/aris-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
$ npx -y skills add OpenLAIR/dr-claw --skill aris-grant-proposal --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/aris-grant-proposal
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
aris-grant-proposal.SKILL.mdname: aris-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
license: MIT
metadata:
author: wanshuiyin/ARIS
version: "1.0.0"
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:
/aris-research-lit → /aris-novelty-check → [structure design] → [draft] → /aris-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 `/aris-idea-discovery` produces validated ideas, the user can either:
- Go to `/aris-experiment-bridge` → `/aris-auto-review-loop` → `/aris-paper-writing` (implement & publish)
- Go to `/aris-grant-proposal` (write funding application first, then implement after funding)
┌→ /aris-experiment-bridge → /aris-auto-review-loop → /aris-paper-writing (publish track)
/aris-idea-discovery ────┤
└→ /aris-grant-proposal → [get funded] → /aris-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., `/aris-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., `/aris-grant-proposal "topic — NSF CAREER, latex output"` or `/aris-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
Read more
name: aris-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 license: MIT metadata: author: wanshuiyin/ARIS version: "1.0.0"
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:
/aris-research-lit → /aris-novelty-check → [structure design] → [draft] → /aris-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 `/aris-idea-discovery` produces validated ideas, the user can either:
- Go to `/aris-experiment-bridge` → `/aris-auto-review-loop` → `/aris-paper-writing` (implement & publish)
- Go to `/aris-grant-proposal` (write funding application first, then implement after funding)
┌→ /aris-experiment-bridge → /aris-auto-review-loop → /aris-paper-writing (publish track)
/aris-idea-discovery ────┤
└→ /aris-grant-proposal → [get funded] → /aris-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., `/aris-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., `/aris-grant-proposal "topic — NSF CAREER, latex output"` or `/aris-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
A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power.
Repo: OpenLAIR/dr-claw
Other skills on dr-claw.
- /dr-claw
Dr. Claw skill for OpenClaw project discovery, idea intake, waiting-session triage, structured session control, event-driven notifications, and mobile reporting through the local drclaw CLI.
Open skill - /academic-researcher
Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research,
Open skill - /autogpt
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
Open skill - /crewai
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical
Open skill - /langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering
Open skill - /llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG
Open skill

