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-polishinggit 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-polishing)<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-polishing"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-polishing/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-polishing"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-polishing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 105 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00192 | $0.01582 |
| Opus 5 | $0.00096 | $0.00791 |
| Sonnet 5 | $0.00038 | $0.00316 |
| Haiku 4.5 | $0.00019 | $0.00158 |
Grade A, and why
nature-polishing 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 6d ago.
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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nature-Style Academic Polishing — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (core principles, paper-type playbooks, per-section guidance, language-specific rules, per-journal style). - A dynamic layer (this file plus
manifest.yaml) that detects the request's axes and loads only the fragments needed for the current job.
Do not try to apply the polishing logic from memory or from this router. Always load fragments from disk as described below.
Routing protocol
Follow these five steps every time the skill is invoked.
1. Load the manifest and the core layer
Read manifest.yaml. It declares the axes (paper_type, section, language, journal), the allowed values, and the file paths each value maps to.
Also read every file listed under always_load. These hold the default stance, failure-mode diagnosis, ethics, and output format that apply to every polish job.
2. Detect the axis values for this request
For each axis in the manifest, decide the value using the manifest's detect: hint and the user's input:
paper_type— research / methods / hypothesis / algorithmic / review. Default: research.section— abstract / intro / results / discussion / conclusion / title / methods. May be multiple. Ask the user if it is ambiguous and matters for the polish.language— en or zh-to-en. Detect from the draft itself.journal— nature / nat-comms / nat-mach-intell / generic. Default: generic. Usenatureonly for flagship Nature,nat-commsfor Nature Communications andnat-mach-intellfor Nature Machine Intelligence (NMI). Do not route another Nature Portfolio title through flagship Nature rules.
State the detected axis values in one short line to the user before proceeding, so they can correct you cheaply. This is a progress update, not an approval gate; continue unless a necessary decision remains unresolved.
3. Load the matching fragments
What ships with it
32 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.
- agents/openai.yaml 263 B
- manifest.yaml 6.5 KB
- README_EN.md 4.2 KB
- README.md 3.6 KB
- references/latex-layout.md 10 KB
- references/nat-comms-2025-diction.md 3.9 KB
- references/phrasebank-playbook.md 4.1 KB
- references/published-article-patterns.md 4.6 KB
- references/section-moves.md 5.9 KB
- references/style-guardrails.md 2.6 KB
- references/writing-strategy.md 4.7 KB
- static/core/failure-modes.md 1.3 KB
- static/core/output-format.md 840 B
- static/core/stance.md 1.6 KB
- static/fragments/journal/generic.md 1022 B
- static/fragments/journal/nat-comms.md 2.4 KB
- static/fragments/journal/nat-mach-intell.md 4.6 KB
- static/fragments/journal/nature.md 1.2 KB
- static/fragments/language/en.md 1.2 KB
- static/fragments/language/zh-to-en.md 1.2 KB
- static/fragments/paper_type/algorithmic.md 953 B
- static/fragments/paper_type/hypothesis.md 765 B
- static/fragments/paper_type/methods.md 806 B
- static/fragments/paper_type/research.md 785 B
- static/fragments/paper_type/review.md 994 B
- static/fragments/section/abstract.md 1.1 KB
- static/fragments/section/conclusion.md 512 B
- static/fragments/section/discussion.md 1.7 KB
- static/fragments/section/intro.md 1.3 KB
- static/fragments/section/methods.md 815 B
- static/fragments/section/results.md 2.7 KB
- static/fragments/section/title.md 645 B
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.
- 6d ago Changed c8ff9503a8d3
- 13d ago First seen · 112 lines · 192 tokens per session scan A f4c08a7b27b1
nature-polishing is a skill published in the GitHub repository Yuan1z0825/nature-skills (40,913 stars, last pushed 2d ago), licensed Apache-2.0. It adds 192 tokens to every session and 1,582 once invoked, about $0.0010 per session on Opus 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 2026-08-30.
Other skills, from other repositories
literature-searcher
Search CrossRef, OpenAlex, PubMed, Semantic Scholar, and optional Scopus; deduplicate results, download open-access PDFs by DOI, classify papers, monitor new results, and analyze coverage. Use when asked to search literature, monitor a topic, download an open-access paper, classify papers, or analyze literature gaps.
academic-research
Search academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence gathering.
academic-trend-research
Research emerging academic trends by combining scholarly databases, web sources, and news to identify rising research areas, breakthrough papers, and shifting scientific consensus. Ideal for R&D teams tracking frontier science.
using-cesiumjs-skills
Use when starting any conversation involving CesiumJS development - provides orientation on available domain skills and how they activate.
latex-empirical-tables
Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide…
cite-placement
Place pre-screened literature citations into a LaTeX or Word manuscript, or restyle the citations already in one. Three modes: (1) inline placement — inline \cite{}/\citet{}/\citep{} with a compiled references.bib, for author-date journals (APA, MLA, Harvard, Chicago author-date, IEEE, Vancouver); (2) footnote…