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#research
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.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
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https://deepseekmodel.com/api/download.php?id=wanshuiyin-auto-claude-code-research-in-sleep-skills-grant-proposal-skill-md&format=skill
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标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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": "completed" . Workflow Phase 0: Input Parsing & Context Gathering Parse $ARGUMENTS to extract: Research direction/idea — may reference existing files or be a freeform description Grant type — detect from keywords (e.g., "科研費"→KAKENHI, "NSF"→NSF, "国自然"→NSFC, "基金"→NSFC) Grant sub-type — detect from keywords (e.g., "Start-up", "若手", "青年", "CAREER", "优青", "海外优青") Overrides — output format, language, review rounds Then gather context from the project directory: Read idea-stage/IDEA_REPORT.md if it exists (from /idea-discovery ); fall back to ./IDEA_REPORT.md if not found Read refine-logs/FINAL_PROPOSAL.md if it exists (from /research-refine ) Read refine-logs/EXPERIMENT_PLAN.md if it exists (from /experiment-plan ) Read review-stage/AUTO_REVIEW.md if it exists (from /auto-review-loop — prior review feedback is gold for grants); fall back to ./AUTO_REVIEW.md if not found Read NARRATIVE_REPORT.md or STORY.md if they exist Read any existing literature notes or survey documents
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |