Agent skill

Nature Polishing

by Galaxy-Dawn in Galaxy-Dawn/claude-scholar

Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from…

MITAuto-check passedResearch & Science

Install Nature Polishing

skills CLI
$ npx skills add Galaxy-Dawn/claude-scholar --skill nature-polishing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Galaxy-Dawn/claude-scholar nature-polishing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholar.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-polishing .claude/skills/nature-polishing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nature-polishing
GitHub stars
5.7k
Used in
3 other repos
Token cost
~3.6k tokens
SKILL.md length
1,883 words
Files
7 (incl. references)
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from…

  • Works in 6 steps: Identify the paper type first → Write for the reader, not for the draft… → Use the hourglass structure → …
  • The user asks to polish a manuscript paragraph
  • SKILL.md covers Default stance, Mined writing memory, When to open extra files and Core architecture, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nature Polishing is an agent skill from Galaxy-Dawn/claude-scholar. Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `README.md`, `references/phrasebank-playbook.md` and `references/published-article-patterns.md`).

It sits in Research & Science, covering Data visualization. The repository describes itself as: Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding… The licence is MIT.

When your agent uses it

  • The user asks to polish a manuscript paragraph
  • Methods section
  • Chinese academic draft for publication-quality English

Example prompts

  • “/nature-polishing”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Identify the paper type first
  2. Write for the reader, not for the draft chronology
  3. Use the hourglass structure
  4. Use the correct writing order
  5. Protect the core argument
  6. Diagnose the failure mode before editing

What it can do on your machine

Read from SKILL.md and the folder at commit 9037873. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nature Polishing loads about 3.6k tokens when it runs, and up to ~8.8k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 1,883 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Galaxy-Dawn/claude-scholar at commit 9037873, republished under its MIT licence (© Galaxy-Dawn). 1,883 words, ~3,585 tokens.

Download SKILL.mdSave it as .claude/skills/nature-polishing/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
nature-polishing
description
Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English.
version
5.0.2
author
Yuan1z skill rebuilt from course notes plus Academic Phrasebank

Nature-Style Academic Polishing

Use this skill to improve scientific writing at two levels:

  • main strategy: paper architecture, published-article patterns, section logic, reader workflow, evidence thresholds, and ethics
  • reference support: reusable phrase families, move patterns, transitions, and style checks

The main strategy should come from the course notes in Chapter1-Week1-7 and the curated article-pattern reference. The wording layer should come from Academic Phrasebank.

Default stance

  • Language serves argument. Do not polish sentences while leaving the reasoning broken.
  • Write with empathy for the reader: relevance first, then novelty, then trust, then reuse, then meaning.
  • There should be no mystery for the writer, but there may be one for the reader.
  • Do not invent data, references, mechanisms, or novelty claims.
  • Do not let AI draft the paper's core scientific argument from scratch.
  • If the draft is Chinese or structurally rough, reconstruct the logic first and the prose second.
  • Avoid em dashes in polished output by default. Prefer commas, parentheses, or full stops. Use colons sparingly unless the user explicitly asks to preserve dash-based punctuation or wants a colon-led style.

Mined writing memory

For academic prose, check the active installed skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md under the current client's skill home. Use only source-attributed entries that fit the section and venue, alongside this skill's curated references. Treat them as optional style and structure examples. Preserve the author's claims and evidence, follow current journal instructions, and do not copy source phrasing. If the memory has no relevant entries, continue without it.

When to open extra files

These files are reference support. Use them after the section's rhetorical job is clear.

FileOpen when
references/published-article-patterns.mdYou need Nature/Nature Communications article-level writing patterns for abstracts, introductions, Results, Discussion, conclusions, or titles
references/writing-strategy.mdYou need paragraph- or section-level argument repair before sentence polishing
references/section-moves.mdYou need section-specific move orders or phrase patterns derived from Academic Phrasebank
references/phrasebank-playbook.mdYou need hedging, transition, evidence, limitation, or future-work phrase families
references/style-guardrails.mdYou need academic-style checks, paragraph/sentence checks, article use, register, or mechanics

Core architecture

1. Identify the paper type first

Before editing, determine what kind of paper or section this is.

  • Research paper: the reader asks why the phenomenon matters, what was done, what was found, and what it means.
  • Methods paper: the reader asks whether the method works, whether it is reproducible, and whether it is better under a fair comparison.
  • Hypothesis-based work: the argument tries to establish or rule out a causal explanation.
  • Algorithmic or device work: the argument proposes a procedure, tool, or system and must show that it performs reliably and advantageously.

Do not use one narrative logic for all paper types.

For article-level rewrites, especially abstracts, introductions, Results openings, Discussion paragraphs, conclusions, and titles, also apply the writing patterns in references/published-article-patterns.md.

2. Write for the reader, not for the draft chronology

Most readers follow a stable sequence:

  1. Is this relevant to me?
  2. What is new here?
  3. Do I trust it?
  4. Can I reuse it?
  5. What does it mean, and where are the boundaries?

Polishing should help the paper answer these questions in this order.

3. Use the hourglass structure

Strong papers often mirror an hourglass:

  • Introduction: open broadly, then narrow to the specific gap, question, hypothesis, methods, and study
  • Discussion/Conclusion: widen again, connecting the findings back to the literature and explaining how the knowledge gap was filled

If a paragraph or section violates this architecture, rebuild it before polishing wording.

4. Use the correct writing order

For a research article, a productive writing order is:

  1. Results
  2. Introduction and Conclusion
  3. Title
  4. Discussion
  5. Materials and Methods
  6. Authors
  7. Abstract

For a methods paper, a productive writing order often begins with:

  1. Methods
  2. Results
  3. Introduction
  4. Conclusion
  5. Discussion
  6. Abstract

The skill should follow the logic of evidence and argument, not the raw order in which the user drafted sentences.

5. Protect the core argument

The paper's core argument includes:

  • the scientific question the paper actually answers
  • why that question matters
  • how the work differs from existing research
  • what the results imply
  • how the main line of reasoning unfolds

AI may help polish, structure, or compare phrasings. AI should not invent or author the core argument. If the argument is weak or unclear, expose that weakness rather than hiding it under polished language.

6. Diagnose the failure mode before editing

Before rewriting, identify the main problem:

  • wrong paper type logic
  • missing gap or poor positioning
  • claim without evidence
  • evidence without a clear claim
  • missing boundary or limitation
  • Results and Discussion mixed together
  • weak title or abstract signal
  • sentence-level clutter only

Prioritize in this order:

paper type -> section job -> paragraph logic -> claim/evidence/boundary -> sentence polish

Section responsibilities

Introduction

The Introduction should:

  • tell the reader why the work matters
  • explain what gap it fills
  • explain why that gap matters
  • state what is already known
  • state what remains unresolved
  • state what question the paper asks
  • indicate how the study addresses it

Do not summarize the Results section here. Do not summarize the Conclusion here.

Results

Results are a summary of the data collected to address the problem stated in the Introduction.

Results writing should:

  • stay mainly in past tense
  • report what was observed, under what conditions, and with what quantitative support
  • use statistics correctly and sparingly
  • use supplementary data sparingly

Results should answer what happened, not what it ultimately means.

Discussion

Discussion should answer:

  • how the work fits within the broader field
  • what has been added to understanding
  • who should be credited for earlier work
  • whether the findings support, complicate, or revise earlier results
  • how the findings are interpreted
  • when that interpretation may fail

Short rule:

  • Results = what we observed
  • Discussion = how we understand it, and when it may fail
Conclusion

Use the three-part close:

  1. restate the central contribution
  2. summarize the key evidence or outcome
  3. state the implication with a boundary

Do not introduce new data in the conclusion. Always run an overclaim check here.

Title

A strong title should:

  • tell the reader what to expect
  • avoid unnecessary technical language
  • be easy to search
  • be substantiated by data
  • create curiosity without sacrificing credibility

Use curiosity with credibility, not empty cleverness. A hook is only acceptable if the claim remains fully defensible.

Materials and Methods

Methods should be specific, complete, transparent, and reproducible.

Another group should be able to determine:

  • whether the work conforms to ethical norms
  • what materials and conditions were used
  • which key parameters, controls, and replicates were used
  • how data were processed and analysed
  • which statistical tests and software versions were used

It is acceptable to abbreviate by citing an earlier report only when that report truly contains the necessary detail.

Never leave vague phrases such as:

  • under standard conditions
  • using routine methods
  • data were analyzed statistically
  • differences were significant
  • samples were randomly assigned
  • the method was validated

Replace them with the actual reproducible information.

Show full SKILL.md (746 more words)Show less
Methods-paper variant

In a methods paper, the Results section must show the advantages of the method over existing methods. Typical questions are:

  • Is it more reliable?
  • Is it faster?
  • Does it require fewer resources?
  • Is the comparison fair and reproducible?

The Methods section in a methods paper may need additional detail such as:

  • axioms, conditions, and assumptions
  • hardware and software environment
  • mathematical derivations
  • evaluation protocol
  • datasets, baselines, metrics, splits, and hyperparameters
Abstract

The abstract is a mini-paper:

context/problem -> gap/objective -> approach -> key results -> implication

It should answer:

  1. What question was addressed?
  2. How was it addressed?
  3. What was found?
  4. Why should anyone care?

Some journals require a strict abstract format. Follow the journal if it conflicts with the generic pattern.

Sentence and paragraph control

Sentence rules
  • In polished prose, aim for sentences in the 10-30 word range.
  • Keep every sentence at <= 30 words.
  • Do not produce full sentences under 10 words unless the user explicitly asks for terse style or the item is a heading, label, or fixed technical expression.
  • If any sentence exceeds 20 words, check whether it contains more than one main proposition.
  • Split overloaded sentences rather than polishing them cosmetically.
  • The last sentence of a paragraph often becomes the longest and weakest. Check it explicitly.
  • Prefer one core subject-verb proposition per sentence.
  • Do not use em dashes as prose punctuation in the polished version unless the user explicitly requests them. Rewrite with commas, parentheses, or shorter sentences instead. Use colons only when they add clear structural value.
Paragraph rules
  • Each paragraph should have one controlling idea followed by support.
  • Supporting material may include data, comparison, explanation, consequence, literature, or limitation.
  • If a new idea appears, start a new paragraph instead of stacking it onto the old one.
  • Use thematic linking, not repetitive This suggests ... openings.
Results vs Discussion sentence types

Results sentences usually report:

  • was detected
  • increased
  • showed
  • enabled
  • achieved

Discussion sentences usually interpret:

  • may reflect
  • suggests that
  • could indicate
  • is likely due to
  • may facilitate

Do not let a Results paragraph drift into Discussion syntax unless the transition is intentional.

Chinese-to-English mode

When the source is Chinese or strongly Chinese-influenced English:

  • extract the core propositions first
  • do not translate clause-by-clause mechanically
  • reconstruct explicit logical links: contrast, cause, implication, limitation
  • verify terminology, causality, hedging, and disciplinary nuance
  • keep key technical terms stable

Citation, ethics, and AI boundaries

Intellectual debt

Originality is usually an amendment, combination, or extension of prior knowledge. A careful writer acknowledges that debt openly.

Do not minimize others' contributions just to make the present work seem more original.

Position attribution clearly

Make it obvious:

  • how the paper builds on prior work
  • who was responsible for the earlier idea, method, data, or interpretation
  • where the reader can locate the source
Cite the source you actually read and verified
  • Cite paper A for A's own data, methods, claims, or conclusions.
  • Cite paper B for B's interpretation, comparison, critique, or commentary on A.
  • Avoid leaning on secondary sources when the source article can be cited directly.
What needs citation
  • someone else's ideas
  • data
  • methods
  • wording
  • structure
  • images
  • distinctive interpretation

Do not assume internet material is public domain just because it is online.

Proofreading checks

Always verify:

  • grammatical errors
  • typographical errors
  • figure numbering
  • missing citations
  • whether the paper is a pleasure or an ordeal to read
AI traffic-light boundary

Green: generally acceptable with author verification

  • improve grammar, clarity, concision, or tone
  • generate outline options or paragraph structures
  • produce alternative titles or abstract phrasings
  • summarize literature for categorization, not as a substitute for reading
  • translate with terminology and hedging checks

Yellow: allowed only with strong human control

  • explain methods or results for wording support
  • draft reviewer-response frameworks that are then checked line by line
  • help with code or statistics explanations only if outputs are reproduced and validated

Red: generally inappropriate

  • ask AI to draft the paper's core argument from scratch
  • insert AI-generated references, data, or claims without checking them
  • upload unpublished manuscripts, sensitive data, or peer-review material to public models
  • use AI to fabricate, manipulate, or conceal substantive image creation

The main danger is not that AI cannot write. The main danger is that it can write incorrectly with great confidence.

Output format

Default output:

  1. The polished text as plain prose, not in a code block.
  2. Revision notes: with 3-5 short bullets on the major structural and stylistic changes.
  3. If the rewrite changed section logic, say so explicitly.

If the user asks for side-by-side revision, provide:

  • Original
  • Polished
  • Why changed

© Galaxy-Dawn, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (references) in skills/nature-polishing of Galaxy-Dawn/claude-scholar.

  • SKILL.md
  • README.md
  • references/phrasebank-playbook.md
  • references/published-article-patterns.md
  • references/section-moves.md
  • references/style-guardrails.md
  • references/writing-strategy.md

Open the folder on GitHubat commit 9037873

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in Galaxy-Dawn/claude-scholar, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Nature Polishing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Nature Polishing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nature Polishing this skillGalaxy-Dawn/claude-scholar5.7k3 repos~3.6kAutomated safety check: PassMIT
Nature-Style Scientific FiguresYuan1z0825/nature-skills47k—~3.1kAutomated safety check: PassApache-2.0
Engineering Figure Agentheyu-233/engineering-figure-agent307—~1.1kAutomated safety check: PassMIT
Scientific Figure GeneratorDeepshare-Official/CCF-Figure227—~1.8kAutomated safety check: PassMIT
Scanpy Single-Cell Analysisdavila7/claude-code-templates33k15 repos~2.8kAutomated safety check: PassMIT
deepTools NGS Toolkitdavila7/claude-code-templates33k12 repos~4.5kAutomated safety check: PassMIT

Similar skills

  • Nature-Style Scientific Figures

    Yuan1z0825/nature-skills

    Creates, revises, audits and exports manuscript-ready scientific figures in Python or R, and routes AI-generated graphical abstracts to a separate workflow.

    47k GitHub stars~3.1k tokensUpdated today
    Research & ScienceAuto-check passed
  • Engineering Figure Agent

    heyu-233/engineering-figure-agent

    A skill your agent uses when the user needs engineering or research-paper figures: system architecture diagrams, algorithm workflows, hardware schematics, benchmark charts, ablation plots, figure…

    307 GitHub stars~1.1k tokensUpdated 4 mo ago
    Research & ScienceAuto-check passed
  • Scientific Figure Generator

    Deepshare-Official/CCF-Figure

    Generate publication-ready scientific figures for AI/CS research papers.

    227 GitHub stars~1.8k tokensUpdated 3 mo ago
    Research & ScienceAuto-check passed
  • Scanpy Single-Cell Analysis

    davila7/claude-code-templates

    Walks through single-cell RNA-seq analysis with Scanpy: loading .h5ad and 10X data, QC, normalization, PCA and UMAP, Leiden clustering, marker genes and cell type annotation.

    33k GitHub starsUsed in 15 repos~2.8k tokens
    Research & ScienceAuto-check passed
  • deepTools NGS Toolkit

    davila7/claude-code-templates

    Guides use of deepTools on sequencing data: BAM to bigWig conversion, QC, sample correlation, and heatmaps or profiles around TSS and peaks for ChIP-seq, RNA-seq and ATAC-seq.

    33k GitHub starsUsed in 12 repos~4.5k tokens
    Research & ScienceAuto-check passed
  • Research Agent

    aws-samples/sample-strands-agent-with-agentcore

    Official

    Deep research with structured reports and charts. An agent skill from aws-samples/sample-strands-agent-with-agentcore.

    195 GitHub stars~1.1k tokensUpdated 2 days ago
    Research & ScienceAuto-check passed

More from Galaxy-Dawn/claude-scholar

All 34 skills in this repo
  • Citation Verification Guide

    Galaxy-Dawn/claude-scholar

    Reference guidance for checking every citation in academic writing against canonical sources such as DOI, arXiv, CrossRef and Semantic Scholar, to catch fake or wrong references.

    5.7k GitHub starsUsed in 2 repos~1.9k tokens
    Auto-check passed
  • Skill Improvement Plan Executor

    Galaxy-Dawn/claude-scholar

    Reads an improvement-plan file from a companion quality-review skill and applies its suggested fixes to a Claude Skill, backing up first.

    5.7k GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed
  • Skill Quality Reviewer

    Galaxy-Dawn/claude-scholar

    Scores a skill across description, content organization, writing style and structure, then produces letter grades and a prioritized improvement plan.

    5.7k GitHub starsUsed in 1 repo~3k tokens
    Auto-check passed
  • UI/UX Design System Advisor

    Galaxy-Dawn/claude-scholar

    Turns a vague UI request into a concrete design system with style, palette, typography and layout guidance from a search script, plus stack-specific implementation advice.

    5.7k GitHub starsUsed in 1 repo~1.1k tokens
    Auto-check passed
  • Daily Paper Digest

    Galaxy-Dawn/claude-scholar

    Finds recent arXiv and bioRxiv papers on a topic, narrows them in stages to one pick per field, and writes bilingual Chinese and English summaries.

    5.7k GitHub stars~1k tokensUpdated 18 days ago
    Auto-check passed
  • Nature Data

    Galaxy-Dawn/claude-scholar

    Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts.

    5.7k GitHub starsUsed in 3 repos~1.6k tokens
    Auto-check passed

Questions about Nature Polishing

What does Nature Polishing do?

Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from…. Nature Polishing is an agent skill from Galaxy-Dawn/claude-scholar. Polish, restructure, or translate academic prose into Nature-leaning English using writing-strategy principles, curated Nature/Nature Communications article patterns, and phrase-level support from Academic Phrasebank.

When should I use Nature Polishing?

Nature Polishing fits situations like: the user asks to polish a manuscript paragraph; methods section; chinese academic draft for publication-quality English.

How do I install Nature Polishing in Claude Code?

Run `npx skills add Galaxy-Dawn/claude-scholar --skill nature-polishing -a claude-code`. Or copy the skill folder (skills/nature-polishing in Galaxy-Dawn/claude-scholar) into .claude/skills/nature-polishing in your project. Claude Code loads it when a task matches its description.

How do I install Nature Polishing in Codex?

Run `npx skills add Galaxy-Dawn/claude-scholar --skill nature-polishing -a codex`. Or copy the skill folder (skills/nature-polishing in Galaxy-Dawn/claude-scholar) into .agents/skills/nature-polishing in your project. Codex loads it when a task matches its description.

Can I use Nature Polishing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Galaxy-Dawn/claude-scholar --skill nature-polishing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-polishing, .gemini/skills/nature-polishing, .github/skills/nature-polishing and .opencode/skills/nature-polishing in your project.

What does Nature Polishing need to run?

SKILL.md names no scripts, command-line tools or credentials: Nature Polishing is instructions for the agent only.

Does Nature Polishing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nature Polishing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nature Polishing use?

Nature Polishing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nature Polishing use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.2k tokens, read only when the agent opens those files.

What are the alternatives to Nature Polishing?

Skills that share tags, products or a category with Nature Polishing: Nature-Style Scientific Figures (Yuan1z0825/nature-skills, 47k stars), Engineering Figure Agent (heyu-233/engineering-figure-agent, 307 stars), Scientific Figure Generator (Deepshare-Official/CCF-Figure, 227 stars) and Scanpy Single-Cell Analysis (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nature Polishing?

Galaxy-Dawn (a GitHub user) maintains it in Galaxy-Dawn/claude-scholar, which has 5,725 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on September 23, 2026.

Source: Galaxy-Dawn/claude-scholar on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.