Skip to main content
ClaudeWave
ES, Spanish
Skill5.9k estrellas del repoactualizado 9d ago

nature-polishing

# nature-polishing This Claude Code skill restructures and polishes academic prose to Nature journal standards using three layers: paper architecture and rhetorical strategy from course notes, published article patterns from Nature and Nature Communications, and reusable phrase families from Academic Phrasebank. Use it when revising any section of a scientific manuscript (abstract, introduction, methods, results, discussion, conclusion, title) or Chinese academic drafts, provided the core scientific argument is sound and only the reasoning clarity or English prose requires improvement.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholar /tmp/nature-polishing && cp -r /tmp/nature-polishing/skills/nature-polishing ~/.claude/skills/nature-polishing
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# 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.

## When to open extra files

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

| File | Open when |
|---|---|
| [references/published-article-patterns.md](references/published-article-patterns.md) | You need Nature/Nature Communications article-level writing patterns for abstracts, introductions, Results, Discussion, conclusions, or titles |
| [references/writing-strategy.md](references/writing-strategy.md) | You need paragraph- or section-level argument repair before sentence polishing |
| [references/section-moves.md](references/section-moves.md) | You need section-specific move orders or phrase patterns derived from Academic Phrasebank |
| [references/phrasebank-playbook.md](references/phrasebank-playbook.md) | You need hedging, transition, evidence, limitation, or future-work phrase families |
| [references/style-guardrails.md](references/style-guardrails.md) | You 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
agent-identifierSkill

Use when creating or configuring Claude Code agents and their frontmatter.

analyze-resultsSlash Command

Run a blocker-first post-experiment workflow: validate evidence, produce strict statistical analysis when possible, and generate a decision-oriented results report only when the analysis bundle is sufficient. Uses results-analysis + results-report as a gated two-stage workflow.

architecture-designSkill

Use only when creating new registrable ML components that require Factory or Registry patterns.

bug-detectiveSkill

This skill should be used when the user asks to "debug this", "fix this error", "investigate this bug", "troubleshoot this issue", "find the problem", "something is broken", "this isn't working", "why is this failing", or reports errors/exceptions/bugs. Provides systematic debugging workflow and common error patterns.

citation-verificationSkill

This skill provides reference guidance for citation verification in academic writing. Use when the user asks about "citation verification best practices", "how to verify references", "preventing fake citations", or needs guidance on citation accuracy. This skill supports ml-paper-writing by providing detailed verification principles and common error patterns.

code-review-excellenceSkill

This skill should be used when the user asks to review a diff or pull request, write review comments, audit code quality, establish review standards, or improve how a team performs code review.

code-reviewerSubagent

Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code. MUST BE USED for all code changes.

command-developmentSkill

This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.