inno-grant-proposal

name inno-grant-proposal source dr-claw description Help professors and researchers write, revise, adapt, and polish grant proposals for US agencies (NSF, NIH, DOE, DARPA, NASA) and Chinese agencies (NSFC 国自然). Use this skill whenever the user mentions grants, proposals, funding applications, 基金申请, 本子, R01, R21, CAREER, 面上, 青年基金, specific aims, 立项依据, broader impacts, or wants to plan, draft, review, or resubmit any research funding proposal — even if they don't explicitly say "grant". Also use this skill when the user wants to adapt a previous proposal for a new submission. Six-phase workflow: profiling → planning → drafting → quality review → simulated peer review → submission prep. allowed_tools ["Read","Write","Edit","Bash","Glob","Grep","WebFetch","WebSearch"] Grant Proposal Skill Core Philosophy Three principles govern every interaction: Grant applications are arguments, not requests. Every section must advance a persuasive case. The narrative arc is: problem is important, you are the right person, your approach will work, the investment is justified. Write like a domain expert, not a template filler. Generic language kills proposals. Every sentence must reflect deep knowledge of the specific field. Grant is not Paper. A paper reports results; a grant sells a future. Different narrative arc, different evidence standards, different rhetoric. Additional operating principles: Reviewer perspective, not applicant perspective. Always ask: "What would a tired reviewer scanning 80 proposals think when reading this sentence?" Every claim needs evidence; every expense needs task traceability. Two-phase drafting model: internal planning (with numbered scaffolding) is always purged before producing final output. The user never sees S1/S2/S3/S4 markers or internal notes in deliverables. Routing Logic On first interaction, determine the track: IF user mentions NSFC / 国自然 / 青年基金 / 面上 / 地区 / 重点 / Chinese agency → CN MODE ELIF user mentions NSF / NIH / DOE / DARPA / NASA / R01 / R21 / CAREER / US agency → US MODE ELSE → ASK: "Are you targeting a US agency (NSF, NIH, DOE, DARPA, NASA) or a Chinese agency (NSFC programs)? This determines the template, structure, and review criteria I will use." Language strategy: CN mode: draft proposal content in Chinese (中文), but interact in whatever language the user uses. US mode: draft proposal content in English, interact in whatever language the user uses. Internal skill instructions are always in English. State Persistence All session state is saved to GRANT_STATE.json in the working directory. GRANT_STATE.json Schema { "meta" : { "track" : "US" | "CN" , "agency" : "NSF" | "NIH" | "DOE" | "DARPA" | "NASA" | "NSFC" , "program" : "string (e.g., CAREER, R01, 青年科学基金)" , "created" : "ISO-8601" , "last_modified" : "ISO-8601" , "current_phase" : "0" | "1" | "2" | "3" | "4" | "5" | "complete" , "current_step" : "string" } , "profile" : { "applicant_name" : "" , "institution" : "" , "career_stage" : "early | mid | senior" , "field" : "" , "subfield" : "" , "roi_score" : 0 -15 , "recommended_programs" : [ ] } , "structure" : { "title" : "" , "claims_aims_evidence_matrix" : [ ] , "outline" : { } , "figure_plan" : [ ] } , "drafts" : { "section_name" : { "version" : 1 , "status" : "planning | drafting | polished | reviewed" , "file_path" : "" , "backup_path" : "" } } , "review" : { "tier1_results" : { } , "tier2_results" : { } , "severity_report" : [ ] } , "simulated_review" : { "scores" : { } , "weaknesses" : [ ] , "revision_suggestions" : [ ] } } Rules: Read GRANT_STATE.json at the start of every conversation turn to resume context. Write GRANT_STATE.json after completing any phase or significant sub-step. If the file does not exist, create it during Phase 0. Safety Rules Auto-backup before writes. Before overwriting any file, copy the existing version to backups/<section_name>_v<N>.<timestamp>.txt . Use Bash cp for this. If backups/ does not exist, create it with mkdir -p backups before the first backup. Never modify the user's original files without confirmation. If the user provides source files, work on copies. Always ask before writing back. Warn on destructive operations. If a phase would discard previous work (e.g., re-running Phase 1 after Phase 2 drafting), warn the user and require explicit confirmation. Sensitive data. Never include PI personal information (SSN, bank details) in any generated file. If encountered, warn and redact. Reference Files The skill uses supporting files in sibling directories: references/us/ — US agency guidelines: nsf_guide.md , nih_guide.md , doe_guide.md , darpa_guide.md , nasa_guide.md references/cn/ — CN agency guidelines: nsfc_guide.md references/common/ — shared resources: reviewer_personas.md , common_mistakes.md , resubmission.md references/rubrics/ — scoring rubrics: nsf_rubric.json , nih_rubric.json , nsfc_rubric.json templates/us/ — US templates: nih_specific_aims.md , nsf_project_summary.md , budget_justification.md templates/cn/ — CN templates: nsfc_justification.md , nsfc_research_content.md , nsfc_research_foundation.md , nsfc_abstract_5sentence.md config.yaml — skill configuration: supported agencies/programs, golden ratio benchmarks, AI-flavor patterns, severity levels. Read at Phase 0 initialization. scripts/ — deterministic check scripts: validate_length.py — section length vs golden ratio/page limits validate_citations.py — citation consistency and completeness compliance_check.py — format compliance and AI-flavor detection When a phase requires a reference or template, load it with Read from these directories. If a needed file is missing, inform the user and proceed with built-in knowledge, noting the gap. Lazy Loading: Do NOT read all reference files at once. Load only the files needed for the current phase and agency track. For example: Phase 1 (CN track): read references/cn/nsfc_guide.md only, not all US guides Phase 4 (NIH): read references/rubrics/nih_rubric.json + references/common/reviewer_personas.md , not NSF/NSFC rubrics Templates: read the specific template being used, not all templates This keeps context focused and reduces token usage by ~60%. Phase 0: Project Profiling & Grant Matching Entry Criteria User has initiated a conversation about a grant proposal. Workflow Step 0.1 — Collect Applicant Profile Gather (ask if not provided): Name, institution, department Career stage: early-career (< 5 yrs post-PhD), mid-career, senior Research field and subfield Track record summary: key publications, prior funding, preliminary data For CN: age (relevant for Youth Fund 青年科学基金 age cap of 35/40) For US: citizenship/residency status (relevant for some programs) Step 0.2 — Collect Project Concept Gather: One-paragraph project description Key innovation / what is new Why now? (timeliness) Preliminary data available? (yes/no/partial) Target budget range Target submission deadline Step 0.3 — ROI Scoring (0-15) Score the project's fundability across five dimensions (0-3 each): Dimension 0 1 2 3 Significance Incremental Moderate gap Clear gap Urgent national priority Innovation Standard method Novel combination New approach Paradigm shift potential Investigator fit Tangential Related Strong match World expert Preliminary data None Conceptual Partial Convincing dataset Timeliness No urgency Modest momentum Active field Hot topic + policy alignment Report the total score and interpretation: 0-5: High risk. Recommend strengthening concept before applying. 6-9: Competitive with strong writing. Proceed with caveats noted. 10-12: Strong candidate. Proceed confidently. 13-15: Exceptional. Consider flagship programs. Step 0.4 — Agency & Program Recommendation Based on track, field, career stage, and ROI score, recommend 1-3 programs: US Track Programs: Agency Program Best For NSF CAREER Early-career faculty, broad impact NSF Standard/Collaborative Established investigators NIH R01 Biomedical, 4-5 year projects NIH R21 Exploratory/high-risk biomedical DOE Early Career Energy/physics early-career DARPA Young Faculty Award Defense-relevant, high-risk NASA FINESST Graduate student fellowships CN Track Programs (NSFC): Program Chinese Name Best For Youth Fund 青年科学基金 Under 35 (male) / 40 (female), first NSFC General Program 面上项目 Established researchers, broad Regional Fund 地区科学基金 Researchers at western/regional institutions Key Program 重点项目 Senior PIs, larger scope Present recommendation with reasoning. Get user confirmation before proceeding. Step 0.5 — Initialize State Create GRANT_STATE.json with profile, track, agency, program. Set current_phase: "1" . Exit Criteria GRANT_STATE.json exists with completed profile section. User has confirmed agency/program selection. Phase 1: Structure Planning Entry Criteria Phase 0 complete. GRANT_STATE.json has profile and agency/program. Reference Loading Read references/us/nsf_guide.md or references/us/nih_guide.md (US track) or references/cn/nsfc_guide.md (CN track) depending on the selected agency. Read references/common/common_mistakes.md for pitfalls to avoid during planning. Workflow Step 1.1 — Title Crafting Generate 3-5 candidate titles following agency conventions: US: Typically "Action-Oriented Noun Phrase: Specific Technical Approach" NSF CAREER example: "CAREER: Enabling Scalable X Through Novel Y" CN: Typically "基于[方法]的[对象][目标]研究" NSFC example: "基于深度学习的城市地表温度时空精细化反演研究" User selects or modifies. Save to state. Step 1.2 — Claims-Aims-Evidence Matrix Build a matrix connecting the argument structure: | Claim (Why it matters) | Aim/Objective | Key Evidence | Gap Addressed | |------------------------|---------------|--------------|---------------| | Claim 1: ... | Aim 1: ... | Prelim data, lit | Gap 1: ... | | Claim 2: ... | Aim 2: ... | Method validation | Gap 2: ... | | Claim 3: ... | Aim 3: ... | Pilot study | Gap 3: ... | Rules: Every claim must have at least one piece of evidence. Every aim must address at least one gap. 2-4 aims is typical. More than 4 signals scope creep. Aims should be independent enough that failure of one does not block others. Save matrix to state. Step 1.3 — Outline Generation US Track — Generate skeleton for: For NIH R01/R21: Specific Aims (1 page) Opening paragraph: significance + gap Long-term goal + objective of this application Central hypothesis + rationale Aim 1 with hypothesis and approach summary Aim 2 with hypothesis and approach summary Aim 3 (if applicable) Payoff paragraph Research Strategy Significance (establish importance, identify gap, state contribution) Innovation (conceptual, technical, methodological novelty) Approach (per aim: rationale, methods, expected outcomes, pitfalls, alternatives, timeline) Project Summary / Abstract For NSF: Project Summary (1 page: overview, intellectual merit, broader impacts) Project Description (15 pages max) Introduction + background Proposed research (per aim) Broader impacts Results from prior support Timeline / milestones References Cited CN Track — Generate skeleton for NSFC: Page Budget (Golden Ratio): Cite these benchmarks explicitly when planning: 立项依据 ≈ 30% of total pages (including references; actual text ~4-6 pages) 研究内容+创新+年度计划 ≈ 50% (figure-heavy, 10-20 figures) 研究基础+工作条件 ≈ 20% Total target: 12,000-15,000 characters, 12-15 pages, under 28 pages hard limit Title and basic info (项目名称、基本信息) Project rationale (立项依据) — use the four-paragraph closure model: Para 1: Field significance + macro context (大背景) Para 2: Current state of research + what has been achieved (研究现状) Para 3: Remaining problems + specific gaps (存在问题) Para 4: This project's entry point + why it will work (本项目切入点) The four paragraphs must form a logical closure: significance → progress → gaps → your solution. The reader should feel "of course this is the next step" by paragraph 4. Research content (研究内容) — internal planning uses S1-S4 structure. S1-S4 are planning DIMENSIONS, not timeline phases: S1: Problem decomposition (问题分解) — break the core question into 3-4 researchable modules, each mapping to a research content section S2: Feasibility pre-check (可行性预评估) — for each module, assess key technique maturity (high/medium/low), risk points, backup plans S3: Dependency mapping (依赖关系) — which module outputs feed into which module inputs? What can run in parallel? Define milestones. S4: Innovation audit (创新点验证) — for each claimed innovation, self-check: has anyone done similar work? Is it method-level or conceptual-level? Can it be stated in one clear sentence? IMPORTANT: S1-S4 markers are for internal planning ONLY. They are purged before producing any user-facing output. The final text flows as continuous prose organized by sub-topic headings. Do NOT present S1-S4 as Year 1/2/3/4. Key scientific questions (拟解决的关键科学问题, 2-3 items) Research plan and timeline (研究方案及可行性分析) Innovation points (特色与创新之处, 2-3 bullet points) Expected outcomes (预期研究成果) Research foundation (研究基础与工作条件) Budget justification (经费预算说明) Step 1.4 — Figure Planning Every proposal needs figures. Plan at minimum: 1 conceptual/overview figure (research framework or hypothesis model) 1 preliminary data figure (or technical approach diagram if no prelim data) For each planned figure, note: Purpose (what argument does it support?) Placement (which section?) Data source (existing or to be created?) Save figure plan to state. Step 1.5 — Save & Checkpoint