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Radiology Writing

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huang-sir1
radiology-writing

Draft or rebuild imaging-AI / radiomics / radiogenomics manuscript sections for Radiology (RSNA), Nature-portfolio/npj, European Radiology, NEJM, Science, or Lancet-family style from author-provided results, figures, notes, or Chinese drafts. Enforces the target venue's manuscript shape: Radiology Summary/Key Results, Nature-style broad scientific narrative, European Radiology key points/clinical relevance, NEJM four-part clinical abstract and SAP rigor, Science compact display-item story, or Lancet-series Research in context and AI/data transparency using The Lancet Digital Health guide as the default proxy. Use when the user wants to write or restructure imaging-research prose, not just polish it. Never invents data, metrics, or citations.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-writing
Stars
1.7K
Forks
17
Bundled files
10
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.

  • 10 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 Writing 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-writing .claude/skills/radiology-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Writing 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 Writing 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 Writing 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.

Radiology-Style Manuscript Writing

Use this skill to construct imaging-research prose — argument first, then sentences — in the exact shape Radiology requires. It is for drafting and restructuring, not only polishing (for sentence-level polish use radiology-polishing).

Core stance

  • Author evidence first. Never invent results, metrics, p-values, cohort numbers, citations, mechanisms, or limitations. Missing input → explicit placeholder or a question.
  • Write the argument before the sentences. One-sentence claim → section architecture → paragraph jobs → prose.
  • Match the Radiology shape. Structured abstract, Summary statement (one sentence), Key Results (≤ 3, ≤ 75 words), structured Discussion, tight word/figure limits.
  • Bound the claim. Ambitious but evidence-bounded; calibrate verbs to evidence (demonstrate → suggest → may reflect).
  • Reporting-aware. Every Methods/Results element should satisfy the relevant checklist item (CLAIM/TRIPOD+AI/CLEAR/STARD) — cross-check with radiology-reporting.

When to use

  • Draft/rebuild any section: title, structured abstract, Summary statement, Key Results, Introduction, Materials and Methods, Results, Discussion.
  • Turn Chinese lab notes / mixed drafts into submission-ready English.
  • Restructure a rejected draft to the Radiology argument shape.

When to open extra files

FileOpen when
references/article-architecture.mdSection order, argument flow, and the Radiology manuscript skeleton
references/structured-abstract.mdWriting the structured abstract + Summary statement + Key Results box
references/methods.mdMaterials and Methods for imaging/AI/radiomics studies (what must appear, in order)
references/results.mdResults narrative: flow, performance with CIs, comparisons, validation
references/discussion.mdStructured Discussion (key-finding first → context → limitations → conclusion)
references/chinese-author-workflow.mdNotes are Chinese / mixed / lab-note style; translate intent and argument, not clause order
references/nature-family-shape.mdTarget is Nature Medicine / Nature Biomedical Engineering / Nature Communications / npj Digital Medicine / Cell Reports Medicine etc. — unstructured abstract, no Summary statement/Key Results box, different Methods placement
references/argument-spine-and-stage-gates.mdFull manuscript rebuild, rejected-paper rescue, unclear contribution, high-impact submission, or a draft whose story/figures/results do not yet lock together
references/journal-family-writing-style.mdTarget journal family is known, or the user supplied author-guide PDFs/classic papers and wants the manuscript to carry that venue's writing style

Intake (identify before drafting)

  • Target venue/shape: Radiology-family (structured abstract, Summary statement, Key Results — default) or Nature-family (unstructured abstract, no Summary statement/Key Results → references/nature-family-shape.md). If undecided, draft the Radiology shape first and confirm venue before finalising the abstract (→ radiology-journal).
  • Section(s) requested.
  • Study type: diagnostic-accuracy, prediction model, radiomics, radiogenomics, reader study, observational, trial.
  • Core claim: what the study actually shows.
  • Evidence: cohorts (n, source, dates), metrics with CIs, comparisons, validation.
  • Boundary: where the claim stops (single-centre? retrospective? prevalence?).
  • Limits: target word/figure counts (verify against current author instructions).

If core claim, evidence, or boundary is missing, surface the gap and offer a scaffold with placeholders rather than inventing content.

Writing workflow

  1. For full manuscripts or high-impact rebuilds, open argument-spine-and-stage-gates.md and establish the project context, contribution-first gate, and results-as-validation map before drafting long prose.
  2. For target-venue writing taste, open journal-family-writing-style.md after confirming the venue family; use it to adjust article shape, title/abstract rhythm, key points, and clinical relevance language.
  3. One-sentence argument: "In [population/modality], we show [advance] using [approach], supported by [key result with CI], with [boundary]."
  4. Pick the architecture (article-architecture.md) by study type.
  5. Map each paragraph to one job: context / gap / objective / design / cohort / technique / analysis / result / comparison / validation / interpretation / limitation.
  6. For full sections, draft the topic-sentence chain first. If the claims do not flow, revise the chain before writing full paragraphs.
  7. Draft from evidence outward — keep claims next to the numbers that support them.
  8. Calibrate verbs to evidence; remove unsupported novelty/"first" claims.
  9. Fit the Radiology shape — abstract headings, Summary statement, Key Results, structured Discussion.
  10. Self-review against the relevant reporting checklist; flag unmet items.

Section defaults

  • Title — concrete: population/condition + modality/method + finding/role. State AI/ radiomics if central. Avoid slogans and "novel."
  • Structured abstract — Background → Purpose → Materials and Methods → Results → Conclusion; report design, cohort sizes/dates, primary metric(s) with CIs, a bounded conclusion. (structured-abstract.md)
  • Summary statement — a single declarative sentence of the main finding.
  • Key Results — up to 3 results/conclusions, ≤ 75 words, with summary data; don't repeat the Summary statement; avoid vague language and abbreviations.
  • Introduction — field/clinical stakes → specific gap → objective/hypothesis. Short; no results dump.
  • Materials and Methods — design + ethics/registration → participants/flow → imaging technique → image analysis/reference standard/readers → model/feature pipeline → statistical analysis. (methods.md)
  • Results — patient flow + characteristics → primary performance with CIs → comparisons → validation/subgroups. Past tense, quantitative. (results.md)
  • Discussionfirst paragraph = concise summary of key findings, then relation to prior work, then limitations, then a bounded conclusion. (discussion.md)

Output format

  1. Draft — the requested prose in Radiology shape.
  2. Section outline — 3–7 compact bullets (for a full section).
  3. Topic-sentence chain — for full sections or major rewrites, the claim sequence before paragraph expansion.
  4. Claim–evidence mapClaim | Evidence (with CI) | Status: supported / needs input.
  5. Stage gates — for full manuscripts: contribution gate, results-as-validation gate, citation/figure/reporting gates that are passed or still open.
  6. Venue style check — if the target family is known: abstract shape, title/key-points logic, clinical relevance language, and any guide-derived limits or VERIFY FROM GUIDE.
  7. Assumptions / missing inputs — only material gaps.
  8. Reporting check — checklist items this draft does/doesn't satisfy (→ radiology-reporting).

For Chinese notes: polished English first, then brief Chinese notes on structural choices.

Handoffs

  • Sentence-level polish / house style → radiology-polishing.
  • Statistics/CIs/tests behind the numbers → radiology-stats.
  • Checklist compliance → radiology-reporting.
  • Figures/legends → radiology-figure.
  • Finding/verifying citations to support a claim in Introduction/Discussion → radiology-citation.
  • Draft is complete and ready for a harsh read before submission → radiology-prereview.

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 Writing AI skill do?

Draft or rebuild imaging-AI / radiomics / radiogenomics manuscript sections for Radiology (RSNA), Nature-portfolio/npj, European Radiology, NEJM, Science, or Lancet-family style from author-provided results, figures, notes, or Chinese drafts. Enforces the target venue's manuscript shape: Radiology Summary/Key Results, Nature-style broad scientific narrative, European Radiology key points/clinical relevance, NEJM four-part clinical abstract and SAP rigor, Science compact display-item story, or Lancet-series Research in context and AI/data transparency using The Lancet Digital Health guide as...

Why use Radiology Writing on TypingMind?

Because you install it once and use it with any model. Radiology Writing 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 Writing 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-writing. 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 Writing?

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 Writing?

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

Is the Radiology Writing 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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