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 = gpt-5.4 — Model used via Codex MCP for proposal review. Must be an OpenAI model (e.g., gpt-5.4, o3, gpt-4o).
• 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".

Optional: Style reference (— style-ref: <source>, opt-in)

Lets the PI steer the proposal's structural layout (section order tendency, paragraph length, figure density, citation style) toward a successful past proposal or paper they'd like to mirror. Default OFF — when the user does not pass — style-ref, do nothing differently from before.

Only when — style-ref: <source> appears in $ARGUMENTS, run the helper FIRST, before drafting:

if [ ! -f tools/extract_paper_style.py ]; then
  echo "error: tools/extract_paper_style.py not found — re-run 'bash tools/install_aris.sh' to refresh the '.aris/tools' symlink (added in #174), or copy the helper manually from the ARIS repo" >&2
  exit 1
fi
CACHE=$(python3 tools/extract_paper_style.py --source "<source>")
case $? in
  0) ;;                                       # use $CACHE/style_profile.md as structural guidance
  2) echo "warning: style-ref skipped (missing optional dep)" >&2 ;;
  3) echo "error: --style-ref source failed; aborting proposal" >&2 ; exit 1 ;;
  *) echo "error: helper failed unexpectedly; aborting proposal" >&2 ; exit 1 ;;
esac

Sources accepted: local TeX dir / file, local PDF, arXiv id, http(s) URL. Overleaf URLs/IDs are rejected — clone the project locally first and pass the local path.

Strict rules (full contract in tools/extract_paper_style.py docstring):

• Use style_profile.md to align paragraph length tendency, figure budget, and citation density. Grant-type-mandated section order (KAKENHI 研究目的 → 研究計画・方法 → 準備状況, NSF Intellectual Merit → Broader Impacts, etc.) always takes precedence — the agency template wins, the style ref only refines secondary structure.
• Never copy proposal prose, claims, vision statements, or budget items from anything reachable through the cache. The reference might be someone else's funded proposal; reproducing language risks plagiarism.
• Never pass — style-ref (or the cache contents) to the GPT-5.4 reviewer sub-agent when it scores the draft — the proposal must be judged on its own merits.

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)
Cultural norms	Explicit yearly milestones (Year 1 / Year 2), budget justification integrated into plan, emphasize 社会的意義 (societal significance), concrete expected outputs (papers, datasets), reference KAKEN database for related funded projects

NSF (US)

Field	Detail
Sections	Project Summary (1p), Project Description (15p max), References Cited, Biographical Sketch, Budget Justification, Data Management Plan
Sub-types	Standard Grant, CAREER (early career), CRII (research initiation), RAPID, EAGER
Language	English
Review criteria	Intellectual Merit, Broader Impacts
Cultural norms	Aim-based structure (Aim 1/2/3), preliminary data strongly expected, broader impacts must be concrete and specific (not generic "benefit society"), Results from Prior Support section

NSFC (China — 国家自然科学基金)

Field	Detail
Sections	立项依据 (Rationale & Significance), 研究内容 (Content), 研究目标 (Objectives), 研究方案 (Plan & Methods), 可行性分析 (Feasibility), 创新性 (Innovation Points), 预期成果 (Expected Outcomes), 研究基础 (PI Foundation & Track Record)
Sub-types	面上项目 (General Program) — emphasis on scientific problem and research accumulation; 青年基金 (Young Scientists Fund) — age ≤35, emphasis on independence and growth potential; 优秀青年基金/优青 (Excellent Young Scientists) — age ≤38, emphasis on outstanding achievements; 杰出青年基金/杰青 (Distinguished Young Scientists) — age ≤45, emphasis on international-leading level; 海外优青 (Overseas Excellent Young Scientists) — emphasis on overseas experience and return contribution plan; 重点项目 (Key Program) — emphasis on systematic in-depth research
Language	Chinese
Review criteria	科学意义 (scientific significance), 创新性 (innovation), 可行性 (feasibility), 研究队伍 (team qualification)
Cultural norms	Heavy emphasis on 国际前沿 (international frontier) positioning, detailed feasibility analysis, explicit citation of applicant's prior publications, 研究基础 section is critical for demonstrating PI capability

ERC (EU — European Research Council)

Field	Detail
Sections	Extended Synopsis (5p), Scientific Proposal Part B2 (15p)
Sub-types	Starting Grant (2-7 years post-PhD), Consolidator Grant (7-12 years), Advanced Grant (established leaders)
Language	English
Review criteria	Ground-break