By Yuan1z0825
Generate Nature-style scientific manuscripts, figures, response letters, patent drafts, and presentations from research materials. Conduct multi-source literature searches, citation management, paper reading with Chinese-English side-by-side rendering, and simulated peer review.
Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv, Scopus, ScienceDirect). Use when the user needs coordinated multi-step literature workflows beyond a single MCP call. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.
Add strict Nature/CNS citations to manuscript text by splitting long passages into citable segments, searching only accepted flagship and subjournal titles from Nature Portfolio, the AAAS Science family, and Cell Press, filtering by publication time range, and exporting one reference-manager-ready output by default. Use this skill whenever the user asks to input text and automatically get references, add citations to a paragraph/manuscript, find Nature-series or CNS support for statements, create text-to-reference correspondence, "分段引用", "自动给出引用", "Nature系列引用", "CNS及子刊", "支撑文献", "补引用", "找引用", or export EndNote/RIS/ENW/Zotero RDF. Also trigger on general academic-writing citation needs even without the word "Nature", such as adding references while writing a paper, finding sources/literature for a claim, building a reference list, citation/referencing for academic writing, and Chinese phrasings like 学术写作引用、写论文加引用、写paper找文献、加参考文献、配文献、引用文献、文献支撑.
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.
Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, figures4papers-style matplotlib plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 论文配图、学术写作配图、科研绘图、科研作图、画图、作图、出图、论文图表、可视化.
Convert scientific papers, theses, technical reports, source code, figures, or research manuscripts into evidence-grounded Chinese invention patent drafts. Use when an AI agent must extract patentable technical contributions, map every claimed feature to source evidence, preserve core formulas as editable Office Math, generate claim-aligned flowcharts and methodology figures, compare a paper with an existing patent, audit support and consistency, or deliver separate Chinese DOCX files for claims, specification, abstract, and abstract figure.
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大家好,我是nature skills的创立者袁一哲,从github以及其他途径联系我的顶尖AI人才数不胜数,所以我成立了TOP AI CREW!今天起人才联盟正式招募! 这里汇聚经过严格筛选的行业强者,多元思维碰撞,前沿技术共生。 拒绝单打独斗,告别低效内耗,和一群同频的顶尖伙伴并肩前行,深耕AI领域,突破技术边界,一同站上行业前沿。 敢想、敢闯、敢创造,下一个AI传奇,由我们共同书写!
感谢大家持续关注 nature-skill。如果你有任何需求,欢迎提交 issue;如果我们认为该需求有意义且可行,也会尽量推进实现。我们同样欢迎 PR,但请务必按照 README 后面说明的格式提交,以便我们更高效地审核与合并。
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6.17投资交流会 |
个人微信 |
Hello everyone, I’m Yizhe Yuan, founder of Nature Skills. After being contacted by countless top AI talents through GitHub and other channels, I decided to launch TOP AI CREW. Starting today, our talent alliance is officially open for recruitment. This is a community of carefully selected industry leaders: a place where diverse perspectives collide, frontier technologies evolve, and ambitious builders grow together. No more working in isolation. No more wasting energy on inefficient solo battles. Here, you’ll move forward alongside a group of world-class, like-minded peers—deepening your expertise in AI, pushing technical boundaries, and advancing to the forefront of the industry together. Think boldly. Move fearlessly. Create relentlessly. The next AI legend will be written by us—together.
Thank you for your continued interest in nature-skill. If you have any feature requests or suggestions, please feel free to submit an Issue. If we find the proposal meaningful and feasible, we will do our best to implement it. We also welcome Pull Requests (PRs). However, please follow the contribution format described later in this README to help us review and merge submissions more efficiently.
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nature-skills is a repository of reusable instruction bundles centred on SKILL.md.
Each skills/nature-* directory is one installable unit. Copy the whole folder, not
only SKILL.md, because many skills depend on references/, static/, assets,
scripts, or README context. The skills/_shared/ directory is shared support
content used by several skills and should stay next to the nature-* folders
when you install skills manually.
Codex plugin marketplace installation
This repository includes Codex plugin packaging at plugins/nature-skills/, so
Codex users can install the complete Nature Skills bundle from the plugin
marketplace instead of copying each skill folder manually.
CLI installation:
codex plugin marketplace add https://github.com/Yuan1z0825/nature-skills --ref main
codex plugin add nature-skills@nature-skills
Codex Desktop users can add the same repository as a custom plugin marketplace:
https://github.com/Yuan1z0825/nature-skills.gitmainnature-skillsAfter installation, all nature-* skills are available through the plugin as a
complete bundle, together with the shared support directory used by the newer
router-style skills. If the skills do not appear immediately, refresh the plugin
page or start a new Codex session.
Manual local-skill installation
Codex can also use these folders directly as local skills.
Clone the repo
git clone https://github.com/Yuan1z0825/nature-skills.git
cd nature-skills
Install one skill
mkdir -p ~/.codex/skills
cp -R skills/_shared ~/.codex/skills/
cp -R skills/nature-reader ~/.codex/skills/
npx claudepluginhub yuan1z0825/nature-skills --plugin nature-skillsScientific writing, citations, grants, posters, and academic career (13 skills)
Production-grade academic research pipeline for Claude Code: research → write → review → revise → finalize. 4 skills, 27 modes, 39-agent ensemble, v3.7.3 + v3.8 L3 claim-faithfulness gate, v3.9.0 cross-index triangulation, v3.10 triangulation policy layer, v3.11 deterministic citation verification gate (#182).
学术论文写作 — 12 agent 协作:结构设计、段落写作、引用合规、双语摘要、格式排版
Semi-automated research assistant for academic research and software development, with skills for literature review, experiments, analysis, writing, and project knowledge management
Academic research agents — hypothesis generation, experiment design, paper drafting, peer review simulation, and more.
Academic paper writing skills for ML conferences (NeurIPS, ICML, ICLR, AAAI)