Grant-Builder Skill
Write proposal prose in the language the target call requires. Produce whichever parts the request needs: concept summary, Significance, Innovation, Approach, specific aims, work packages, milestone table, role split by institution, evaluation framework, reviewer-risk memo.
Korean Government Grant Mode
When the user requests a Korean industry-academia grant (산학과제) or research plan (연구계획서) — e.g., MOHW, MOTIE, MSS or regional industry-academia programs — 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 (three-attachment format)
| Attachment | Contents |
|---|---|
| 1 (첨부1, 기본정보) | project title, participating institutions, investigator CVs, publication / patent record |
| 2 (첨부2, 매칭확인서) | per-institution cost-share confirmation, typically finalized after a kickoff meeting between the institutions |
| 3 (첨부3, 연구계획서) | the 10-page research plan — structure below |
Attachment 3 Standard Structure
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. 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. Team Capability (약 1p) - expertise + representative record (SCI papers, patents) per investigator - cross-institution synergy (hospital = data / clinical; university = algorithm) 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. Budget Plan (약 1p) - RA salaries, computing equipment, consumables, academic activities, indirect costs
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.
Workflow
Phase 1: Decode the funding call
Extract funding body, call theme, eligibility constraints, deliverable expectations, timeline and evaluation criteria. If no call text is available, infer a generic academic-medical AI proposal structure and label the assumptions.
Phase 2: Frame the problem
Define the clinical pain point, the current workflow limitation, why existing AI or standard care is insufficient, and 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 now, and why the proposed solution is worth funding.
- Innovation: what is genuinely different, why the integration is new, why the novelty is useful and not just technical.
- Approach: dataset and participating sites, model or workflow components, validation plan, benchmark/comparator, failure analysis, risk mitigation.
Route to search-lit to support significance and prior-art positioning; cite a reference only with
a /search-lit-confirmed DOI or PMID, otherwise mark it [UNVERIFIED - NEEDS MANUAL CHECK]. Mark
any unconfirmed clinical definition, diagnostic criterion or guideline recommendation [VERIFY] and
ask the user. Route to design-study if the evaluation framework is weak, and to write-paper only
when the proposal requires publication-style narrative sections.
Phase 5: Execution plan
Generate milestones by quarter or year, institution-level responsibilities, dependencies and handoffs, and required infrastructure. Do not fabricate budget details, and do not promise datasets, partners or infrastructure the user has not evidenced.
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 - ...
Before Finalizing
Check, and flag any failure to the user:
- Is the clinical need explicit and credible?
- Is the novelty more than "we will use AI", with a clinical consequence?
- Are the aims linked to measurable outputs with a concrete benchmark or success criterion?
- Is the validation plan convincing, including external validation or a deployment path?
- Is the multi-site structure realistic, with each institution in a distinct, functionally integrated role?
- Are compute, annotation, and regulatory needs acknowledged?
- Are there too many aims for the timeline?
- Does it read as a funded program rather than a paper?
