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academic-writing-refiner学术写作精炼者

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

517

周安装

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GitHub Stars

209

下载量

181
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:academic-writing-refiner(学术写作精炼者)
来源仓库:https://github.com/wentorai/research-plugins
仓库路径:skills/academic-writing-refiner
安装命令:
npx skills add https://github.com/wentorai/research-plugins --skill academic-writing-refiner
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/wentorai/research-plugins --skill academic-writing-refiner

简介

学术写作精炼者用于辅助文档、README、Markdown 等内容的整理与改写,提升文本可读性。

  • 它适合提炼结构、补齐章节、统一术语或检查链接,适用于内容稿件优化场景。
  • 通过 npx skills add 命令从指定仓库安装,需保留项目已有事实和路径。
  • 涉及对外文案时应控制语气,避免过度营销或夸大能力。
  • academic-writing-refiner 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Academic Writing Refiner

Overview

Academic papers are judged not only on their scientific merit but also on the quality of their English. Grammatical errors, awkward phrasing, and non-native patterns (commonly called "Chinglish" when originating from Chinese-English interference) can distract reviewers and undermine credibility.

This skill provides a systematic checklist for identifying and correcting common academic English issues. It draws from the academic-writing-guide repository (327+ stars) maintained by SYSUSELab, which catalogs frequent mistakes observed in student and researcher manuscripts across STEM disciplines.

The approach is checklist-driven: rather than relying solely on automated tools, researchers learn to recognize error patterns and self-edit effectively. This skill is especially useful for non-native English speakers preparing manuscripts for international journals and conferences.

Common Error Categories

Grammar and Syntax Errors

Error TypeExample (Wrong)Correction
Article misuse"We propose a novel the method""We propose a novel method"
Subject-verb disagreement"The results shows that...""The results show that..."
Tense inconsistency"We train the model and evaluated it""We trained the model and evaluated it"
Dangling modifier"Using gradient descent, the loss decreased""Using gradient descent, we decreased the loss"
Run-on sentence"The model converges fast it achieves high accuracy""The model converges fast and achieves high accuracy"

Chinglish Patterns

These are interference patterns common when translating from Chinese thought patterns into English:

  1. Topic-comment structure. Chinese allows "As for X, Y does Z." English prefers "Y does Z to X."

- Wrong: "As for the dataset, we use ImageNet." - Better: "We use ImageNet as our dataset."

  1. Redundant verbs. Chinese often uses verb-verb compounds that become redundant in English.

- Wrong: "We can be able to achieve..." - Better: "We can achieve..."

  1. Missing determiners. Chinese has no articles, leading to dropped "the/a/an."

- Wrong: "Model achieves state-of-art result." - Better: "The model achieves a state-of-the-art result."

  1. Overuse of "respectively."

- Wrong: "The accuracy and F1 are 95% and 0.93 respectively." - Better: "The accuracy is 95% and the F1 score is 0.93."

  1. "With the development of..." This opening is overused to the point of cliche.

- Better: Lead with the specific problem or finding.

Self-Editing Checklist

Apply this checklist before submitting any manuscript:

Pass 1: Structure and Flow

  • Each paragraph has a clear topic sentence.
  • Transitions connect paragraphs logically (however, therefore, in contrast).
  • No paragraph exceeds 200 words.
  • Figures and tables are referenced in order.

Pass 2: Grammar and Mechanics

  • All verbs agree with their subjects.
  • Tense is consistent within each section (past for Methods/Results, present for general truths).
  • Every acronym is defined on first use.
  • No comma splices or run-on sentences.
  • Articles (a, an, the) are used correctly.

Pass 3: Style and Conciseness

  • Remove "very," "really," "quite," "basically" unless essential.
  • Replace "in order to" with "to."
  • Replace "a large number of" with "many" or give the exact count.
  • Convert passive voice to active where possible.
  • Eliminate "it is well known that" and similar filler phrases.

Pass 4: Technical Accuracy

  • All numbers have units.
  • Table column headers are unambiguous.
  • Equations are numbered and referenced.
  • Statistical claims include confidence intervals or p-values.

Automated Polishing Tools

While manual review is irreplaceable, these tools serve as a useful second pass:

# LanguageTool CLI for grammar checking
java -jar languagetool-commandline.jar -l en-US paper.tex

# Writefull for academic-specific suggestions (VS Code extension)
# Install from VS Code marketplace: "Writefull for LaTeX"

# textlint for rule-based prose linting
npm install -g textlint textlint-rule-no-dead-link textlint-rule-write-good
textlint paper.md

Tool Comparison

ToolTypeAcademic FocusFree Tier
GrammarlyCloudGeneral + academicYes (limited)
WritefullPluginHigh (trained on papers)Yes
LanguageToolLocal/CloudGeneralYes (full)
textlintCLIConfigurable rulesYes (open source)
TrinkaCloudHigh (academic-specific)Yes (limited)

Sentence-Level Revision Examples

Before and After

Before: "In recent years, with the rapid development of deep learning, more and more researchers have paid attention to the problem of image classification, which is a very important task in computer vision."

After: "Image classification is a fundamental task in computer vision. Recent advances in deep learning have renewed interest in this problem, with convolutional and transformer architectures achieving human-level accuracy on standard benchmarks."

Before: "We can observe from Table 1 that our method can achieve better performance than baseline methods in terms of all evaluation metrics."

After: "Our method outperforms all baselines across every metric (Table 1)."

Best Practices

  • Read published papers in your target venue. Absorb the register and conventions of journals like Nature, IEEE TPAMI, or ACL.
  • Have a native speaker review critical sections. At minimum, ask someone to read the Abstract and Introduction.
  • Use consistent terminology. If you call it "feature extraction" in Section 2, do not switch to "representation learning" in Section 4 unless you define the distinction.
  • Limit sentences to 25 words on average. Long sentences are harder to parse, especially for non-native readers.
  • Revise in separate passes. Do not try to fix grammar, structure, and style simultaneously.

References

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