nature-skills

大家好，我是上海交通大学博士生袁一哲，目前主要从事医疗 AI 相关的研究与创业实践。欢迎大家持续关注 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。

Hello everyone, I’m Yuan Yizhe, a PhD student at Shanghai Jiao Tong University. I’m currently working on research and entrepreneurial projects in medical AI. Thank you for your continued interest in nature-skill. If you have any requests, feel free to open an issue. If we find the request meaningful and feasible, we’ll do our best to implement it. We also welcome PRs, but please make sure to follow the submission format described later in the README so that we can review and merge them more efficiently.

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这里有充足的计算资源，以及深耕医疗大模型（LLM）、视觉预训练、Prompt Engineering 及自动化医疗 AI Agent 的科研团队。我们更看重你的自驱力、学习能力与科研产出追求。

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Installation

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.

1. Codex

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:

Marketplace source: https://github.com/Yuan1z0825/nature-skills.git
Branch/ref: main
Plugin: nature-skills

After 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/

Copying _shared is harmless even for skills that do not use it, and it avoids broken relative references for skills such as nature-reader, nature-writing, nature-polishing, and nature-paper2ppt.

Install all current skills

mkdir -p ~/.codex/skills
cp -R skills/_shared ~/.codex/skills/
for d in skills/nature-*; do
  cp -R "$d" ~/.codex/skills/
done

Update after pulling new changes

git pull
cp -R skills/_shared ~/.codex/skills/
for d in skills/nature-*; do
  cp -R "$d" ~/.codex/skills/
done

Finish

Restart Codex so newly added skills are picked up.
Then ask naturally, for example: Translate this paper into a full markdown reader. or Make this paper into a Chinese journal-club PPT.

If you prefer not to use the terminal, copying the skills/nature-* folder(s) into ~/.codex/skills/ manually works as well; also copy skills/_shared/ once. For a longer walkthrough, see install.md.

2. Claude Code

Claude Code does not consume Codex skill folders directly. The recommended Claude Code setup is a thin subagent or slash-command wrapper that points to a stable clone of this repository, so supporting files such as references/, static/, assets, scripts, and skills/_shared/ remain available.

mkdir -p ~/ai-skills
cd ~/ai-skills
git clone https://github.com/Yuan1z0825/nature-skills.git

Create a user-level subagent wrapper:

mkdir -p ~/.claude/agents
cat > ~/.claude/agents/nature-reader.md <<'EOF'
---
name: nature-reader
description: Full-paper bilingual, figure-aware, source-grounded Markdown reader for journal or conference papers. Use proactively when the user asks to translate an entire paper or generate a complete markdown reader.
---

When invoked, first read `~/ai-skills/nature-skills/skills/nature-reader/SKILL.md`.
Treat that file as the governing workflow.
If the skill references supporting files, read only the specific files you need from
`~/ai-skills/nature-skills/skills/nature-reader/` and
`~/ai-skills/nature-skills/skills/_shared/`.
Do not replace the skill with a generic paper-summary response.
EOF

After that, start a new Claude Code session or open /agents, and invoke it naturally or explicitly:

Use the nature-reader subagent to turn this PDF into a full markdown reader.

If you prefer commands instead of subagents, create a project or user command under .claude/commands/ or ~/.claude/commands/ that tells Claude Code to read the real SKILL.md from the cloned repository.

Official Claude Code docs:

3. Other agents or manual use

If your agent supports reusable prompt files, system prompts, or agent profiles, the minimum portable unit is the skill directory itself:

skills/
├── _shared/              # keep this when a skill references ../_shared
└── nature-<topic>/
    ├── README.md
    ├── SKILL.md
    ├── manifest.yaml     # present for router-style skills
    ├── static/           # present for router-style skills
    └── references/...

In that case:

Copy the whole skill directory into your prompt library or project.
Preserve SKILL.md, manifest.yaml, static/, references/, scripts, assets, and any needed skills/_shared/ files together.
Adapt the frontmatter and body to the target agent's native format if needed.

Star History

Skill index

Skill	Status	Purpose	Trigger keywords
`nature-figure`	Stable	Nature/high-impact Python or R figure workflow with bundled figures4papers demos	"Nature figure", "publication plot", "scientific figure", "figures4papers"
`nature-polishing`	Stable	Academic prose polishing to Nature style	"Nature style", "polish", "academic writing"
`nature-writing`	Draft	Nature-style manuscript section drafting and argument restructuring	"Nature writing", "write abstract", "write introduction", "manuscript draft"
`nature-reviewer`	Draft	Nature-style reviewer assessment with 3 referee reports and a cross-review synthesis	"Nature reviewer", "pre-submission review", "reviewer report", "peer-review critique", "审稿人视角评估"
`nature-citation`	Beta	Strict Nature / CNS-family citation retrieval with ENW, RIS, and Zotero RDF export	"Nature citation", "CNS citation", "text citation", "supporting references", "Zotero RDF"
`nature-data`	Draft	Nature Data Availability statements, repository plans, and FAIR checks	"Data Availability", "repository", "FAIR metadata", "data availability statement"
`nature-reader`	Beta	Full-paper bilingual Markdown reader with source anchors and figure grounding	"nature reader", "full markdown", "paper md", "原文对照", "图文对应", "全文翻译"
`nature-response`	Beta	Point-by-point reviewer response letters with comment triage, action mapping, and risk checks	"response to reviewers", "rebuttal letter", "major revision", "审稿意见回复"
`nature-paper2ppt`	Beta	Chinese PPTX decks from scientific papers	"paper PPT", "journal club", "paper to slides", "paper presentation"
`nature-academic-search`	Beta	Multi-source literature search, citation verification, and reference management	"search papers", "find articles", "academic search", "literature search", "verify DOI"

Adding a new skill? Follow the contribution guide at the bottom of this file.

nature-figure

What it does — Generates multi-panel matplotlib figures that match Nature journal visual standards: correct typography, semantic colour palette, editable SVG output, and non-redundant panel information architecture.

Example output gallery — Five dense, simulated Nature-style result figures are included in the nature-figure gallery: material/mechanism, spatial imaging, in vivo efficacy, single-cell systems and perturbation validation.

Chart-type atlas — The nature-figure chart atlas classifies 10 supported chart families, including bar, line, heatmap, scatter/bubble, radar/polar, distribution, forest/interval, area/stacked, image-plate and network/matrix layouts.

Built from — Production scripts from papers published in Nature Machine Intelligence and top ML/bioinformatics venues (figures4papers). The figures4papers demo scripts and preview assets are bundled inside skills/nature-figure/assets/figures4papers/, with a routing guide at skills/nature-figure/references/demos.md.

Key rules enforced

Three mandatory rcParams must always appear first:

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'DejaVu Sans', 'Liberation Sans']
plt.rcParams['svg.fonttype'] = 'none'   # text stays as <text> nodes, not paths

Primary output is always .svg; .png at 300 dpi is a secondary raster preview.
Multi-panel figures follow a three-level information hierarchy: overview → deviation → relationship. No two panels may answer the same scientific question.

Reference files

skills/nature-figure/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
└── references/
    ├── api.md            PALETTE, helper signatures, validation rules
    ├── design-theory.md  Typography, layout, export policy, anti-redundancy rules
    ├── common-patterns.md Ultra-wide panels, legend axes, print-safe bars
    ├── tutorials.md      End-to-end walkthroughs (bars, trends, heatmaps)
    ├── chart-types.md    Radar, 3D sphere, scatter, fill_between, log-scale
    └── demos.md          Bundled figures4papers scripts and preview routing

Supported chart types — Stacked bar, grouped bar, horizontal ablation bar, trend/line, sequential heatmap, diverging z-score heatmap, bubble scatter, radar/polar, 3D sphere illustration, fill-between area, log-scale bar, GridSpec multi-panel.

nature-polishing

What it does — Transforms academic draft text (including Chinese → English translation) into prose matching Nature journal conventions: ≤ 30-word sentences, section-aware tense and hedging, precise vocabulary, correct citation practice, and British English.

Built from — A graduate-level scientific English writing course, Academic Phrasebank, and close reading of curated Nature and Nature Communications research articles across materials, energy systems, construction decarbonization and machine learning.

Key rules enforced

Domain	Core rule
Sentence length	Every sentence ≤ 30 words; count individually; last sentence most likely to fail
Hedging calibration	Match claim strength to evidence: demonstrate → suggest → may reflect
Section tense	Results = past tense + quantitative detail; Discussion = hedging + mechanism
Citation integrity	Cite only sources personally read and verified; four attribution types
Overclaim detection	Flag absolutes, unwarranted causation, scope expansion, unverified "first" claims
British English	signalling, colour, analyse, programme, modelling, behaviour

12-step polishing workflow

Sentence split → Section ID → Hourglass check → Tense audit → Sentence edit → Vocabulary upgrade → Template check → Citation audit → House style → Overclaim → Proofreading → Plain-text output

Reference files

skills/nature-polishing/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
└── references/
    ├── latex-layout.md
    ├── published-article-patterns.md
    ├── phrasebank-playbook.md
    ├── section-moves.md
    ├── style-guardrails.md
    └── writing-strategy.md

nature-writing

What it does — Drafts or rebuilds manuscript sections from author-provided claims, results, figures, notes, or Chinese drafts. It is for argument construction: abstracts, introductions, Results narratives, Discussions, Conclusions, titles and full manuscript outlines, method sections, experiment sections and reviewer-facing self-review.

Built from — Close reading of curated Nature and Nature Communications articles, especially how published papers move from field-scale stakes to a narrow gap, then to evidence, interpretation and bounded implication. It also integrates open research-writing notes for paragraph flow, section logic and adversarial paper review.

Key rules enforced

Domain	Core rule
Evidence first	Do not invent data, mechanisms, references, statistics, novelty or limitations
Abstract	Context/problem → gap → approach → key result → implication → boundary
Introduction	Field scale → bottleneck → prior attempts → unresolved gap → present study
Method	Module motivation → module design → forward process → technical advantage
Results	Build an evidence ladder, not a chronological lab diary
Experiments	Tie claims to baselines, ablations, metrics, stress tests and readable tables
Discussion	Explain meaning, relation to prior work, constraints and future use
Review	Run claim-evidence and rejection-risk checks before submission
Chinese notes	Translate intent and argument, not clause order

Reference files

skills/nature-writing/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
├── agents/
│   └── openai.yaml
└── references/
    ├── abstract.md
    ├── article-architecture.md
    ├── chinese-author-workflow.md
    ├── conclusion.md
    ├── experiments.md
    ├── introduction.md
    ├── method.md
    ├── paper-review.md
    ├── paragraph-flow.md
    ├── related-work.md
    └── examples/

nature-reviewer

What it does — Simulates a Nature-style pre-submission reviewer assessment from the referee perspective. It returns three reviewer reports plus a cross-review synthesis, focusing on novelty, significance, technical soundness, presentation, and likely editorial risk.

Key rules enforced

Domain	Core rule
Reviewer role	Assess as external referees, not as an author rebuttal writer
Evidence grounding	Use only the manuscript/material supplied by the user and the local reviewer source basis
Multi-reviewer output	Produce three distinct reviewer reports plus a synthesis
Editorial relevance	Separate novelty, significance, technical confidence, presentation, and decision risk
Boundaries	Do not invent experiments, citations, journal policy, or manuscript content

Reference files

skills/nature-reviewer/
├── README.md
├── SKILL.md
└── references/
    ├── source-basis.md
    ├── reviewer-workflow.md
    ├── review-axes.md
    ├── report-structure.md
    ├── role-boundaries.md
    └── qa-checklist.md

nature-citation

What it does — Converts manuscript text or standalone claims into strict Nature / CNS-family citation candidates, then exports one reference-manager-ready file in ENW, RIS, or Zotero RDF. It can also generate an HTML screening page for year filtering, citation selection, and format-specific download.

Built from — Crossref metadata retrieval, DOI record export, and journal-family filtering logic for Nature Portfolio, the AAAS Science family, and Cell Press.

Key rules enforced

Domain	Core rule
Scope filtering	Restrict to Nature Portfolio, Science family, Cell Press, or flagship-only journals
Segmentation	Split long text into citable claim units with stable segment IDs
Search discipline	Translate Chinese claims into English scientific concepts; prefer precision over volume
Support grading	Distinguish strong, partial, background, limiting, and metadata-only support
Export integrity	Do not fabricate DOI, pages, volume, issue, or journal metadata
Download options	Support one-file export in `ENW`, `RIS`, or Zotero `RDF`

Reference files

skills/nature-citation/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
├── references/
│   ├── journal-scope.md
│   ├── ris-endnote.md
│   ├── script-usage.md
│   └── search-strategy.md
└── scripts/
    └── nature_citation.py

Example workflow — Segment a paragraph, search in-scope citations, review candidates in the HTML browser, then download only the selected records as ENW, RIS, or Zotero RDF.

nature-data

What it does — Prepares and audits Data Availability statements, repository plans, dataset citations, and FAIR metadata checks for Nature-family and Springer Nature submissions. It is bilingual-aware: Chinese author notes such as "data availability statement", "request from corresponding author", "raw data", "restricted data", and "public database" are converted into precise submission-ready English with Chinese action notes.

Built from — Springer Nature research data policy, Nature Portfolio reporting standards, Scientific Data repository and citation practice, the FAIR Guiding Principles, and DataCite metadata conventions.

Key rules enforced

Domain	Core rule
Data Availability	Map every result-supporting dataset to a durable access route
Repository strategy	Prefer mandated or discipline-specific repositories with persistent identifiers
Restricted data	State the restriction reason, controller, review route, and access conditions
Dataset citations	Cite public datasets with DataCite-style creator, title, repository, year, and identifier metadata
FAIR metadata	Check identifiers, licence, README/data dictionary, provenance, version, and reuse conditions
Chinese alignment	Translate intent rather than literal wording; flag vague "reasonable request" phrasing

Reference files

skills/nature-data/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
├── agents/
│   └── openai.yaml
└── references/
    ├── chinese-author-alignment.md
    ├── fair-metadata-checklist.md
    ├── policy-principles.md
    ├── repository-and-identifiers.md
    ├── source-basis.md
    └── statement-patterns.md

nature-response

What it does — Drafts, audits, and revises point-by-point reviewer response letters for Nature-family and high-impact journal manuscript revisions. It treats the response letter as an editor-facing verification document: every reviewer concern is assigned a stable ID, classified, mapped to an action, and tied to manuscript evidence, a revision location, or an unresolved author-input flag.

Built from — Nature editorial process guidance, Nature-family revision-package instructions, Springer Nature rebuttal advice, and transparent peer-review considerations.

Key rules enforced

Domain	Core rule
Completeness	Every reviewer comment receives an ID and a response, cross-reference, or unresolved flag
Action mapping	Each reply maps to a concrete manuscript action such as `ACCEPT_TEXT`, `ACCEPT_ANALYSIS`, `SOFTEN_CLAIM`, or `AUTHOR_INPUT_NEEDED`
Traceability	Claimed changes must cite a section, page, line, figure, table, supplement, citation, or visible placeholder
Factuality	Do not invent experiments, analyses, citations, line numbers, figure panels, editor instructions, or manuscript changes
Tone	Use cooperative, evidence-forward language; disagree only with scientific or scope-based reasoning
Chinese alignment	Convert Chinese author notes into English response prose plus Chinese confirmation items when needed

Reference files

skills/nature-response/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
├── references/
│   ├── action-mapping.md
│   ├── chinese-author-alignment.md
│   ├── comment-taxonomy.md
│   ├── difficult-cases.md
│   ├── intake-and-routing.md
│   ├── qa-checklist.md
│   ├── response-structure.md
│   ├── source-basis.md
│   └── tone-and-stance.md
├── tests/
    ├── conflicting-reviewers.md
    ├── defensive-draft-audit.md
    ├── evaluation-summary.md
    ├── impossible-experiment.md
    ├── major-revision-missing-evidence.md
    ├── minor-revision.md
    └── rubric.md
└── examples/
    ├── conflicting-reviewers.md
    ├── major-revision-with-missing-evidence.md
    └── minor-revision.md

nature-paper2ppt

What it does — Turns a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes into a concise Chinese .pptx presentation for journal club, group meeting, lab meeting, paper sharing, or thesis seminar.

The skill identifies the paper type and central argument, selects only figures and tables that support the evidence chain, writes Chinese slide titles, bullets, captions, takeaways and speaker notes, creates the actual PPTX deck, and runs lightweight package QA.

Key rules enforced

Domain	Core rule
Narrative	Use the paper's scientific argument as the slide spine, not the manuscript section order
Paper type	Classify the paper before choosing claim-first, problem-to-solution, workflow-to-validation, or evidence-map logic
Figures	Use figures as evidence; crop or split dense panels rather than shrinking them into unreadable slots
Output	Build a real `.pptx` as the primary deliverable, with Chinese text and speaker notes
QA	Reopen or inspect the PPTX package, record slide count, embedded media, notes, and any rendering limits
Integrity	Do not fabricate results, methods, numbers, datasets, mechanisms, or figure details

Reference files

skills/nature-paper2ppt/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
└── references/
    ├── design-and-layout.md
    ├── figure-assets.md
    └── self-review.md

nature-academic-search

What it does — Provides a multi-source academic search and reference-management workflow backed by a local MCP server. It searches PubMed, CrossRef and arXiv in parallel, fetches records by DOI, PMID or arXiv ID, formats citations, looks up MeSH terms, verifies bibliographic identifiers, and supports .nbib, .ris, .bib and .enw reference-file workflows.

Built from — A unified MCP server with source adapters for PubMed E-utilities, CrossRef REST metadata and arXiv Atom metadata, plus reusable workflow notes for source-tier routing, search strategy, citation parsing, deduplication, RIS/BibTeX field mapping and reference-file conversion.

Setup note — For Claude Code MCP use, run bash skills/nature-academic-search/install.sh your-email@example.com, restart Claude Code, and optionally set NCBI_API_KEY for higher PubMed rate limits. For plain prompt use, copy the whole skills/nature-academic-search/ directory like the other skills.

Key rules enforced

Domain	Core rule
Source routing	Start with structured API-backed sources: PubMed for biomedical searches, CrossRef for DOI and cross-disciplinary metadata, and arXiv for preprints
Fallback discipline	Escalate from T1 sources to limited APIs or scraped/manual sources only when needed, and warn when results may be incomplete
Deduplication	Merge multi-source hits by DOI, PMID, arXiv ID and normalized title rather than counting duplicate records as separate evidence
Citation verification	Resolve DOI, PMID and arXiv IDs before citation formatting; expose missing or failed metadata instead of filling fields by guesswork
MeSH strategy	Use MeSH lookup for biomedical PubMed queries when the task needs recall, controlled vocabulary or systematic search structure
File integrity	Preserve bibliographic fields when converting `.nbib`, `.ris`, `.bib` and `.enw`; do not fabricate volume, issue, pages, DOI or PMID values

MCP tools

Tool	Purpose
`search_papers`	Search CrossRef, PubMed and arXiv with optional source selection and per-source result limits
`get_paper_by_id`	Fetch paper metadata by DOI, PMID or arXiv ID with automatic ID-type detection
`get_citation`	Generate formatted citations in styles such as APA, Nature, IEEE, Vancouver, Chicago and MLA
`lookup_mesh`	Query PubMed MeSH descriptors for biomedical search-term expansion

Reference files

skills/nature-academic-search/
├── README.md
├── SKILL.md
├── manifest.yaml
├── static/
├── install.sh
├── config/
│   ├── mcp-snippet.json
│   ├── settings-snippet.json
│   └── triggers-academic-search.toml
├── mcp-server/
│   ├── academic_search_server.py
│   ├── sources/
│   ├── tests/
│   └── utils/
├── references/
│   ├── citation-parser.md
│   ├── dedup-engine.md
│   ├── ris-bibtex-format.md
│   ├── search-strategy.md
│   ├── source-tiers.md
│   └── workflows/
└── scripts/
    ├── converters.py
    ├── format-converter.py
    └── preflight.py

Example workflow — Search the same topic across PubMed, CrossRef and arXiv, merge and deduplicate candidate papers, verify key identifiers, look up MeSH terms for the biomedical subset, then export or convert the selected references for Zotero, EndNote or BibTeX.

Shared design principles

All skills in this collection adhere to the following:

Primary sources only — rules are grounded in published Nature content or official journal guidelines, not general style preference.
Explicit over implicit — every rule is stated with a rationale, not just asserted.
Section-aware — academic writing and figures both require context-sensitivity; each skill applies different logic depending on which part of a paper is being handled.
Output-first — every skill returns something immediately usable: copy-paste prose, a .svg file, a .pptx deck, or a concrete recommendation. No intermediate planning documents.
Extensible by design — each skill is self-contained in its own directory; adding a new skill requires no changes to existing ones.

Adding a new skill

To add a skill to this collection:

1. Create a directory

skills/nature-<topic>/

2. Minimum required files

File	Required	Purpose
`SKILL.md`	Yes	Frontmatter (`name`, `description`) + rules + workflow; loaded by the agent after triggering
`README.md`	Yes	Human-readable reference in full English
`references/*.md`	Recommended for complex skills	Modular rule files (api, design theory, tutorials, chart types, …)

3. SKILL.md frontmatter template

---
name: nature-<topic>
description: >-
  One-sentence description of what the skill does and when to trigger it.
  Include the output format and the primary use case.
---

4. Update this index

Add a row to the Skill index table above:

| [`nature-<topic>`](skills/nature-<topic>/README.md) | Draft / Stable | One-line purpose | trigger keywords |

5. Status labels

Label	Meaning
`Draft`	Rules defined; not yet tested on real examples
`Beta`	Tested on examples; edge cases may remain
`Stable`	Validated on real academic content; rules are settled

Candidate skills (not yet built)

The following are documented gaps. Contributions welcome.

Candidate	Scope	Priority
`nature-stats`	Statistical reporting conventions for Nature (effect sizes, confidence intervals, p-value formatting, sample size statements)	High
`nature-methods`	Deep-dive Methods writing assistant — reproducibility checklist, forbidden phrases, ethical approval templates, supplementary organisation	Medium
`nature-cover`	Cover letter drafting — hook paragraph, significance framing, fit-to-journal argument, ≤ 500-word limit	Medium

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项目会长期免费维护，大家正常使用就好。
很多朋友还是学生党，所以完全不需要有任何“必须打赏”的压力。

如果这些内容刚好帮你节省了一些时间、解决了一点问题，
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nature-skills

📢 课题组诚招“医学 + AI”实习生

Installation

1. Codex

2. Claude Code

3. Other agents or manual use

Star History

Skill index

nature-figure

nature-polishing

nature-writing

nature-reviewer

nature-citation

nature-data

nature-response

nature-paper2ppt

nature-academic-search

Shared design principles

Adding a new skill

Candidate skills (not yet built)

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💬 想说的话

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