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grants

Grant writing and proposal architecture: funder fit, proposal structure, budget design, and success-factor scoring. Use when writing a grant proposal, evaluating funder fit, auditing a draft for competitiveness, or planning a budget.

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含まれるファイル(7)

  • SKILL.md6.9 KB
  • references/budget-design-and-justification.md7.2 KB
  • references/funder-fit-and-research-strategy.md6.6 KB
  • references/proposal-structure-and-narrative.md7.8 KB
  • scripts/budget_realism_checker.py9.7 KB
  • scripts/funder_fit_scorer.py7.4 KB
  • scripts/proposal_structure_validator.py8.7 KB

SKILL.md(原文)

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Grant Writing & Proposal Architecture

A skill for crafting competitive grant proposals across funder types: government (NIH, NSF, DOE, ARPA), foundation, corporate, philanthropic, and SBIR / STTR. Focuses on the architecture of a winning proposal: fit, structure, narrative, budget — not boilerplate templating.

When to use this skill

  • Evaluating funder fit before investing weeks in a proposal
  • Designing the proposal structure for a specific funder
  • Writing or auditing the narrative for competitiveness
  • Designing a realistic, defensible budget
  • Pre-submission proposal review for common failure modes
  • Building a grants strategy (which to apply to over the year)

Inputs the advisor expects

  • The funder name + specific program / RFP
  • The research / project idea (problem, approach, outcomes)
  • Team composition (PI, co-investigators, key personnel)
  • Institutional / org context
  • Past funding history
  • Budget envelope (or constraint)
  • Submission deadline

Clarify First

Before generating the proposal, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Funder + specific program / RFP — sets the mental model (NIH 5-criteria vs NSF merit+impact vs foundation mission fit); drives structure and narrative
  • Project idea (problem, approach, outcomes) — drives the significance/innovation narrative and the Heilmeier answers
  • Budget envelope — drives budget design and whether scope matches the funder's typical award size
  • Team composition (PI, key personnel) — drives the investigator/environment fit dimension

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Workflows

Workflow 1 — Score funder fit before committing

  1. Capture funder, program, project idea, team strengths.
  2. Run funder_fit_scorer.py to grade fit on 7 dimensions.
  3. If fit < 65, look for better-aligned funder; don't waste 4 weeks.
python3 grants/scripts/funder_fit_scorer.py \
  --input funder_fit.json --format markdown

Workflow 2 — Validate proposal structure against funder expectations

  1. Capture the proposal section list + page allocation.
  2. Run proposal_structure_validator.py against funder type expectations.
  3. Adjust before drafting deep.
python3 grants/scripts/proposal_structure_validator.py \
  --input proposal_structure.json --funder-type nih --format markdown

Workflow 3 — Audit budget for realism

  1. Capture budget line items with justifications.
  2. Run budget_realism_checker.py against funder norms + project scope.
  3. Adjust before submission.
python3 grants/scripts/budget_realism_checker.py \
  --input budget.json --format markdown

Decision frameworks

Funder fit dimensions

  1. Topic alignment — does the funder fund this area?
  2. Mechanism alignment — does the funder fund this kind of work (R&D, services, scale-up)?
  3. Stage alignment — early-stage / mid / scale?
  4. Geographic alignment — does the funder fund your region?
  5. Team profile alignment — does the funder fund your kind of team?
  6. Budget envelope alignment — does the funder's typical award size match?
  7. Competitive density — is it 5% acceptance or 35%?

A score below 65 across these is usually a "skip this funder" signal.

Funder type — distinct mental models

Funder typeEmphasizesDe-emphasizes
NIHSignificance + innovation + approach + investigator + environment (5 criteria)Commercial outcome
NSFIntellectual merit + broader impactsDirect commercial outcome
ARPA / DARPAHeilmeier catechism (defined moonshot question)Incremental work
SBIR / STTRCommercial path + technical riskPure science
FoundationMission fit + measurable outcomesPure academic novelty
CorporateCommercial relevance to sponsorIndependence from sponsor
CrowdfundingStory + community appealTechnical rigor

Write to the funder's mental model, not a generic "good grant."

The Heilmeier catechism (good for any proposal)

  1. What are you trying to do?
  2. How is it done today; what are the limits?
  3. What's new in your approach; why succeed?
  4. Who cares; if you succeed, what difference does it make?
  5. What are the risks; how will you mitigate?
  6. How much will it cost; how long?
  7. What are the mid-term + final outcomes you'll deliver?

A proposal that can't answer all seven crisply isn't ready.

Common engagements

"Help me decide between two RFPs"

  1. Score both for fit; the higher one is usually right.
  2. If close: which has earlier deadline / smaller proposal effort?
  3. Don't submit to both same year unless funders are independent.

"Audit my draft proposal"

  1. Check funder-fit assumptions (did the program actually fund what you're proposing?)
  2. Check structure against funder template
  3. Check narrative: is the problem compelling? approach novel?
  4. Check budget: realistic + justified
  5. Check team credentials: matches scope?
  6. Read for: jargon, vague claims, unjustified assumptions

"We've never applied for an NIH R01. What's the prep?"

  1. Smaller grant first (R21, K, F32) if eligible — build track record
  2. Talk to a program officer before drafting (essential)
  3. Pre-submission inquiry where allowed
  4. Get a mock review from someone who's reviewed for NIH

Anti-patterns to avoid

  • Applying without funder fit. Wastes 4-8 weeks.
  • Generic proposal sent to multiple funders. Each wants a specific mental model.
  • Budget that doesn't match scope. Reviewer red flag.
  • Vague significance statement. "This is important" without specifics.
  • No risk discussion. Reviewers know there's risk; not acknowledging it = naive.
  • Team without right credentials. Match key personnel to scope.
  • Submitting at last minute. Errors; missed letters of support.

References

  • references/funder-fit-and-research-strategy.md — fit dimensions, funder types, multi-funder strategy
  • references/proposal-structure-and-narrative.md — per-funder structures, narrative discipline
  • references/budget-design-and-justification.md — budget categories, indirect costs, common errors

Related skills

  • research/litreview — literature review for proposals
  • c-level-advisor/general-counsel-advisor — legal review of terms
  • c-level-advisor/cfo-advisor — financial review

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