Radiology Journal logo

Radiology Journal

CommunityPopular
huang-sir1
radiology-journal

Match a finished or near-finished imaging-AI / radiomics / radiogenomics manuscript to the right target journals and build a submission tier list (reach / target / safety) grounded in each venue's publication patterns, author-guide style profiles, and the paper's real strengths and weaknesses. Use when the user asks where to submit, "选刊/投哪个期刊", journal selection, "can this go to Nature Medicine / Science / NEJM / Lancet / Lancet Oncology / Lancet Digital Health / Radiology", fit assessment, or a submission ladder. Uses The Lancet Digital Health guide as the default Lancet-series proxy. Grades external validation, prospectivity, reader study, calibration, clinical utility, sample size, centers, novelty, and reporting compliance; returns fit, risk, strengthening priorities, and venue-style requirements. Verifies current journal scope via live search; never selects on impact factor alone.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-journal
Stars
1.7K
Forks
17
Bundled files
4
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.

  • 4 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 Journal 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/huang-sir1/radiology-skills.git /tmp/radiology-skills
mkdir -p .claude/skills
cp -r /tmp/radiology-skills/radiology-skills/modules/radiology-journal .claude/skills/radiology-journal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Journal 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 Journal 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 Journal 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.

Journal Selection & Submission Tiering

Use this skill when the paper is essentially written and the question is where to send it. It reads the manuscript's real strengths and weaknesses, grades it on the dimensions imaging venues actually weigh, and returns an honest reach / target / safety ladder — not a wish list ranked by impact factor.

Core stance

  • Fit beats prestige. The best journal is the one whose published pattern matches this paper's design and evidence — not the highest impact factor it might survive.
  • Grade honestly on the right axes. External validation, prospectivity, reader/clinical- utility evidence, calibration, sample size, number of centers, and novelty determine tier far more than topic.
  • Name the biggest weakness first. The limiting factor (single-center, no external validation, small n, retrospective) sets the realistic ceiling; say it plainly.
  • Patterns are durable; current scope is not. Use the publication-pattern heuristics here, but verify each candidate's current aims/scope live (→ radiology-search) before committing.
  • A ladder, not a bet. Give reach/target/safety with the trade-off (turnaround, fit, risk), and what to strengthen to move up a tier.
  • Integrity. Don't promise acceptance, don't inflate an under-validated paper into a top-tier pitch, and don't pick on impact factor alone.

When to use

  • "Where should I submit this?" / "这篇文章适合投哪个期刊?/ 帮我选刊。"
  • "Can my single-center retrospective radiomics paper go to Nature Medicine / Lancet Digital Health?"
  • "Build me a reach/target/safety submission list."
  • "What's the biggest weakness deciding my journal tier, and how do I move up?"

When to open extra files

FileOpen when
references/venue-patterns.mdWhat each imaging/clinical/AI venue tends to publish and the bar it enforces (verify live)
references/fit-grading.mdGrading the paper on the tier-deciding dimensions; turning the grade into reach/target/safety
references/submission-logistics.mdArticle types, format/word/figure limits, cover letter, suggested reviewers, transfer cascades
references/venue-style-profiles.mdThe user supplies author-guide PDFs/classic articles, asks for journal "taste," or the target is European Radiology / Nature Partner / npj and style/logistics must match the parsed guide profile

Workflow

  1. Extract the paper profile — type (DTA / prediction / radiomics / radiogenomics / reader / segmentation), core selling point, and the single biggest weakness.
  2. Grade it (fit-grading.md) on: external validation, prospectivity, reader/utility evidence, calibration, sample size, centers, novelty, reporting compliance.
  3. Map to venues (venue-patterns.md) — which tiers the grade realistically reaches; reject obvious mismatches with the reason.
  4. Apply venue style profile when available (venue-style-profiles.md) — article shape, house taste, required compliance artifacts, and visual/writing constraints from supplied guides or classic papers.
  5. Verify current scope — hand each shortlisted venue to radiology-search to confirm it still publishes this kind of work and recently has.
  6. Build the ladder — reach / target / safety, each with match reason, risk, turnaround consideration, and the one thing to strengthen to climb.
  7. Logistics (submission-logistics.md) — article type, limits, cover-letter angle, suggested reviewers, and any transfer cascade.

Output contract

  1. Paper profile — type, core selling point, biggest weakness, reporting-guideline fit.
  2. Fit grade — scored on the tier-deciding dimensions, with the limiting factor named.
  3. Submission ladderReach / Target / Safety, each: venue (pattern-matched + to be verified live), match reason, risk, what to strengthen to move up.
  4. Strengthen-first — the highest-leverage improvements to raise the tier (→ relevant skill).
  5. Verify now — the live-scope checks to run before submitting (→ radiology-search).
  6. Venue style — if a guide/profile is available: article shape, voice, figure/table taste, and compliance artifacts to satisfy before submission.
  7. Logistics — article type, limits, cover-letter angle, suggested reviewers.

Quality bar

A good selection reads like a mentor who has published across these venues: it names the paper's ceiling honestly, gives a realistic ladder, verifies scope rather than trusting memory, and tells the author exactly what to strengthen to aim higher — never selling impact factor as fit.

Handoffs

  • Pre-submission audit to fix weaknesses first → radiology-prereview.
  • Reporting-guideline compliance and the submission map → radiology-reporting.
  • Strengthening evidence (external validation, reader study) → radiology-design / radiology-translation.
  • Verifying current journal scope / recent comparable papers → radiology-search.
  • Cover letter / manuscript wording → radiology-writing.
  • Journal tiering is strategy, not a prediction of acceptance.

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 Journal AI skill do?

Match a finished or near-finished imaging-AI / radiomics / radiogenomics manuscript to the right target journals and build a submission tier list (reach / target / safety) grounded in each venue's publication patterns, author-guide style profiles, and the paper's real strengths and weaknesses. Use when the user asks where to submit, "选刊/投哪个期刊", journal selection, "can this go to Nature Medicine / Science / NEJM / Lancet / Lancet Oncology / Lancet Digital Health / Radiology", fit assessment, or a submission ladder. Uses The Lancet Digital Health guide as the default Lancet-series proxy. Grad...

Why use Radiology Journal on TypingMind?

Because you install it once and use it with any model. Radiology Journal 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 Journal 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-journal. 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 Journal?

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 Journal?

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

Is the Radiology Journal 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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