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-reviewergit 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-reviewer)<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-reviewer"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-reviewer/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-reviewer"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00135 | $0.02393 |
| Opus 5 | $0.00068 | $0.01196 |
| Sonnet 5 | $0.00027 | $0.00479 |
| Haiku 4.5 | $0.00014 | $0.00239 |
Grade A, and why
nature-reviewer 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 13d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nature Reviewer Assessment Skill
Use this skill to simulate a Nature-style reviewer assessment package from the referee
side.
This skill is for reviewer-style manuscript evaluation, not for drafting the authors'
response. If the user wants rebuttal writing, route to nature-response.
Default stance
- Ground the review only in the local source basis plus manuscript facts supplied by the user.
- Evaluate the manuscript against source-grounded axes:
originality,scientific importance,interdisciplinary readership,technical soundness, andreadability for nonspecialists. - Use the 12-axis technical concern taxonomy only as an internal coverage checklist; it supplements but never replaces the five source-grounded axes.
- Return exactly
3 mutually blind reviewer reports + 1 post-review synthesisunless the user explicitly asks for another structure. - Give every reviewer only the same immutable manuscript/source packet, the same journal criteria, and that reviewer's preassigned emphasis. Never provide another review, a shared concern ledger, a draft synthesis, or hints about what another reviewer noticed.
- Run each reviewer in a genuinely separate context, subagent, process, or invocation. If the environment cannot isolate contexts, generate one reviewer report per invocation or explicitly state that mutual blindness cannot be guaranteed; never present shared-context drafting as independent peer review.
- Define emphasis briefs before any report is generated. They are working lenses, not reviewer identities, specialties, institutions, or biographies.
- Freeze each individual report before comparing them. Natural duplication or disagreement is valid evidence of independent review and must not be edited away to manufacture diversity.
- Identify who would be interested in the results and why.
- Identify technical failings that must be addressed before the authors' case is established.
- Give every substantive concern a stable ID, a faithful
claim_pointer, and a verifiableevidence_pointer; mark missing locations instead of inventing them. - Separate user-visible concerns into
Major ConcernsandMinor Comments. Mark a Major ConcernBlocking Yesonly when the current manuscript cannot establish its central case until that concern is resolved; Minor Comments are never blocking. - Do not impose a concern quota. If no grounded concern exists at a level, state that explicitly instead of inventing one.
- Keep the critique intellectually sharp but professionally phrased; severity comes from impact on the manuscript's case, not from hostile wording.
- Avoid em dashes, en dashes, and colons as routine prose punctuation throughout reviewer reports and synthesis. Prefer a new sentence, comma, semicolon, parentheses, or a short heading followed by a new line. Retain ordinary hyphens in standard compound terms and stable IDs such as
R1-M1. Preserve punctuation in source-faithful titles, quotations, formulas, identifiers, URLs, times, and required machine-readable syntax when changing it would be inaccurate. - Distinguish clearly between what is supported, what is weak, and what is not assessable from the provided material.
- When the manuscript has a clear technical domain, use claim-dependent domain gates as supporting checks, but keep the output inside the same 3-reviewer
nature-reviewerstructure. - Do not claim the editor's final decision or certainty about fit to
Nature.
What ships with it
18 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 253 B
- manifest.yaml 2.5 KB
- README_EN.md 3.5 KB
- README.md 3.0 KB
- references/domain-specific-review-gates.md 10 KB
- references/editorial criteria and processes.md 4.3 KB
- references/qa-checklist.md 6.4 KB
- references/report-structure.md 5.6 KB
- references/review-axes.md 3.4 KB
- references/reviewer-workflow.md 6.3 KB
- references/role-boundaries.md 3.3 KB
- references/source-basis.md 5.3 KB
- references/technical-concern-taxonomy.md 6.3 KB
- tests/punctuation-style.md 1.8 KB
- tests/reviewer-independence.md 2.7 KB
- tests/severity-tiering.md 2.7 KB
- tests/test_reviewer_instruction_contracts.py 1.2 KB runs code
- tests/traceable-review.md 1.7 KB
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.
- 13d ago First seen · 174 lines · 135 tokens per session scan A 88aab215096a
nature-reviewer is a skill published in the GitHub repository Yuan1z0825/nature-skills (40,913 stars, last pushed 2d ago), licensed Apache-2.0. It adds 135 tokens to every session and 2,393 once invoked, about $0.0007 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…