grant-proposal

name	grant-proposal
description	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.
argument-hint	[research-direction — grant-type]
allowed-tools	Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Agent, Skill, mcp__codex__codex, mcp__codex__codex-reply

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".

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-breaking nature, Methodology, PI track record
Cultural norms	Emphasis on "high-risk/high-gain", methodology table with WP/deliverables/milestones, Gantt chart expected, strong PI narrative

DFG (Germany — Deutsche Forschungsgemeinschaft)

Field	Detail
Sections	State of the Art, Objectives, Work Programme, Bibliography, CV
Language	English or German
Review criteria	Scientific quality, Originality, Feasibility, PI qualification

SNSF (Switzerland — Swiss National Science Foundation)

Field	Detail
Sections	Summary, Research Plan, Timetable, Budget
Language	English
Review criteria	Scientific relevance, Originality, Feasibility, Track record

ARC (Australia — Australian Research Council)

Field	Detail
Sections	Project Description, Feasibility, Benefit, Budget
Language	English
Review criteria	Research quality, Feasibility, Benefit to Australia

NWO (Netherlands — Dutch Research Council)

Field	Detail
Sections	Summary, Proposed Research, Knowledge Utilisation
Language	English
Review criteria	Scientific quality, Innovative character, Knowledge utilisation

GENERIC

For any grant not listed above. User provides section names, page limits, and review criteria via argument:

/grant-proposal "topic — GENERIC, sections: Background|Methods|Impact, language: English"

State Persistence (Compact Recovery)

Grant proposal drafting is a long task that may trigger context compaction. Persist state to grant-proposal/GRANT_STATE.json after each phase:

{
  "phase": 2,
  "grant_type": "KAKENHI",
  "grant_subtype": "Start-up",
  "language": "Japanese",
  "codex_thread_id": "019cfcf4-...",
  "gap_statement": "...",
  "aims_count": 3,
  "status": "in_progress",
  "timestamp": "2026-03-18T15:00:00"
}

Write this file at the end of every phase. On invocation, check for this file:

• If absent or status: "completed" → fresh start
• If status: "in_progress" and within 24h → resume from saved phase (read GRANT_PROPOSAL.md and GRANT_REVIEW.md to restore context)
• If older than 24h → fresh start (stale state)

On completion, set "status": "completed".

Workflow

Phase 0: Input Parsing & Context Gathering

Parse $ARGUMENTS to extract:

1. Research direction/idea — may reference existing files or be a freeform description
2. Grant type — detect from keywords (e.g., "科研費"→KAKENHI, "NSF"→NSF, "国自然"→NSFC, "基金"→NSFC)
3. Grant sub-type — detect from keywords (e.g., "Start-up", "若手", "青年", "CAREER", "优青", "海外优青")
4. Overrides — output format, language, review rounds

Then gather context from the project directory:

1. Read IDEA_REPORT.md if it exists (from /idea-discovery)
2. Read refine-logs/FINAL_PROPOSAL.md if it exists (from /research-refine)
3. Read refine-logs/EXPERIMENT_PLAN.md if it exists (from /experiment-plan)
4. Read AUTO_REVIEW.md if it exists (from /auto-review-loop — prior review feedback is gold for grants)
5. Read NARRATIVE_REPORT.md or STORY.md if they exist
6. Read any existing literature notes or survey documents
7. Scan for the user's publication list (e.g., publications.md, cv.md, bio.md, CV.pdf)
8. Check for grant-proposal/GRANT_STATE.json (resume from prior interrupted run)

If insufficient context exists:

• No research idea at all → suggest running /idea-discovery first
• No literature survey → will invoke /research-lit inline in Phase 1
• No publication list → leave PI qualification section with [TODO: Add publications] placeholders
• Has AUTO_REVIEW.md → extract reviewer feedback and use it to strengthen the feasibility narrative

Phase 1: Literature & Landscape Positioning

Invoke /research-lit to ground the proposal in real literature, then search for competing funded projects:

/research-lit "$ARGUMENTS"

What this does:

• Reuse existing surveys if /research-lit was already run and notes exist
• Otherwise invoke /research-lit for multi-source literature search (arXiv, Scholar, Zotero, local PDFs)
• Search for funded projects in the same area via WebSearch:
  • KAKENHI → KAKEN database (https://kaken.nii.ac.jp/)
  • NSF → NSF Award Search (https://www.nsf.gov/awardsearch/)
  • NSFC → NSFC funded projects
  • Other agencies → general web search
• Identify competing groups and their recent publications
• Run /novelty-check on the proposed research direction to verify the gap is real:

  /novelty-check "[proposed gap statement]"
  

• Build the gap statement — the single most important sentence in the proposal:

  "Despite progress in [X], [specific gap] remains unaddressed because [reason].
  This proposal addresses this by [approach], which will [expected impact]."
  

🚦 Checkpoint: Present the landscape summary and gap statement to the user:

📚 Literature & landscape analysis complete:
- [key findings from literature]
- [competing funded projects found]
- Gap statement: "[the gap statement]"

Does this accurately capture the positioning? Should I adjust before designing the proposal structure?

⛔ STOP HERE and wait for user response. Do NOT auto-proceed unless AUTO_PROCEED=true was explicitly set by the user.

Options for the user:

• Reply "go" or "ok" → proceed to Phase 2 with current positioning
• Reply with adjustments (e.g., "focus more on X", "the gap should emphasize Y") → refine and re-present
• Reply "stop" → end the skill, save current progress to grant-proposal/DRAFT_NOTES.md

State: Write GRANT_STATE.json with phase: 1 and the gap statement.

Phase 2: Narrative Structure & Aims Design

Design the proposal's logical architecture before writing any prose.

2.1 Define Specific Aims (2-4)

Each aim must satisfy:

• Independently valuable — if one aim fails, others still produce publishable results
• Logically connected — Aim 1 enables Aim 2, Aim 2 informs Aim 3
• Concrete deliverables — each aim maps to specific outputs (papers, datasets, tools, benchmarks)
• Feasible within budget and timeline

2.2 Build Claims-Aims-Evidence Matrix

| Aim | Key Claim | Preliminary Evidence | Proposed Validation | Risk Level | Deliverable |
|-----|-----------|---------------------|--------------------|-----------:|-------------|
| Aim 1 | [claim] | [pilot data, prior work] | [experiments] | LOW | [paper, dataset] |
| Aim 2 | [claim] | [theoretical basis] | [experiments] | MEDIUM | [paper, tool] |

2.3 Design the Narrative Arc

Grant proposals follow a fundamentally different arc from papers:

Problem → Why Now → What We Propose → Why It Will Work → What We Will Deliver
         (not: Problem → Method → Results → Implications)

• Problem: What gap exists and why it matters (scientific + societal)
• Why Now: What recent developments make this the right time (new data, new methods, new need)
• What We Propose: The specific aims and approach
• Why It Will Work: Preliminary data, PI track record, team expertise, feasibility arguments
• What We Will Deliver: Concrete outputs, timeline, expected publications

2.4 Timeline & Milestones

Design year-by-year (or quarter-by-quarter) plan:

### Year 1
- Q1-Q2: [Aim 1 tasks]
- Q3-Q4: [Aim 1 completion + Aim 2 start]
- Expected outputs: [papers, datasets]

### Year 2
- Q1-Q2: [Aim 2 completion + Aim 3]
- Q3-Q4: [Aim 3 completion + synthesis]
- Expected outputs: [papers, tools, final report]

2.5 Structural Review

Invoke /research-review to get critical feedback on the proposal structure before drafting:

/research-review "[GRANT_TYPE] [GRANT_SUBTYPE] proposal structure:
Gap: [gap statement]
Aims: [aims list with claims-evidence matrix]
Timeline: [timeline]
— reviewer persona: [GRANT_TYPE] review panelist"

What this does:

• GPT-5.4 xhigh acts as a grant review panelist (not a paper reviewer)
• Evaluates aims independence, narrative arc, risk identification, timeline realism
• Identifies the single biggest reviewer concern
• Provides actionable fixes ranked by severity

Apply structural feedback before proceeding to drafting.

🚦 Checkpoint: Present the proposal structure to the user:

🏗️ Proposal structure designed:
- Gap: [gap statement]
- Aim 1: [title] — Risk: LOW
- Aim 2: [title] — Risk: MEDIUM
- Aim 3: [title] — Risk: LOW
- Timeline: [summary]
- Reviewer feedback: [key points from GPT-5.4]

Proceed to section drafting? Or adjust the structure?

⛔ STOP HERE. This is the most critical checkpoint — the proposal structure determines everything downstream.

Options for the user:

• Reply "go" or "ok" → proceed to Phase 3 (section drafting)
• Reply with structural changes (e.g., "merge Aim 2 and 3", "add an aim about X", "reduce to 2 aims") → redesign and re-present
• Reply "back" → return to Phase 1 to adjust the gap/positioning
• Reply "stop" → save current structure to grant-proposal/DRAFT_NOTES.md

State: Write GRANT_STATE.json with phase: 2, aims summary, and Codex threadId.

Phase 3: Section Drafting

Draft each section according to the grant type template. Write complete prose, not outlines or placeholders.

What this does:

• Writes all required sections in the agency-specific language and tone
• Pulls content from IDEA_REPORT.md, FINAL_PROPOSAL.md, and literature notes
• Uses /paper-illustration for figure generation (if user requests)
• Leaves [TODO] only for PI-specific information, [AMOUNT] for budget figures
• Outputs grant-proposal/GRANT_PROPOSAL.md

Drafting Order (optimized for narrative coherence)

1. Specific Aims / Research Objective — the "abstract" of the grant. Write first, refine last.
2. Background / Significance / State of the Art — establish the problem and gap.
3. Research Plan / Methods — per aim, with feasibility arguments.
4. Figures — generate key diagrams (see below).
5. Timeline & Milestones — year-by-year deliverables.
6. PI Qualification / Preparation Status — track record, team, infrastructure.
7. Budget Justification — narrative only (leave dollar/yen amounts as [AMOUNT] placeholders).
8. Broader Impacts / Societal Significance — if required by the grant type.

Figure Generation

Grant proposals benefit greatly from clear diagrams. Generate the following figures using SVG or matplotlib (save to grant-proposal/figures/):

1. 全体構成図 / Overview Diagram — Show the relationship between aims (Aim 1 → Aim 2 → Aim 3), shared resources (participants, stimuli, pipeline), and outputs. This is the single most important figure.
2. 実験パラダイム図 / Experimental Paradigm — Visual schematic of each paradigm (stimulus timing, conditions, EEG recording).
3. 年次計画 / Timeline Gantt Chart — Year-by-year (or H1/H2) milestones with deliverables.

For AI-generated publication-quality figures, invoke /paper-illustration:

/paper-illustration "Overview diagram showing [aims relationship + shared resources] for grant proposal"

For simpler diagrams (flowcharts, Gantt charts), generate clean SVG or matplotlib directly via code.

🚦 Figure Checkpoint: Before generating, ask which figures the user wants:

🎨 The following figures would strengthen this proposal:
1. 全体構成図 / Overview — aims relationship + shared resources
2. 実験パラダイム図 / Paradigm — stimulus timing + conditions
3. 年次計画 / Gantt — timeline with milestones

Which should I generate? (e.g., "1 and 3", "all", "skip")

⛔ Wait for user response. Generate only the requested figures.

Grant-Specific Drafting Guidelines

KAKENHI:

• Write in formal Japanese academic style (である調, not です/ます調)
• Use 「」for Japanese quotations, bold for emphasis
• Structure: 研究の学術的背景 → 研究期間内に何をどこまで明らかにするか → 本研究の学術的な特色・独創性
• Include explicit 年次計画 (yearly plan) with concrete milestones
• Emphasize 社会的意義 (societal significance)
• Reference related KAKEN-funded projects to show awareness of the field

NSF:

• Write in clear, direct English
• Use Aim-based structure with bold headings
• Preliminary data paragraphs for each Aim (with figure references)
• Broader Impacts must be concrete: specific outreach activities, broadening participation plans
• Include Results from Prior Support (if PI has prior NSF funding)

NSFC:

• Write in formal Chinese academic style
• 立项依据 must position work at 国际前沿 (international frontier)
• 创新性 section must list numbered innovation points (创新点)
• 研究基础 must cite PI's own publications (with IF and citations if possible)
• 可行性分析 must address: technical feasibility, team capability, time feasibility, equipment/conditions

ERC:

• Write a compelling "high-risk/high-gain" narrative
• Extended Synopsis must be self-contained and compelling
• Include Work Package table with deliverables and milestones
• Gantt chart (describe in text, or generate as figure)

For Each Section

1. Pull relevant content from IDEA_REPORT.md, FINAL_PROPOSAL.md, literature notes
2. Write complete prose — no [TODO] except for PI-specific information
3. Include figure/table placeholders where appropriate (e.g., [Figure 1: System architecture])
4. Cite references properly — use citation keys, will build bibliography later
5. Match the agency's tone and style — formal Japanese for KAKENHI, direct English for NSF, etc.

Phase 4: External Review

Invoke /research-review on the complete draft for grant-type-specific evaluation:

/research-review "Complete [GRANT_TYPE] [GRANT_SUBTYPE] proposal draft. Evaluate as a [GRANT_TYPE] review panelist using official criteria. [PASTE FULL PROPOSAL TEXT]"

What this does:

• GPT-5.4 xhigh acts as a grant review panelist
• Scores each section 1-5 using agency-specific criteria
• Identifies fatal flaws and recommends funding/revisions/rejection
• Provides ranked action items for improvement
• All feedback saved to grant-proposal/GRANT_REVIEW.md

⚠️ Codex MCP fallback: If mcp__codex__codex is not available (no OpenAI API key), skip external review. Note "External review skipped — no Codex MCP available. Consider running /auto-review-loop-llm separately." in GRANT_REVIEW.md. The proposal is still usable without external review.

If /research-review is invoked (preferred), it handles the Codex call internally. If calling Codex directly (e.g., to maintain thread context from Phase 2):

Round 1 (full draft review):

mcp__codex__codex-reply:
  threadId: [from Phase 2]
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Review this complete [GRANT_TYPE] [GRANT_SUBTYPE] proposal draft.

    Act as a [GRANT_TYPE] review panelist. Evaluate using the official criteria:

    [INSERT GRANT-TYPE-SPECIFIC CRITERIA — see Grant Type Specifications above]

    For each section:
    1. Score 1-5 (5 = excellent)
    2. Strongest aspect
    3. Most critical weakness
    4. Specific fix suggestion (actionable, not vague)

    Overall assessment:
    - Would you recommend funding? (Yes / Yes with revisions / No)
    - Single most impactful change to improve funding chances?
    - Any fatal flaws?

    [PASTE FULL PROPOSAL TEXT]

Round 2+ (after revisions):

If MAX_REVIEW_ROUNDS > 1 and revisions were applied:

mcp__codex__codex-reply:
  threadId: [saved from Round 1]
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round N review of revised [GRANT_TYPE] [GRANT_SUBTYPE] proposal]

    Since your last review, I have applied the following changes:
    1. [Change 1]: [what was done]
    2. [Change 2]: [what was done]
    3. [Change 3]: [what was done]

    Please re-evaluate. Same format: section scores, overall assessment, remaining weaknesses.
    Focus on whether the CRITICAL and MAJOR issues from Round 1 have been adequately addressed.

    [PASTE REVISED PROPOSAL TEXT]

Phase 5: Revision & Output

5.1 Apply Reviewer Feedback

Parse reviewer feedback into severity levels:

• CRITICAL — fatal flaws that would lead to rejection. Fix immediately.
• MAJOR — significant weaknesses. Fix before submission.
• MINOR — suggestions for improvement. Fix if time allows.

Implement CRITICAL and MAJOR fixes. If MAX_REVIEW_ROUNDS > 1, re-submit for another round via mcp__codex__codex-reply.

5.2 Generate Output

Markdown output (default):

grant-proposal/
├── GRANT_PROPOSAL.md          # Complete proposal, all sections
├── GRANT_REVIEW.md            # Review history and reviewer feedback
├── GRANT_STATE.json           # State persistence file
├── figures/                   # Generated diagrams (if any)
└── references.bib             # Bibliography (if citations were used)

LaTeX output (when OUTPUT_FORMAT = latex):

grant-proposal/
├── main.tex                   # Master file
├── sections/
│   ├── aims.tex               # Specific Aims / Research Objective
│   ├── background.tex         # Background / Significance
│   ├── research_plan.tex      # Research Plan / Methods
│   ├── timeline.tex           # Timeline & Milestones
│   ├── pi_qualification.tex   # PI Qualification / Track Record
│   └── budget.tex             # Budget Justification (if applicable)
├── references.bib
└── figures/                   # Any generated diagrams

5.3 Final Checks

Before declaring done:

• All sections required by the grant type are present and complete
• Gap statement is clear and appears early in the proposal
• Each aim is independently valuable and logically connected
• Timeline includes concrete yearly milestones and deliverables
• PI qualification section has content (or clear [TODO] placeholders)
• Budget justification uses [AMOUNT] placeholders (no fabricated numbers)
• Language matches the grant type (Japanese for KAKENHI, Chinese for NSFC, etc.)
• No leftover [TODO] markers except for PI-specific information
• References are real (no hallucinated citations)
• Review feedback has been addressed (CRITICAL and MAJOR items)

🚦 Final Checkpoint: Present the completed proposal summary:

📝 Grant proposal draft complete:
- Type: [GRANT_TYPE] [GRANT_SUBTYPE]
- Language: [language]
- Aims: [N] aims covering [summary]
- Timeline: [N] years
- Review score: [summary from GPT-5.4]
- Output: grant-proposal/GRANT_PROPOSAL.md

Files saved to grant-proposal/. Please review and customize:
1. PI qualification section (add your publications and track record)
2. Budget amounts (replace [AMOUNT] placeholders)
3. Any [TODO] markers for personal information

What would you like to do next?
- "figures" → generate proposal diagrams
- "review again" → run another round of external review
- "latex" → convert to LaTeX format
- "done" → finalize

Key Rules

• Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.

• Do NOT fabricate budget amounts. Generate narrative budget justification only. Leave specific dollar/yen/yuan/euro amounts as [AMOUNT] placeholders for the user to fill in.

• Do NOT fabricate PI information. If no publication list is available, leave [TODO: Add publications] placeholders. Never invent papers, grants, or credentials.

• Do NOT hallucinate citations. Use references from literature survey. Mark uncertain citations with [VERIFY].

• Grant ≠ paper. A grant argues for future work (feasibility + potential). A paper argues for completed work (results + claims). Write accordingly — emphasize "what we will do" and "why it will work", not "what we found."

• Aims must be independently valuable. If Aim 2 fails, Aim 1 and Aim 3 should still produce publishable results.

• Preliminary data de-risks. Include any pilot results, existing datasets, or prior publications that demonstrate feasibility.

• Reviewer-facing structure. Bold key sentences. Use numbered lists for clarity. Make the reviewer's job easy.

• Cultural norms matter. KAKENHI expects 社会的意義; NSF expects Broader Impacts; NSFC expects 国际前沿 positioning. Missing these is a red flag for reviewers.

• Feishu notifications are optional. If ~/.claude/feishu.json exists, send checkpoint at each phase transition and pipeline_done at final output. If absent, skip silently.

Parameter Pass-Through

Parameters can be passed inline with — separator. They flow to sub-skills when invoked:

/grant-proposal "topic — KAKENHI Start-up, sources: zotero, arxiv download: true"

Parameter	Default	Description	Passed to
grant type	KAKENHI	Agency (KAKENHI/NSF/NSFC/ERC/DFG/SNSF/ARC/NWO/GENERIC)	—
grant subtype	auto	Sub-type (Start-up/Wakate/CAREER/Youth/etc.)	—
output format	markdown	markdown or latex	—
language	auto	Output language override	—
max review rounds	2	External review cycles	—
sources	all	Literature sources	→ /research-lit
arxiv download	false	Download arXiv PDFs	→ /research-lit
reviewer model	gpt-5.4	Codex review model	→ Codex MCP
auto proceed	false	Skip checkpoints	—

Composing with Other Skills

Sub-skills used by this skill

Sub-skill	Phase	Purpose
/research-lit	1	Literature survey (if not already done)
/novelty-check	1	Verify the gap is real
/research-review	2, 4	Structural review + full draft review
/paper-illustration	3	Generate proposal figures (optional)

Funding Track (this skill's primary use case)

/idea-discovery "direction"              ← Workflow 1: find validated ideas
/research-refine "idea"                  ← sharpen the method
/grant-proposal "idea — KAKENHI"         ← this skill: write the grant proposal
                                          ← [submit & get funded]
/experiment-bridge                       ← implement experiments with funding
/auto-review-loop "results"              ← Workflow 2: iterate until submission-ready
/paper-writing                           ← Workflow 3: write the paper

Publish Track (skip this skill)

/idea-discovery → /experiment-bridge → /auto-review-loop → /paper-writing → submit