Overview

Community

Welcome! Let's co-create nature-skills. A growing collection of Claude skills for producing academic work at Nature-journal standard. Currently covering scientific figures, manuscript prose, data availability, and paper-to-presentation workflows; future releases may add related tasks such as statistical reporting, peer‑review responses, and methods writing. Our philosophy — Every skill in this collection shares a common philosophy: rules are derived from primary sources (published Nature papers, journal author guidelines, and structured writing curricula), not from general style intuition.

The group is currently full. Please contact the administrator if you’d like to join.

---

Star History

---

Skill index

Skill	Status	Purpose	Trigger keywords
nature-figure	Stable	Publication-ready matplotlib figures	"Nature figure", "publication plot", "scientific figure"
nature-polishing	Stable	Academic prose polishing to Nature style	"Nature style", "polish", "academic writing"
nature-data	Draft	Nature Data Availability statements, repository plans, and FAIR checks	"Data Availability", "repository", "FAIR metadata", "数据可用性声明"
nature-paper2ppt	Beta	Chinese PPTX decks from scientific papers	"paper PPT", "journal club", "文献汇报", "论文做成 PPT"

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).

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

nature-figure/
├── README.md
├── SKILL.md
└── 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

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 — Close reading of five Nature s41586 papers (2026) and a graduate-level scientific English writing course; 25 rules extracted across sentence architecture, paper structure, vocabulary, citation integrity, house style, and AI ethics.

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

nature-polishing/
├── README.md
└── SKILL.md    25 rules + 12-step workflow (loaded by Claude automatically)

---

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 "数据可用性声明", "可向通讯作者索取", "原始数据", "受限数据", and "公开数据库" 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

nature-data/
├── README.md
├── SKILL.md
├── 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-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

nature-paper2ppt/
├── README.md
└── SKILL.md

---

Shared design principles

All skills in this collection adhere to the following:

1. Primary sources only — rules are grounded in published Nature content or official journal guidelines, not general style preference.
2. Explicit over implicit — every rule is stated with a rationale, not just asserted.
3. 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.
4. 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.
5. 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

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>`](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-response	Peer-review response letters — point-by-point reply structure, tone calibration, handling major vs. minor revisions	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
nature-review	Writing a literature review or review article in Nature Reviews style — synthesis vs. summary, argument-led structure	Low