Key points
- Project origin: nature-skills is an open-source repository by Yuan Yizhe, a PhD candidate at Shanghai Jiao Tong University working on medical AI. It has 151 commits and breaks Nature-level academic writing into 9 reusable Skills rather than a single tool.
- Nine-Skill pipeline:
nature-polishing(Stable): language polishing from Chinese drafts to Nature-style Englishnature-writing(Draft): argument scaffolding from claim to evidencenature-figure(Stable): publication-grade figures via Python/Rnature-citation(Beta): CNS-grade retrieval with RIS/ENW/Zotero exportnature-data(Draft): Data Availability statements and FAIR checksnature-reader(Beta): bilingual Markdown reader with figure groundingnature-response(Beta): point-by-point reviewer reply workflow with risk checksnature-paper2ppt(Beta): paper-to-Chinese-PPT conversionnature-academic-search(Beta): parallel PubMed + CrossRef + arXiv search- Polishing is rewriting, not editing: a 6-layer diagnostic (paper-type logic, section responsibility, argument gaps, sentence stacking, tense/voice mismatch, vocabulary/citation format) runs before a 12-step sentence workflow that enforces ≤30-word sentences, British English, hedging calibration (
demonstrate/suggest/may reflect), and overclaim detection. - Evidence-first writing order: Results → Introduction + Conclusion → Title → Discussion → Materials and Methods → Authors → Abstract. Section templates specify exact structural elements, e.g.,
field scale → bottleneck → prior attempts → unresolved gap → present studyfor Introductions. - Figures as visual argument: a mandatory "figure contract" requires stating the one-sentence claim, mapping each panel to the claim, selecting an archetype (quantitative grid, schematic-led composite, image plate + quant, asymmetric mixed-modality), choosing Python or R backend, and defining journal/export specs. Provided matplotlib settings target Arial, 7pt body text, editable SVG/PDF fonts, and triple export to
.svg/.pdf/.tiff. - Citation integrity: only sources the author has personally read are cited; primary vs. secondary sources are distinguished; four attribution types (idea, data, method, wording/structure/image) are tracked; DOIs/PMIDs are resolved before export.
- Reviewer response workflow: triage (major/minor/ambiguous) → action mapping → risk check → point-by-point response (acknowledgement → reply → modified location → revised text).
- AI traffic-light policy:
- Green: grammar fixes, outline options, translation, literature summary
- Yellow: explaining methods/results, drafting reply frames (requires line-by-line review)
- Red: writing the core argument, inserting AI-generated references or data, uploading unpublished manuscripts to public models
- Rules are grounded: every rule derives from published Nature/Nature Communications papers or official Author Guidelines, not personal preference.
- Output-first design: each Skill returns immediately usable artifacts (prose,
.svg/.pdf/.tiff,.ris,.pptx), not intermediate planning documents. - Installation:
- Claude Code:
/plugin marketplace add https://github.com/Yuan1z0825/nature-skills, then/plugin install nature-skills, then/reload-plugins - Codex Desktop: add the repo URL as a custom plugin marketplace on
mainand install - Manual:
git clone https://github.com/Yuan1z0825/nature-skills.gitand copy eachskills/nature-*directory into~/.claude/skills/or~/.codex/skills/ - Limitations: only polishing and figure are Stable; the rest are Draft/Beta; rules target the Nature family so Cell/Science may need adjustment; plugin installation requires Claude Code or Codex Desktop; the pipeline does not analyze raw experimental data, so statistical errors remain the author's responsibility.
- Roadmap candidates:
nature-stats,nature-methods,nature-cover,nature-review. - GitHub:
github.com/Yuan1z0825/nature-skills - Author: Yuan Yizhe, PhD candidate, Shanghai Jiao Tong University (medical AI)
- Source article: Juejin community, 2026-05-10