Radiology Prereview

CommunityPopular

huang-sir1

radiology-prereview

Run a rigorous pre-submission mock peer review of an imaging-AI / radiomics / radiogenomics manuscript — simulate the methods, statistics, reporting-guideline, figure, citation/claim-verification, and data-sharing reviewer a top journal would assign, and surface the issues that cause desk-reject or major revision before submission. Use when the user wants a mock review, pre-submission audit, "投稿前预审/模拟审稿", "find the holes before a reviewer does", a two-pass abstract/figure/table claim audit, or a readiness check. Returns a reviewer-style report with Blocker / Major / Minor issues, each tied to the manuscript location and the reporting-guideline or methodological risk, plus an editor-style recommendation and a prioritised fix order. Never fabricates compliance or papers over a real weakness.

Overview

Publisher	huang-sir1
Repository	`radiology-skills`
Skill name	`radiology-prereview`
Stars	1.7K
Forks	17
Bundled files	6
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.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by huang-sir1 on GitHub. Read the source before you install it.

Installation

Install the Radiology Prereview 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.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/modules/radiology-prereview

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/huang-sir1/radiology-skills.git /tmp/radiology-skills
mkdir -p .claude/skills
cp -r /tmp/radiology-skills/radiology-skills/modules/radiology-prereview .claude/skills/radiology-prereview

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

Use it in TypingMind

Enable Radiology Prereview 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 Radiology Prereview 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 Radiology Prereview 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.

Pre-submission Mock Review

Use this skill to be the harshest fair reviewer before the real one is. It reads the manuscript the way a methods-literate Radiology/Lancet-DH/Nature-Medicine reviewer would, finds the dealbreakers, and returns a reviewer-style report you can act on — so issues are fixed on your terms, not surfaced in a rejection.

Core stance

Adversarial but on the author's side. Hunt for the weakness a reviewer will weaponise, then hand back the fix — not just the criticism.
Dealbreakers first. No patient-level split, data leakage, no external validation, undefined labels, unclear segmentation, incomplete statistics, overclaiming — these decide the outcome. Triage them before cosmetics.
Map to the guideline. Tie each issue to the specific CLAIM/CLEAR/TRIPOD+AI/STARD/IBSI item or methodological risk a reviewer would cite (→ radiology-reporting).
Check the claims against the evidence. Does the abstract/Discussion overstate AUC, correlation, or retrospective results? Flag every claim the data don't support.
Honest readiness verdict. Give an editor-style recommendation (ready / minor / major / not yet) with the reasons — don't reassure.
Integrity. Never invent compliance, never wave through a real weakness to be encouraging.

When to use

"Mock-review my paper before I submit." / "投稿前帮我模拟审稿、做预审。"
"Find the holes a reviewer will find."
"Is this ready for [target journal], or what must I fix first?"
After drafting, before radiology-journal selection and submission.

When to open extra files

File	Open when
references/review-dimensions.md	The full set of dimensions to review (design, data, labels, leakage, stats, reporting, figures, claims, sharing)
references/dealbreakers.md	The hard issues that trigger desk-reject / major revision, with how to detect and fix each
references/review-report-format.md	The reviewer-report + editor-recommendation output structure
references/pre-submission-hard-gates.md	Final submission readiness audit, rejected-paper rescue, contribution map, reviewer objection register, or when deciding whether a paper is truly ready
references/ai-radiogenomics-pitfall-audit.md	Imaging-AI, foundation-model, VLM, radiomics, deep radiomics, or radiogenomics manuscripts need a targeted audit for leakage, external validation, site/scanner confounding, superficial XAI, weak clinical utility, or mechanism overclaim
references/claim-verification-gate.md	Submission-facing abstract, Key Results, figure legend, table, graphical abstract, novelty, comparison, and numerical claims need two-pass extraction and verification

Workflow

Intake — manuscript (or sections), study type, target journal/tier if known.
Classify the study and load the dimensions (review-dimensions.md); pull the right guideline stack via radiology-reporting.
For final readiness checks, open pre-submission-hard-gates.md and score each hard gate as PASS / CONDITIONAL / FAIL before writing softer reviewer comments.
Hunt dealbreakers (dealbreakers.md) — partition hygiene, leakage, external validation, labels/reference standard, segmentation reproducibility, statistical completeness, overclaim, data/code availability.
For AI/radiogenomics manuscripts, open ai-radiogenomics-pitfall-audit.md and audit the common failures that make a high-AUC paper look untrustworthy.
Review each dimension — record Issue | Severity (Blocker/Major/Minor) | Location | Guideline/risk | Fix.
Run two-pass claim audit for submission-facing text — abstract, Key Results, figure legends, tables, graphical abstract, and Discussion comparison/novelty claims should be extracted first, then verified via references/claim-verification-gate.md.
Check claims vs evidence — abstract, Key Results, Discussion: is every claim bounded by the data?
Write the report (review-report-format.md) — reviewer comments by severity + an editor-style recommendation + a prioritised fix order (what unlocks the most).

Output contract

Summary assessment — 3–5 sentences: what the paper does, its real strength, its decisive weakness, and the readiness verdict.
Major/Blocker comments — numbered, reviewer-style, each with location, the guideline/risk, and the concrete fix.
Minor comments — numbered, smaller issues.
Claims vs evidence — overclaims and the bounded rewording.
Claim audit status — for final readiness: extraction complete? verification complete? unsupported/numerical/visual-table claims remaining?
Hard-gate table — if final readiness is requested: contribution, data integrity, validation, statistics, reporting, figures, citation, ethics/data availability, and reviewer objection status.
Editor-style recommendation — ready / minor revision / major revision / not yet, with reasons.
Fix order — prioritised, routed to the relevant skill (stats, reporting, design, etc.).

Quality bar

A good mock review predicts the real reviews: it catches the dealbreakers, cites the exact item a reviewer would, separates fatal from cosmetic, and tells the author the order to fix things — without inventing compliance or softening a genuine blocker.

Handoffs

Checklist item-by-item audit → radiology-reporting.
Statistical completeness (CIs, calibration, DCA, multiplicity) → radiology-stats.
Leakage specifics → radiology-radiomics / radiology-deep-learning.
Missing external validation / reader study → radiology-design / radiology-translation.
Data/code/ethics gaps → radiology-data / radiology-ethics.
Rewriting overclaims / sections → radiology-writing / radiology-polishing.
Then choose the venue → radiology-journal; reviewer replies later → radiology-response.

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 Radiology Prereview AI skill do?

Why use Radiology Prereview on TypingMind?

Because you install it once and use it with any model. Radiology Prereview 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 Radiology Prereview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/modules/radiology-prereview. 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 Radiology Prereview?

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 Radiology Prereview?

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

Is the Radiology Prereview AI skill free?

Yes. It is published on GitHub by huang-sir1 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

Pre-submission Mock Review

Core stance

When to use

When to open extra files

Workflow

Output contract

Quality bar

Handoffs

Bundled files

Frequently asked questions

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