Nature Skills is a collection of reusable skills that help AI agents handle academic writing and scientific visualization. Researchers and AI-assisted scholars use it to turn research tasks into repeatable workflows and usable outputs. The catalogue entries are skills from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Yuan1z0825/nature-skills --skill nature-paper2wordgit clone --depth 1 https://github.com/Yuan1z0825/nature-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper2word)<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper2word"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-paper2word/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-paper2word"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-paper2word.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00176 | $0.02689 |
| Opus 5.5 | $0.00070 | $0.01076 |
| Sonnet 5.5 | $0.00035 | $0.00538 |
| Haiku 4.5 | $0.00018 | $0.00269 |
Grade A, and why
nature-paper2word scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Markdown → compliant Word
This file is the router. The engine is deterministic: the same manuscript and the same format
requirements always produce a byte-identical .docx. Do not substitute a hand-written
python-docx script; run the installed pipeline.
Before the first export in a session, confirm the engine is available: md-to-word --version
must print a version. This skill ships documentation only; if the command is missing, install it
with pip install md-to-word-docx, then run the check again.
The full vocabulary lives in the manual inside the package, not here. md-to-word --contract
prints its absolute path (Chinese or English, following the manuscript). Everything below is order
of work; every spelling is looked up in the manual.
1. Decide the scenario first
| Scenario | What the user says | What to do |
|---|---|---|
| Journal or school compliance | "format it for Nature / my university" | Get a target .docx template or a written spec, then build a requirements file. |
| Existing requirements | "use our lab's spec file" | Pass it with -r. |
| Baseline export (no explicit spec) | "turn this into a Word file" | Normalize the manuscript to the contract, then export with the built-in preset. The preset is a complete, valid format, not a provisional default. |
State the chosen preset or requirements path in one line, then proceed. Without -r the only file
picked up is one named exactly format.txt beside the manuscript; no other .txt is guessed at
(if one is there, the run names it in a reminder). With no format.txt, the preset follows the
manuscript's language: zh-academic for Chinese, en-nature for everything else. The run reports
which one it used.
No path accepts arbitrary Markdown and returns a finished document: a preset supplies formatting, not missing structure. When the manuscript violates the contract, pre-flight stops with a line number rather than emitting a document that looks acceptable while its formatting was never mapped.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 172 lines · 176 tokens per session scan A d9371f771c6f
nature-paper2word is a skill published in the GitHub repository Yuan1z0825/nature-skills (51,003 stars, last pushed yesterday), licensed Apache-2.0. It adds 176 tokens to every session and 2,689 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-28.
Other skills, from other repositories
ieee-latex
Compile, diagnose, and fix IEEEtran LaTeX manuscripts for JSAC, TWC, TCOM, WCL, CL, and related IEEE journals: documentclass options, BibTeX/IEEEtran bibliography, undefined citations/refs, overfull hboxes, figure/table placement, algorithm/equation numbering, Index Terms, EDICS, biographies, and final PDF checks. Use…
pydicom
Reads, inspects, writes, transforms, and preflights local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
pptx-posters
Creates and audits editable scientific posters in macro-free PowerPoint (.pptx) from author-approved local content and assets. Used when the requested deliverable is a PowerPoint research/conference poster and exact physical, printer, accessibility, provenance, and package-security checks are required.
extracting-lab-tables
Detects and extracts tabular laboratory panels from PDFs, scans, and images into structured rows ready for OpenMed and FHIR. Use when the user has a CBC, CMP, lipid panel, or other lab report as a scanned image / PDF / spreadsheet and needs the test name, value, unit, reference range, and abnormal flag as clean rows.…
figures
Makes publication-quality plots from data with matplotlib, learning curves, scaling laws, benchmark and ablation comparisons, Pareto trade-offs, heatmaps and confusion matrices, sized for the page, vector, with uncertainty shown. Use whenever results are plotted, charted or visualized for a paper, report or answer, or…
paper-compile
A build workflow that turns LaTeX source files into a PDF and checks whether the paper compiles correctly. LaTeX is a text-based system commonly used for academic papers.