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Grant Proposal
Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says "write grant", "grant proposal", "申請書", "write KAKENHI", "科研費", "基金申请", "写基金", "NSF proposal", or wants to turn research ideas into a funding application.
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> Override for Codex users who want **Gemini**, not a second Codex agent, to act as the reviewer. Install this package **after** `skills/skills-codex/*`.
# Grant Proposal: From Research Ideas to Fundable Application
Draft a grant proposal based on: **$ARGUMENTS**
## Overview
This skill turns validated research ideas into a structured, reviewer-ready grant proposal. It chains sub-skills into a grant-specific pipeline:
```
/research-lit → /novelty-check → [structure design] → [draft] → /research-review → [revise] → GRANT_PROPOSAL.md
(survey) (verify gap) (aims + matrix) (prose) (panel review) (fix) (done!)
```
**This is a parallel branch, not part of the linear Workflow 1→1.5→2→3 pipeline.** After `/idea-discovery` produces validated ideas, the user can either:
- Go to `/experiment-bridge` → `/auto-review-loop` → `/paper-writing` (implement & publish)
- Go to `/grant-proposal` (write funding application first, then implement after funding)
```
┌→ /experiment-bridge → /auto-review-loop → /paper-writing (publish track)
/idea-discovery ────┤
└→ /grant-proposal → [get funded] → /experiment-bridge → ... (funding track)
```
Grant proposals argue for **future work** (feasibility + potential), not completed work (results + claims). This skill handles the unique requirements of grant writing: narrative arc design, reviewer-facing structure, budget justification, timeline planning, and agency-specific formatting.
## Constants
- **GRANT_TYPE = `KAKENHI`** — Default grant type. Supported: `KAKENHI`, `NSF`, `NSFC`, `ERC`, `DFG`, `SNSF`, `ARC`, `NWO`, `GENERIC`. Override via argument (e.g., `/grant-proposal "topic — NSF"`).
- **GRANT_SUBTYPE = `auto`** — Sub-type within the grant agency. Examples: KAKENHI `Start-up`/`Wakate`/`Kiban-B`; NSFC `Youth`/`Excellent-Youth`/`Distinguished`/`Overseas`/`Key`; NSF `CAREER`/`CRII`/`Standard`. Auto-detected from argument or defaults to the most common sub-type.
- **REVIEWER_MODEL = `gemini-review`** — Gemini reviewer invoked through the local `gemini-review` MCP bridge for proposal review. Set `GEMINI_REVIEW_MODEL` if you need a specific Gemini model override.
- **OUTPUT_FORMAT = `markdown`** — Output format. Supported: `markdown`, `latex`. LaTeX uses grant-specific templates when available.
- **MAX_REVIEW_ROUNDS = 2** — Maximum external review-revise cycles before finalizing.
- **OUTPUT_DIR = `grant-proposal/`** — Directory for generated proposal files.
- **LANGUAGE = `auto`** — Output language. Auto-detected from grant type: KAKENHI→Japanese, NSF→English, NSFC→Chinese, ERC→English, DFG→English (or German), SNSF→English, ARC→English, NWO→English. Override explicitly if needed.
- **AUTO_PROCEED = false** — At each checkpoint, **always wait for explicit user confirmation** before proceeding. Grant proposals require PI-specific judgment at every stage. Set `true` only if user explicitly requests fully autonomous mode.
> 💡 These are defaults. Override by telling the skill, e.g., `/grant-proposal "topic — NSF CAREER, latex output"` or `/grant-proposal "topic — NSFC Youth, language: English"`.
## Grant Type Specifications
### KAKENHI (Japan — JSPS)
| Field | Detail |
|-------|--------|
| **Sections** | 研究目的 (Research Objective), 研究計画・方法 (Plan & Methods), 準備状況 (Preparation Status), 人権の保護 (Ethics, if applicable) |
| **Sub-types** | 基盤研究 A/B/C (Kiban), 若手研究 (Wakate), 研究活動スタート支援 (Start-up), 国際共同研究 (International), 学術変革領域 (Transformative), 挑戦的研究 (Challenging), DC1/DC2 (doctoral) |
| **Language** | Japanese (English technical terms acceptable) |
| **Review criteria** | 学術的重要性 (academic significance), 独創性 (originality), 研究計画の妥当性 (plan feasibility), 研究遂行能力 (PI capability) |#broad-capability#wanshuiyin-aris#ml-research#autonomous#deep#researchgemini-api