/scientific-writing

name: scientific-writing
description: Drafts, revises, and audits 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: "2.3"
  last-reviewed: "2026-10-01"
  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Do not send unpublished manuscripts, peer-review or editorial material, sensitive or restricted data, PHI or other personal data, proprietary content, or source documents to an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.
• For binary trial outcomes, report group event counts/denominators plus both absolute and relative effects with uncertainty when the reporting guideline requires them. Distinguish risk difference (percentage points), relative risk, and odds ratio; do not rewrite one as another or infer an absolute effect without the baseline risk. See [CONSORT 2025 explanation](https://www.bmj.com/content/389/bmj-2024-081124).
• 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.

Workflow

1. Establish the local workspace

For a new draft, optionally generate fail-closed Markdown, JSON, and CSV scaffolds:

python3 scripts/scaffold_manuscript.py \
  --output-dir ./draft-workspace \
  --document-id local-draft \
  --study-design randomized_trial \
  --guideline consort-2025

The generator never overwrites files. Its output is explicitly not submission-ready and contains placeholders that the linter rejects.

2. Select reporting guidance

Choose by actual design and article type, then open the current official statement, checklist, explanation document, extensions, and target-journal instructions.

python3 scripts/select_reporting_guidelines.py select \
  --study-design randomized_trial

Current major routes rechecked on 2026-10-01 include CONSORT 2025, SPIRIT 2025, PRISMA 2020, STROBE, STARD and STARD-AI, TRIPOD+AI, CARE, ARRIVE 2.0, SQUIRE 2.0, and CHEERS 2022 [SW-S06–SW-S18].

The selector is non-scoring. It does not certify quality, compliance, completeness, or acceptance. See `references/reporting_guidelines.md`.

3. Build the evidence record

Assign:

• `E` IDs to sources in `source_manifest.json`;
• `C` IDs to claims in `claims.csv`;
• `N`, `M`, `O`, and `R` IDs to numeric facts, methods, outcomes, and results in

`consistency_manifest.json`.

Store a hash of claim text in CSV rather than raw claim text. Use one claim per physical Markdown line; the hash binds the entire line after removing claim/evidence markers and collapsing whitespace. During drafting, append:

`