/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   # Use to verify current salary caps, deadlines, or solicitation details
  - WebSearch  # Use to look up specific program announcements or funding opportunities

Grant Proposal Skill

Core Philosophy

Three principles govern every interaction:

1. **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. 2. **Write like a domain expert, not a template filler.** Generic language kills proposals. Every sentence must reflect deep knowledge of the specific field. 3. **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

1. **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. 2. **Never modify the user's original files without confirmation.** If the user provides source files, work on copies. Always ask before writing back. 3. **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. 4. **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 compl