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Writing51k · #1974

scientific-writing

Has your agent write and revise scientific manuscripts accurately. It's useful for ensuring papers and reports are scientifically correct and traceable.

What it does

This tool helps draft and revise scientific texts like papers and reports. It ensures that the information used is backed by evidence and that everything is organized according to guidelines.

When to use it

  • ✓When writing sections of a scientific paper.
  • ✓When organizing references and citations.
  • ✓When preparing a manuscript for journal submission.

Example of what to ask your AI

“I need to write the methods section of my paper about the clinical trial, using the CONSORT 2025 guidelines.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/scientific-writing/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/scientific-writing/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

You provide the information, and the tool helps structure the text, check if sources are correct, and ensure adherence to scientific standards. It does not invent data and highlights what needs human verification.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add K-Dense-AI/scientific-agent-skills --skill scientific-writing

Or download just the file:

curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/scientific-writing/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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Do not send unpublished manuscripts, peer-review or editorial material, sensitive or restricted data, PHI or other personal data, proprietary content, or source documents to an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.
  • 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 \

…
FAQ

Can the AI write the entire paper for me?

No, the AI is an assistant. It helps draft and organize, but scientific decisions and final approval always rest with human authors.

Can I send confidential data to the AI?

No, you should never send unpublished information, sensitive, or confidential data without authorization and without reviewing security and privacy policies.

What does 'evidence provenance' mean?

It means every piece of information or number in the text must be directly linked to a source verified by a human, who confirmed the information and its details.

Details

Original description by the author

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.

repo: K-Dense-AI/scientific-agent-skills

Popularity

GitHub stars
51k
Ranking position
#1974
License
MIT
Updated
10/15/2026

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