nature-polishing
nature-polishing is an Academic writing & papers skill for AI agents, published by Yuan1z0825 in the nature-skills repository on GitHub. Skillspace rates nature-polishing D (68/100) and it passed the safety scan; a judge model has also reviewed it.
Polish, restructure, or translate academic prose into concise Nature-leaning English while preserving facts, evidence boundaries, terminology, and citation intent. Use for manuscript paragraphs, abstracts, introductions, Results, discussions, conclusions, titles, Methods, Chinese drafts, proofreading, language editing, and general academic or scientific writing. Also use to shorten bloated Results, allocate evidence across main text, captions, and Supplementary Information, prevent reviewer-driven revision accretion, reduce repeated statistics or claims, and apply paragraph-necessity checks. Covers LaTeX layout or typesetting fixes such as sparse pages, stranded headings, oversized or split figures, float errors, multi-panel arrangement, and sparse Supplementary Information via references/latex-layout.md. Trigger on 学术写作、科研写作、论文润色、SCI写作、英文论文润色、语言润色、润色、改写、学术英语、排版.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 41,237
Placed #1 of 6 in the judged category final for Academic writing & papers.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 99 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #1 of 6 in the judged category final for Academic writing & papers.
- Trigger
90 90 - Instructions
90 90 - Specificity
82 82 - Safety
87 87 - Disclosure
95 95 - Maintenance
90 90
The most granular of the Nature-style routers, adding main-text-versus-SI discipline, per-journal (flagship/Nat Comms/NMI) nuance, and an explicit flag-don't-fabricate rule behind a clean two-layer disclosure design.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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