grant-proposal

name	grant-proposal
description	Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an `/interview-me` spec, delegates the data-management plan to `/data-management-plan` and the facilities statement to `/capture-environment`, and emits a funder-requirements checklist. Use when user says "draft a grant", "write a proposal", "NSF proposal", "NIH aims", "ERC application", "foundation grant", "specific aims", or "scaffold a grant proposal". NOT a submission tool — produces a draft the user uploads to the sponsor's portal themselves.
argument-hint	[--funder nsf|nih|erc|foundation] [--input <spec>] [--out <dir>] [--no-verify]
disable-model-invocation	true
allowed-tools	["Read","Grep","Glob","Write","Task"]
effort	high

/grant-proposal — Research Grant Proposal Scaffolder

Compose a funder-shaped grant proposal draft from primitives you already have: an /interview-me research spec supplies the science, /data-management-plan supplies the DMP, /capture-environment supplies the facilities/computational statement, and /lit-review supplies the prior-work framing. This skill structures and stitches — it does not submit anywhere, and it does not invent identification strategy where a spec is absent.

Core principle: A proposal is a coherence artifact. Aims, methods, budget, timeline, and broader impacts must agree with each other and with the underlying research spec. The skill's main value-add over a blank template is the Phase 3 coherence pass (aims ↔ methods ↔ budget ↔ timeline).

When to use

• Drafting an NSF / NIH / ERC / foundation proposal from an existing research idea.
• Turning an /interview-me spec (or a /preregister PAP) into a fundable narrative.
• Assembling the boilerplate-but-required pieces (DMP, facilities, data-sharing) so the human writes only the science.
• During resubmission, re-scaffolding aims after a reviewer "revise and resubmit" round.

When NOT to use

• You need the science itself invented — run /interview-me first; this skill refuses to fabricate an identification strategy.
• The sponsor is a clinical-trial funder requiring its own protocol template (out of scope; use the sponsor's native forms).
• You want a finished, submittable PDF — this writes Markdown sections + a checklist; final assembly into the sponsor's format is yours.

Funder profiles

Generic across sponsors via placeholder profiles. --funder selects the section set + naming; default is nsf.

Funder	Core sections (named per sponsor)	Page/format signals
nsf	Project Summary (Overview/Intellectual Merit/Broader Impacts) · Project Description · Broader Impacts · Data Management & Sharing Plan · Facilities/Equipment · Budget Justification	15-page Project Description; DMSP required
nih	Specific Aims (1 p.) · Research Strategy (Significance/Innovation/Approach) · Vertebrate/Human Subjects (if any) · Data Management & Sharing · Facilities & Other Resources · Budget Justification	Aims page is load-bearing
erc	Extended Synopsis (B1) · Scientific Proposal (B2: state-of-art, objectives, methodology) · CV + track record · Resources/Budget · Data Management	PI-centric; "high-risk/high-gain" framing
foundation	Project Summary · Statement of Need · Goals & Objectives · Methods/Approach · Evaluation Plan · Budget Justification · Sustainability	Mission-fit framing; lighter methods

Economics framing is the primary lens (DiD/event-study, IV, RCT, panel; AEA Data Editor / openICPSR / DCAS data-sharing expectations), but the section scaffold is field-agnostic — a biology or CS forker fills the same slots.

Workflow

Phase 0 — Detect funder + spec

1. Resolve --funder (or infer from the request wording; default nsf). Echo the chosen profile back before drafting.
2. Locate the research spec: --input <path>, else the most recent quality_reports/specs/research_spec_*.md from /interview-me. If none exists, stop and recommend /interview-me — do not invent the science.
3. From the spec, extract: research question, hypotheses (directional), identification strategy (DiD / IV / RDD / RCT / structural), data sources, sample, expected results, contribution. Record any paper_type: field.
4. Scan quality_reports/ for adjacent artifacts to reuse: a /lit-review synthesis (prior work), a /preregister PAP (analysis plan), a passport.yaml or /data-analysis outputs (preliminary results).

Phase 1 — Scaffold sections from templates + the spec

Generate the funder's section set. Map spec content into slots:

• Specific Aims / Project Summary — RQ + 2–3 numbered, directional aims drawn from the spec's hypotheses.
• Background & Significance — motivation + prior work; pull citations from the /lit-review synthesis if present (do not re-search unless asked).
• Research Design & Methods — lift the identification strategy verbatim from the spec (estimand, treatment/control, identifying assumption, robustness: pre-trends, placebo, clustering). Name the estimator concretely (e.g. fixest::feols, did::att_gt, Stata csdid).
• Preliminary Results — summarize any existing /data-analysis / passport outputs; otherwise mark [PRELIMINARY RESULTS: none yet — describe planned pilot].
• Timeline & Milestones — quarter/year table aligned to the aims (every aim gets a milestone).
• Broader Impacts / Significance — sponsor-appropriate framing (NSF Broader Impacts vs NIH Significance vs foundation mission-fit).
• Budget Justification skeleton — personnel / data acquisition / compute / travel / dissemination line-item stubs, each tied to an aim.

For every MUST slot the spec did not supply, write [CLARIFY: <specific question>] — never fabricate. Re-use the MUST / SHOULD / MAY clarity language from templates/requirements-spec.md.

Phase 2 — Compose the DMP and computational statements (delegate)

1. Data Management (& Sharing) Plan — invoke /data-management-plan via Task with the funder + data sources from the spec. It returns the DMP section (repository choice — openICPSR / Dataverse / Zenodo, access/retention, FAIR/DCAS alignment). If any data source is sensitive (restricted-use admin data, PII, IRB-restricted), have it honor .claude/rules/confidential-data.md and describe access via a secure enclave / FSRDC rather than open release. Do not draft a sharing plan that promises to release confidential data.
2. Facilities / Computational-Environment statement — invoke /capture-environment via Task to produce the compute/software/dependency statement (cluster, R/Stata/Python toolchain, renv.lock / DESCRIPTION / requirements.txt provenance) for the Facilities section.

If a delegate skill is unavailable, leave a [DELEGATE: /data-management-plan] placeholder rather than half-writing its output.

Phase 3 — Coherence pass (aims ↔ methods ↔ budget ↔ timeline)

The differentiating step. Cross-check the assembled draft and report mismatches:

• Aims ↔ Methods — every aim has a named method/estimator; no orphan method serves no aim.
• Methods ↔ Budget — each cost line traces to an aim (e.g. an RCT aim implies a participant-incentives line; admin data implies an acquisition/enclave line; a large simulation implies a compute line).
• Aims ↔ Timeline — every aim has at least one milestone; no milestone is unattributed.
• DMP ↔ Methods — the data named in Methods matches the data described in the DMP; confidential sources are not promised as open.
• Page/format budget — flag sections likely to overflow the funder's page limit (NSF 15-page Project Description, NIH 1-page Aims).

Phase 4 — Post-flight verification + output

• Post-flight (CoVe): if Background/Significance cites prior literature, run the Post-Flight protocol from .claude/rules/post-flight-verification.md — spawn claim-verifier via Task (context: fork) on the citations. Surface PASS / PARTIAL / FAIL. Skip on --no-verify or zero citations.
• Write sections to --out (default quality_reports/grants/YYYY-MM-DD_<slug>/), one Markdown file per section plus checklist.md.

Output / Report format

A proposal_draft.md (concatenated sections) plus a checklist.md:

# Grant Proposal Draft — [Title]
**Funder:** NSF | NIH | ERC | foundation     **Date:** YYYY-MM-DD
**Source spec:** quality_reports/specs/research_spec_<slug>.md

## Funder-Requirements Checklist
| Requirement | Status | Source |
|---|---|---|
| Project Summary / Specific Aims | DRAFTED | Phase 1 |
| Research Design & Methods | DRAFTED | spec |
| Data Management & Sharing Plan | DELEGATED | /data-management-plan |
| Facilities / Computational Env | DELEGATED | /capture-environment |
| Budget Justification | SKELETON | Phase 1 |
| Broader Impacts / Significance | DRAFTED | Phase 1 |
| [n] [CLARIFY:] items unresolved | TODO | — |

## Coherence Report
- Aims ↔ Methods: PASS / [n issues]
- Methods ↔ Budget: PASS / [n issues]
- Aims ↔ Timeline: PASS / [n issues]
- DMP ↔ Methods (confidential-data check): PASS / [n issues]
- Page-budget flags: [sections at risk of overflow]

## Post-Flight Verification
Claims extracted: N · Verified: N · Outcome: PASS / PARTIAL / FAIL

Exit behavior

• All MUST slots filled + coherence PASS: report "DRAFT READY — review [CLARIFY:] items, then assemble in the sponsor's portal."
• Open [CLARIFY:] / [DELEGATE:] items or coherence issues: report "INCOMPLETE — N items unresolved" and list them. The skill never blocks like /audit-reproducibility (it is a drafting tool, not a gate) — it surfaces, the author resolves.
• No research spec found: stop in Phase 0 and recommend /interview-me. Nothing is written.

Flags

• --funder <nsf|nih|erc|foundation> — Select the funder profile that shapes section structure and the requirements checklist.
• --input <spec> — Path to an existing /interview-me research spec to seed Aims and Methods (otherwise the skill elicits them).

Cross-references

• .claude/skills/interview-me/SKILL.md — produces the research spec this skill consumes; run it first if none exists.
• .claude/skills/data-management-plan/SKILL.md — Phase 2 delegate for the DMP/DMSP section.
• .claude/skills/capture-environment/SKILL.md — Phase 2 delegate for the facilities/computational statement.
• .claude/skills/lit-review/SKILL.md — supplies Background & Significance prior-work framing.
• .claude/skills/preregister/SKILL.md — a PAP can seed the analysis plan; preregistration is the forward commitment a funded project then executes.
• .claude/rules/confidential-data.md — governs how sensitive data sources appear in the DMP and budget.
• .claude/rules/post-flight-verification.md — Phase 4 citation fact-check.

What this skill does NOT do

• Submit anywhere. It writes Markdown + a checklist; you assemble and upload to Research.gov / ASSIST / the ERC portal / the foundation's system.
• Invent the science. No spec → no proposal. It will not fabricate an identification strategy, hypotheses, or aims.
• Write the DMP or facilities statement itself. Those are delegated to /data-management-plan and /capture-environment; this skill only stitches their output into the funder's section set.
• Compute the budget. It scaffolds line items tied to aims; actual dollar figures, indirect-cost rates, and effort percentages are the PI's and the grants office's job.
• Guarantee page-limit compliance. It flags likely overflow; final trimming to the sponsor's exact format is manual.