scientific-writing

これはレビュー時に保存された抜粋です。完全で最新の内容は外部ソースを確認してください。

---
name: scientific-writing
description: Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.
license: MIT
compatibility: Requires Python 3.11+ only for optional dependency-free local CLIs; core guidance is platform-neutral. Bundled tools are offline and require no API keys.
metadata:
  version: "2.0"
  skill-author: K-Dense Inc.
---

# Scientific Writing

## Purpose

Produce clear scientific prose without inventing evidence or concealing uncertainty.
Keep drafting, evidence verification, and submission approval as separate stages.

The accountable human authors control scientific decisions and final approval. AI is
not an author, and generated fluency is never evidence [SW-S01, SW-S03].

## Non-negotiable safety rules

### Confidentiality

Do not send unpublished manuscripts, peer-review or editorial material, sensitive or
restricted data, PHI or other personal data, proprietary content, or source documents
to an external service without:

1. explicit authorization from a person or body empowered to grant it; and
2. a documented review of journal, institutional, funder, consent, ethics, contractual,
   legal, and data-use policy.

When authorization or policy is unclear, keep processing local and use only the minimum
metadata needed. De-identification requires expert review; removing obvious names is
not sufficient. See `references/authorship_ai_confidentiality.md`.

### No fabrication

Never invent or complete:

- citations, references, DOI, PMID, PMCID, ISBN, URLs, or quotations;
- results, data values, denominators, sample sizes, units, effect estimates,
  uncertainty, statistical tests, or significance claims;
- methods, materials, protocol details, software versions, analysis choices, or
  deviations;
- registrations, approvals, consent, ethics statements, participant details, or dates;
- authors, author order, CRediT roles, acknowledgments, or permissions;
- funding, sponsor roles, conflicts, data or code availability, or AI disclosures.

Use an explicit missing, unverified, or not-applicable state. Do not substitute plausible
boilerplate.

### Evidence binding

Every factual or numeric manuscript claim must map to verified evidence IDs. A human
verifier must open the source, confirm the proposition and locator, verify bibliographic
metadata, and record who verified it and when.

Search snippets, generated summaries, memory, and another work's bibliography may aid
discovery but do not verify a claim. See `references/evidence_workflow.md`.

### Scientific fidelity

- Preserve uncertainty and alternative explanations.
- Distinguish confirmatory, exploratory, descriptive, and post hoc work.
- Keep methods and results consistent.
- Reconcile units, denominators, sample sizes, populations, time points, and labels.
- Report negative, null, adverse, unexpected, failed, and inconclusive findings when
  they belong to the study record.
- State concrete limitations and bound generalizability.
- Do not convert association into causation or non-significance into equivalence.

## Intake

Before drafting, obtain or mark unresolved:

- document type, study design, stage, audience, and target venue;
- current author instructions and policy access date;
- protocol, registration, analysis plan, amendments, and reporting guideline;
- manuscript or section scope;
- verified source manifest and claim registry;
- methods, results, tables, figures, and supplements;
- authorship, CRediT, declarations, and approval records;
- confidentiality classification and authorized processing boundary;
- data, code, materials, and repository constraints.

Do not ask for restricted source material if metadata or a local user-run audit is
sufficient.

## Workf