random collage fragments with no visual thesis
empty space that creates no direction or tension

Correction policy

Correct one failure class per regeneration. Preserve accepted subject features, text, material choices, and crop. Save the correction prompt and output as versioned siblings.

When the user asks only for review or diagnosis, stop after the report and correction prompt. Generate or edit the image only when the user explicitly asks to fix, remake, or produce it.

Never patch generated text or composition with code.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB

Frequently asked questions

What does the Paper Signal Audit AI skill do?

Why use Paper Signal Audit on TypingMind?

Because you install it once and use it with any model. Paper Signal Audit 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 Paper Signal Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jiahuiqu17/paper-signal/tree/main/skills/paper-signal-audit. 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 Paper Signal Audit?

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 Paper Signal Audit?

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

Is the Paper Signal Audit AI skill free?

Yes. It is published on GitHub by jiahuiqu17 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Paper Signal Audit

Load the audit basis

Audit workflow

Report format

Anti-AI diagnosis

Correction policy

Bundled files

Frequently asked questions

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