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Radiology Grant

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huang-sir1
radiology-grant

Reframe and polish imaging-AI / radiomics / radiogenomics research as a research grant proposal — convert paper-style "we built a model" into grant logic (clinical need → scientific question → hypothesis → specific aims/研究内容 → technical route/技术路线 → innovation/创新点 → feasibility/可行性 → expected outcomes), and strengthen title, abstract, background/立项依据, aims, key scientific question/关键科学问题, and significance. Primary and most-developed track is NSFC (国家自然科学基金: 青年/面上/地区) and provincial funds (省自然); also covers reframing the same proposal for international funders (NIH R01, ERC Starting/Consolidator/Advanced, Wellcome Trust) including realistic eligibility for China-based applicants and cross-border routes (NSFC international collaboration line, RGC, MSCA, foundations). Use when the user mentions 国自然/省自然/基金申请/标书/立项依据/科学问题/技术路线/创新点/可行性, NIH/R01/ERC/Wellcome/国际基金, or wants to turn a study into a fundable proposal for any of these. Flags weak innovation, thin preliminary data, open-loop technical routes, over-promising, and funder-eligibility mismatches. Reminds the author to verify the current official guidelines; never fabricates preliminary results, citations, or eligibility.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-grant
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 Grant 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-grant .claude/skills/radiology-grant
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Grant 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 Grant 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 Grant 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.

Research Grant Proposals (国自然 / 省自然 / institutional / international)

Use this skill to turn an imaging-research idea or finished study into a fundable proposal. A grant is not a paper: reviewers fund a scientific question and a credible plan to answer it, not a model. This skill rebuilds the logic and polishes each section to the structure funders expect. NSFC/省自然 is the primary, most fully worked-out track; the same reframing logic extends to international funders (NIH, ERC, Wellcome) when the team is also pursuing cross-border funding — see references/international-grants.md.

Core stance

  • Grant logic, not paper logic. Lead with the clinical need and the scientific question, then hypothesis → aims → technical route → innovation → feasibility → expected outcomes. "Construct a model / improve accuracy" is an engineering task, not a science question — reframe it into the mechanism or generalisable principle being tested.
  • Eligibility before drafting, for any international target. Confirm the author/institution can actually apply as lead PI before writing a word — NIH, ERC, and Wellcome each have real, currently-changing eligibility constraints that are easy to miss (→ references/international-grants.md). Wasting effort on an inaccessible mechanism is worse than a hard "not this track."
  • The key scientific question (关键科学问题) is the spine. One sharp, answerable question that the aims serve. If the aims don't all serve it, the proposal is unfocused.
  • Close the loop. The technical route (技术路线) must connect need → question → each aim → method → expected result → back to the question. Reviewers reject open-loop routes.
  • Innovation must be specific and defensible. Name the increment (question / data / method / validation / mechanism). Avoid "first/领先" without grounding.
  • Feasibility is shown, not asserted. Preliminary data, team capability, data access, and ethics make it credible — never fabricate preliminary results.
  • Don't over-promise. Aims must be achievable in the period and budget; over-scoping reads as naïveté.
  • Verify the current guidelines. Word limits, format, attachments, ethics, and 限项 rules change yearly — confirm against the current official 申报指南 (the author must check; this skill flags it, it does not have the current year's rules memorised).

When to use

  • "把我的研究改写成国自然/省自然标书 / 帮我写立项依据、科学问题、技术路线、创新点。"
  • "Turn this finished study into a grant proposal."
  • "Is my innovation point / feasibility strong enough? polish my aims."
  • "Reframe 'build a model' into a fundable scientific question."
  • "我们也想申请 NIH / ERC / Wellcome,能不能用同一份研究改写?" / "Can this also become an NIH R01 / ERC / Wellcome proposal, and am I even eligible to apply?"

When to open extra files

FileOpen when
references/grant-architecture.mdThe section-by-section structure and what each must accomplish (NSFC/provincial)
references/reframe-and-innovation.mdConverting paper/engineering framing into a scientific question + a defensible innovation point
references/feasibility-and-pitfalls.mdShowing feasibility (preliminary data, route closure, team/data/ethics) and the common rejection reasons
references/international-grants.mdTarget is also/instead NIH, ERC, or Wellcome — eligibility reality for China-based applicants, section-mapping from NSFC structure, and more directly reachable cross-border routes

Workflow

  1. Identify the target funder(s) — NSFC/省自然 (default), and/or NIH/ERC/Wellcome/other international. For any international target, check eligibility first (international-grants.md) before investing drafting effort.
  2. Extract the science. From the idea/study, find the clinical need and the one scientific question worth funding (reframe engineering goals into mechanism/generalisable principle). This is funder-independent — do it once, reuse for every target.
  3. Set the architecture (grant-architecture.md for NSFC/provincial; international-grants.md for the funder-specific section mapping) — title, abstract, 立项依据, 研究目标, 研究内容 (aims), 关键科学问题, 技术路线, 创新点, 可行性, 预期成果, plan.
  4. Build aims that serve the question — each aim a testable sub-question with a method and an expected result; aims are coherent, not a feature list.
  5. Forge the innovation point (reframe-and-innovation.md) — specific, defensible, tied to the gap; classify the kind of innovation.
  6. Close the technical route — a loop diagram in prose: need → question → aims → methods → expected results → question; mark validation and risk mitigations.
  7. Evidence feasibility (feasibility-and-pitfalls.md) — preliminary data (real only), team, data access, ethics; pre-empt the common rejection reasons.
  8. Polish & guideline-check — tighten each section; flag every place the author must verify the current official 申报指南 (limits, format, 限项, ethics).

Output contract

  1. Target funder(s) & eligibility (only when international) — funder, whether the author/institution can be lead PI under current rules, and the realistic route if not (co-PI, alternate mechanism) — before any section drafting.
  2. Scientific question — the one fundable question + hypothesis.
  3. Section drafts — title, abstract, 立项依据, 研究目标/内容, 关键科学问题, 技术路线, 创新点, 可行性, 预期成果 — drafted or restructured.
  4. Innovation point — specific, classified, defensible.
  5. Technical-route check — is the loop closed? gaps marked.
  6. Feasibility & risk — what supports feasibility; risks + mitigations.
  7. Weaknesses & fixes — thin innovation, weak preliminary data, over-scoping, open loop.
  8. 待核验(中文) — current-guideline items the author must confirm (字数/格式/附件/伦理/限项).

Quality bar

A good proposal makes a reviewer see one sharp scientific question, aims that all serve it, a closed technical route, a specific innovation, and credible feasibility — written to the funder's structure, with no fabricated preliminary data and an explicit reminder to verify the current year's guidelines.

Handoffs

  • Study/validation design behind an aim → radiology-design.
  • Frontier framing & evidence for the gap → radiology-frontier (verify live → radiology-search).
  • Statistics/sample-size for an aim → radiology-stats.
  • Ethics/feasibility of data → radiology-ethics / radiology-data.
  • English polish of an English-language proposal → radiology-polishing.
  • Verifying current call deadlines, page limits, and funder eligibility rules live → radiology-search.
  • This skill drafts and critiques proposals; it does not guarantee funding, does not confirm eligibility on the author's behalf, and does not replace the current official 申报指南 or the target institution's sponsored-programs/grants office.

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 Grant AI skill do?

Reframe and polish imaging-AI / radiomics / radiogenomics research as a research grant proposal — convert paper-style "we built a model" into grant logic (clinical need → scientific question → hypothesis → specific aims/研究内容 → technical route/技术路线 → innovation/创新点 → feasibility/可行性 → expected outcomes), and strengthen title, abstract, background/立项依据, aims, key scientific question/关键科学问题, and significance. Primary and most-developed track is NSFC (国家自然科学基金: 青年/面上/地区) and provincial funds (省自然); also covers reframing the same proposal for international funders (NIH R01, ERC Starting/Consolid...

Why use Radiology Grant on TypingMind?

Because you install it once and use it with any model. Radiology Grant 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 Grant 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-grant. 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 Grant?

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 Grant?

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

Is the Radiology Grant 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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