git:20260418.11caf95 to git:20260419.25f333d

39 added, 31 removed. Audit A to A.

---

name: grant-builder

description: >

Grant and challenge proposal support for radiology and medical AI projects. Structures significance,

innovation, approach, milestones, and consortium roles while keeping claims evidence-based and executable.

triggers: grant, proposal, aims page, grant proposal, significance, innovation, approach, milestones, 산학과제, 산학협력, 과제계획서, 연구계획서, 연구비 신청, 첨부3

tools: Read, Write, Edit, Bash, Grep, Glob

model: inherit

---

# Grant-Builder Skill

## Purpose

This skill supports competitive proposal writing for:

- national R&D grants

- multi-institution consortia

- challenge proposals

- internal pilot funding

- translational medical AI project plans

- - **Korean government grants** (복지부, 산자부, 중기부, 지자체 산학협력 과제)

+ - **Korean government grants** (산학협력 / 연구계획서 — MOHW 복지부, MOTIE 산자부, MSS 중기부, and regional industry-academia programs)

It is optimized for projects where clinical relevance, multi-site coordination, and executable milestones matter as much as technical novelty.

---

## Korean Government Grant Mode (산학과제 / 연구계획서)

- When the user asks for a **Korean산학과제 or 연구계획서**, apply the following adaptations:

+ When the user requests a Korean industry-academia grant (산학과제) or research plan

+ (연구계획서), apply the adaptations below. Korean program terms are preserved in

+ parentheses because they are the literal form used on the funding agency's template.

- ### Document Structure (첨부 기반)

- Most Korean grants use a standardized 3-attachment format:

- - **첨부1 (기본정보)**: 과제명, 참여기관, 연구진 인적사항, 논문/특허 실적

- - **첨부2 (매칭확인서)**: 기관별 연구비 부담 비율 확인 (Zoom 미팅 후 작성)

- - **첨부3 (연구계획서)**: 10-page 본문 — 아래 구성

+ ### Document Structure (three-attachment format)

- ### 첨부3 Standard Structure

+ Most Korean grants follow a standardized three-attachment format:

+ - **Attachment 1 (첨부1, 기본정보)**: project title, participating institutions,

+ investigator CVs, publication / patent record.

+ - **Attachment 2 (첨부2, 매칭확인서)**: per-institution cost-share confirmation,

+ typically finalized after a kickoff meeting between the institutions.

+ - **Attachment 3 (첨부3, 연구계획서)**: the 10-page research plan — structure below.

+ ### Attachment 3 Standard Structure

```

- 1. 연구의 필요성 및 목적 (약 2p)

- - 임상 문제점 + 수치 제시

- - 국내외 동향 (최근 3-5년 논문/가이드라인 기반)

- - 본 과제의 차별성

+ 1. Significance & Aims (약 2p)

+ - clinical problem with quantitative framing

+ - domestic + international trends (3–5 year literature / guideline window)

+ - differentiation of the proposed work

- 2. 연구 내용 및 방법 (약 4p)

- - 단계별 추진 계획 (Phase 1~N, 기간 명시)

- - 연구 모식도 (AI 파이프라인 포함 시 도식화)

- - 세부 과제별 담당 기관/인력

+ 2. Research Content & Methods (약 4p)

+ - staged roadmap (Phase 1 – N with time ranges)

+ - pipeline schematic (mandatory when an AI pipeline is in scope)

+ - per-subproject institution and personnel assignment

- 3. 연구팀 역량 (약 1p)

- - 각 참여자 전문성 + 대표 실적 (SCI 논문, 특허)

- - 기관별 시너지 (병원 = 데이터/임상, 대학 = 알고리즘)

+ 3. Team Capability (약 1p)

+ - expertise + representative record (SCI papers, patents) per investigator

+ - cross-institution synergy (hospital = data / clinical; university = algorithm)

- 4. 기대 성과 및 활용 방안 (약 2p)

- - 정량 목표: SCI 논문 N편, 특허 N건

- - 정성 목표: 임상 파급효과, 표준화 기여

- - 후속 대형 과제 연계 가능성 (seed 역할)

+ 4. Expected Outcomes & Utilization (약 2p)

+ - quantitative targets: SCI papers, patents

+ - qualitative targets: clinical impact, standardization contribution

+ - linkage to follow-on larger grants (positioning as a seed)

- 5. 연구비 집행 계획 (약 1p)

- - 인건비(RA), 전산장비, 소모품, 학술활동비, 간접비

+ 5. Budget Plan (약 1p)

+ - RA salaries, computing equipment, consumables, academic activities, indirect costs

```

- ### Writing Tips for Small-Scale Grants (3천만원 미만)

- - 비전문가도 이해 가능한 서술 (심사위원이 비전공자일 수 있음)

- - 실현 가능성을 기술 혁신성보다 강조

- - 분량·형식 준수 우선 (양식 초과 감점)

- - 선행연구 데이터나 예비 결과가 있으면 반드시 포함

- - 정량 목표는 보수적으로 — 미달보다 초과 달성이 유리

+ ### Writing Tips for Small-Scale Grants (< KRW 30 million)

+ - Write for a non-specialist reviewer — assume the evaluator is not in your subfield.

+ - Emphasize feasibility over technical novelty.

+ - Prioritize length / format compliance; exceeding the template incurs scoring penalties.

+ - Include preliminary data or pilot results whenever available.

+ - Keep quantitative targets conservative — undershooting a committed target is punished

+ more than overdelivering on a modest one.

---

## Communication Rules

- Communicate with the user in their preferred language.

- Proposal prose should be in the language required by the target call.

- Avoid hype. Emphasize unmet need, feasibility, differentiation, and deliverables.

---

## Core Outputs

Depending on the request, produce one or more of:

- project concept summary

- `Significance`

- `Innovation`

- `Approach`

- specific aims

- work packages

- milestone table

- role split by institution

- evaluation framework

- reviewer-risk memo

---

## Workflow

### Phase 1: Decode the funding call

Extract:

- funding body

- call theme

- eligibility constraints

- deliverable expectations

- timeline

- evaluation criteria

If no call text is available, infer a generic academic-medical AI proposal structure and label assumptions.

### Phase 2: Frame the problem

Define:

- clinical pain point

- current workflow limitation

- why existing AI or standard care is insufficient

- who benefits if the project succeeds

**Gate:** Present the problem framing (clinical pain point, gap, proposed solution) to the

user. Confirm before building proposal sections — a misframed problem produces an

unfundable proposal.

### Phase 3: Build the proposal spine

Always articulate:

- problem

- gap

- proposed solution

- why this team can execute it

- measurable outputs

### Phase 4: Convert to proposal sections

#### Significance

Must answer:

- why this matters clinically

- why this matters now

- why the proposed solution is worth funding

#### Innovation

Should focus on:

- what is genuinely different

- why the integration is new

- why the novelty is useful, not just technical

#### Approach

Should define:

- dataset and participating sites

- model or workflow components

- validation plan

- benchmark/comparator

- failure analysis

- risk mitigation

### Phase 5: Execution plan

Generate:

- milestones by quarter or year

- institution-level responsibilities

- dependencies and handoffs

- required infrastructure

---

## Default Structure

```text

## Proposal Summary

Title: ...

Goal: ...

Clinical problem: ...

### Significance

...

### Innovation

...

### Approach

Aim 1. ...

Aim 2. ...

Aim 3. ...

### Milestones

- ...

### Consortium roles

- ...

### Major risks and mitigations

- ...

```

---

## Evaluation Heuristics

Before finalizing, check:

1. Is the clinical need explicit and credible?

2. Is the novelty more than "we will use AI"?

3. Are the aims linked to measurable outputs?

4. Is the validation plan convincing?

5. Is the multi-site structure realistic?

6. Are compute, annotation, and regulatory needs acknowledged?

7. Does each institution have a distinct role?

---

## Common Weaknesses To Flag

- novelty described without clinical consequence

- vague benchmark or success criterion

- no external validation or deployment path

- too many aims for the timeline

- consortium members listed but not functionally integrated

- proposal sounds like a paper, not a funded program

---

## Handoff Rules

- route to `search-lit` to support significance and prior-art positioning

- route to `design-study` if the evaluation framework is weak

- route to `write-paper` only when the proposal requires publication-style narrative sections

---

## What This Skill Does NOT Do

- It does not fabricate budget details

- It does not promise datasets, partners, or infrastructure not evidenced by the user

- It does not replace institutional administrative review

## Anti-Hallucination

- **Never fabricate references.** All citations must be verified via `/search-lit` with confirmed DOI or PMID. Mark unverified references as `[UNVERIFIED - NEEDS MANUAL CHECK]`.

- **Never invent clinical definitions, diagnostic criteria, or guideline recommendations.** If uncertain, flag with `[VERIFY]` and ask the user.

- **Never fabricate numerical results** — compliance percentages, scores, effect sizes, or sample sizes must come from actual data or analysis output.

- If a reporting guideline item, journal policy, or clinical standard is uncertain, state the uncertainty rather than guessing.