data-management-plan

name	data-management-plan
description	Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data management plan", "DMP", "DMSP", "NIH data sharing plan", "write the data plan for my grant", or when a grant proposal needs a data-management section. NOT a submission tool — produces a draft the user pastes into the funder portal (DMPTool, NIH ASSIST, Horizon Europe portal).
argument-hint	[--funder nsf|nih|erc|horizon] [--input <spec-or-proposal>] [--no-verify]
disable-model-invocation	true
allowed-tools	["Read","Grep","Glob","Write","Task"]
effort	medium

/data-management-plan — Funder-Compliant DMP Generator

Produce a Data Management Plan ready to paste into a funder portal. This skill writes the prose and structure; it does not submit anywhere. It is a composition skill — it folds the disclosure-avoidance / IRB rules from .claude/rules/confidential-data.md and the environment + replication-package plan from /capture-environment and /replication-package into a single funder-shaped document.

When to use

• Writing a grant proposal. Every NSF, NIH, ERC, and Horizon Europe proposal needs a DMP (NSF), DMS Plan (NIH 2023 policy), or Data Management Plan (ERC/Horizon). /grant-proposal calls this skill for that section.
• Before data collection on a funded project. The plan is a commitment you make at award time and report against at renewal.
• When restricted or human-subjects data is involved. The access/sharing and preservation sections change materially — see Phase 2.

When NOT to use

• For a clinical-trial data-sharing statement governed by ICMJE / ClinicalTrials.gov — use the trial sponsor's template.
• As a substitute for IRB protocol text — the DMP references IRB constraints; it is not the protocol itself.

Inputs

• $0 --funder nsf|nih|erc|horizon — target funder profile. If omitted, Phase 0 detects it from --input or asks once.
• --input <path> — a research spec (/interview-me output under quality_reports/specs/), a grant draft, or a passport-adjacent description. The skill extracts data types, sample, and identification strategy from it.
• --no-verify — skip the Phase 4 citation/standard post-flight (inherited from /preregister).

Workflow

Phase 0 — Detect funder + data sensitivity

1. Resolve the funder (--funder, else infer from --input, else ask once). Load its section schema:

   Funder	Plan name	Required sections (abridged)
   NSF	Data Management Plan (2 pp max)	data types · standards · access/sharing · re-use/redistribution · archiving
   NIH	DMS Plan (2023 policy)	data type · tools/software · standards · preservation/access/timelines · access/distribution + reuse · oversight
   ERC	DMP (Horizon Europe Annex)	FAIR per dataset · data summary · making data FAIR · resource allocation · security · ethics
   Horizon Europe	DMP (DMP template)	same FAIR-first structure as ERC; open by default, "as open as possible, as closed as necessary"

2. Classify the data on three axes (drives Phases 2–3):

   • Public (open survey, scraped public records, simulated) — minimal restrictions.
   • Restricted (admin/tax/Census, proprietary, licensed under DUA) — access procedures dominate.
   • Human-subjects (PII, biospecimen-linked, survey with identifiers) — IRB + disclosure avoidance dominate.

   If the data is restricted or human-subjects, set sensitive = true and run Phase 2. If it is purely public, Phase 2 is a short paragraph.

Phase 1 — Scaffold sections from the funder profile

Generate the six house sections, mapped onto the funder's required headings:

1. Data description & types — what data, source, volume, formats produced. Be specific: panel/admin microdata, RCT outcomes, event-study event files, replication intermediate .rds/.dta/.parquet.
2. Formats & metadata standards — open/non-proprietary formats where possible (.csv/.parquet over .dta; codebooks; DDI / Dublin Core / domain schema). Name the standard, don't say "appropriate metadata".
3. Storage & backup — during the project: encrypted institutional storage, 3-2-1 backup, version control for code (not raw restricted data in git).
4. Access & sharing — who can access, when, under what terms. For restricted data this is the restricted-data access procedure (see Phase 2).
5. Preservation & archiving — a named repository with a persistent identifier (see Phase 3).
6. Roles & responsibilities — PI as data steward, data manager, institutional support, succession plan.

For any required field the input does not supply, write [CLARIFY: <specific question>] rather than fabricating — same convention as /preregister.

Phase 2 — Fold in disclosure-avoidance + IRB constraints (only if sensitive = true)

Pull the relevant rules from .claude/rules/confidential-data.md and weave them into the access & sharing and preservation sections:

• Restricted data → describe the access path, not the data. State the data provider, the DUA/restricted-use agreement, and how a replicator obtains access (e.g., FSRDC application, openICPSR restricted-access tier, provider application). The data itself is not deposited; the path to it is.
• Human-subjects → IRB + minimization. Reference the IRB protocol number (or [CLARIFY:]), the consent terms governing sharing, and the de-identification plan. Shared outputs are de-identified per the consent.
• Disclosure avoidance for any released microdata or tables. Name the technique: suppression of small cells (n < threshold), rounding, top-coding, noise infusion, or aggregation. For tabular output, state the minimum cell-count rule. Defer the actual pre-release scan to /disclosure-check, and say so in the plan ("released outputs pass /disclosure-check before deposit").

Phase 3 — Fold in the computational-environment + replication-package plan

The DMP should commit to reproducibility, not just data deposit:

• Environment capture. State that the computational environment will be captured (R sessionInfo() / renv.lock, Stata version + .do ado dependencies, Python requirements.txt / container). Point to /capture-environment as the mechanism. AEA Data Editor / DCAS standards expect this.
• Replication package. Commit to depositing a replication package (code + non-restricted data + a master run script + README) in a trusted repository. Point to /replication-package as the builder.
• Repository choice — match the data class:
  • Economics / social science → openICPSR (AEA's home; DCAS-compliant) or Harvard Dataverse.
  • Restricted data → openICPSR restricted-access tier or the provider's enclave (FSRDC); deposit code + metadata, not the microdata.
  • Domain repos → field-specific (e.g., ICPSR proper, GenBank, Zenodo for code) where the funder or community expects them.
• State the persistent identifier (DOI) and the timeline (e.g., "at publication" or "within 12 months of project end" — NIH expects no later than publication or award end).

Phase 4 — Post-flight (skip with --no-verify)

If the draft cites a funder policy or standard by name/number (e.g., "per NIH NOT-OD-21-013", "DCAS v1"), invoke /verify-claims via Task to confirm the policy citation resolves. Forked claim-verifier never sees the draft. Surface any FAIL/PARTIAL.

Phase 5 — Output

Write the draft to quality_reports/dmp/YYYY-MM-DD_<funder>_<slug>.md and a funder checklist alongside it.

✓ DMP draft saved: quality_reports/dmp/<file>.md
  Funder: <nsf|nih|erc|horizon>   Data class: <public|restricted|human-subjects>
  Sections: <count> total — <complete> complete, <clarify> with [CLARIFY:] placeholders
  Disclosure/IRB folded in: <yes (Phase 2) | n/a — public data>
  Repository: <openICPSR | Dataverse | domain repo>   PID: <DOI planned | [CLARIFY:]>
  Policy citations verified: <PASS>/<PARTIAL>/<FAIL>  (or "none to verify")
  Next: resolve [CLARIFY:] items, then paste into <DMPTool | NIH ASSIST | Horizon portal>

The funder checklist is a table: each required section → present? → complete / [CLARIFY:], so the user sees at a glance whether the plan will pass the funder's compliance check.

Exit behavior

• All required sections present, zero [CLARIFY:] → "DMP READY", checklist all green.
• Any required section unresolved → "INCOMPLETE — N MUST items unresolved", listed in the checklist. The draft is still written (so the user can fill it in), but not marked ready.
• This skill does not block anything — it produces a document. The gate is the funder's, not ours.

Cross-references

• .claude/rules/confidential-data.md — restricted-data / IRB / disclosure-avoidance rules folded in at Phase 2.
• .claude/skills/disclosure-check/SKILL.md — pre-release disclosure scan the plan commits released outputs to.
• .claude/skills/capture-environment/SKILL.md — the environment-capture mechanism Phase 3 references.
• .claude/skills/replication-package/SKILL.md — the replication-package builder Phase 3 commits to.
• .claude/skills/grant-proposal/SKILL.md — calls this skill for the proposal's data-management section.
• .claude/skills/preregister/SKILL.md — sibling document-generator; shares the MUST/[CLARIFY:] + post-flight conventions.
• .claude/rules/replication-protocol.md — the reproducibility contract the deposited package must satisfy.

What this skill does NOT do

• Submit the plan. It writes a Markdown draft; the user pastes it into DMPTool / NIH ASSIST / the Horizon portal.
• Run the disclosure scan or build the package. It commits the project to /disclosure-check, /capture-environment, and /replication-package, and references them — it does not execute them.
• Write the IRB protocol. It references the protocol number and consent terms; the protocol is authored separately.
• Choose a repository for you when the funder mandates one. If NIH names a domain repository for your data type, that mandate wins over the defaults in Phase 3 — the skill flags it as [CLARIFY:] rather than guessing.