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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

Install / Use

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill grant-proposal

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Supported Platforms

Universal

Tags

Our assessment of grant-proposal

grant-proposal scores 98/100 on our quality scale, 8th of 188 Customer Support skills we index (top 5%).

Its SKILL.md is 33 KB long, well organised into 48 sections with 25 code examples: a thorough specification that gives an agent plenty to work with.

With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 9 days ago, so grant-proposal is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

grant-proposal compared with similar skills

All 4 of these similar skills score higher than grant-proposal; compare them before choosing.

SkillScoreStarsUpdatedFormat
grant-proposal (this skill)by wanshuiyin9816.6k9d agoSKILL.md
algorithmic-artby anthropics100177.9k5d agoSKILL.md
pptxby anthropics100177.9k5d agoSKILL.md
designby nextlevelbuilder100130.2k6d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k6d agoSKILL.md

Frequently asked questions

How do I install grant-proposal?
Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill grant-proposal. The install tabs above show the steps for each supported agent.
Which AI agents does grant-proposal work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is grant-proposal safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is grant-proposal still maintained?
The repository was last updated 9 days ago, so grant-proposal is actively maintained.

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] [— style-ref: <source>]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, 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-6-astra — Model used via Codex MCP for proposal review. Must be an OpenAI model (e.g., gpt-6-astra, 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:

# Resolve $STYLE_HELPER via the canonical strict-safe chain (see
# shared-references/integration-contract.md §2). Policy A — gate:
# unresolved helper means --style-ref cannot be satisfied, so abort.
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
STYLE_HELPER=".aris/tools/extract_paper_style.py"
[ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py"
[ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; }
[ -f "$STYLE_HELPER" ] || {
  echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
  echo "       Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2
  echo "       --style-ref cannot be satisfied; aborting." >&2
  exit 1
}
STYLE_STATUS=0
CACHE=$(python3 "$STYLE_HELPER" --source "<source>") || STYLE_STATUS=$?
case "$STYLE_STATUS" 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-6-Astra 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-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": "complet

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars16.6k
CategoryCustomer
Updated9d ago
Forks1.4k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions