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academic-writing-refiner
Checklist-driven academic English polishing and Chinglish correction
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Checklist-driven academic English polishing and Chinglish correction
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
公司金融实证研究的"漏斗式选题查找器"。互动开场先后询问 (1) 研究方向、(2) 候选标题数量 N, 再扫描全球文献(已出版英文学术期刊 + SSRN working paper + 全球高校 department seminar 1 年内日程),基于 Edmans (2024) "1000 Rejections" 红线生成 N 个候选标题,**通过并行 subagent(Agent 工具)批量生成计划书 + 查新;每个 subagent 必须强制调用 Skill 工具加载 econfin-proposal 与 novelty-check 两个预设 skill 完成各自模块**,**只有当 novelty score >= 9 时(即 JF/JFE/RFS 顶刊层次),subagent 才把 proposal + 查新报告合并的 md 写入 F:\Dropbox\CC\选题大全\<研究方向短名>\(以"简短选题名称-分数"命名,子文件夹名由 Step 0 从用户输入的研究方向派生);< 9 分的选题在 subagent 内部直接丢弃,绝不写盘、绝不输出**。当用户说"找选题"、"帮我找选题"、"想做 X 方向"、 "empirical CF idea search"、"批量生成研究计划书"、"100 ideas"、"econfin-idea-finder" 时触发。
Create and compile beautiful Beamer presentations following the Rhetoric of Decks philosophy. Use when making slides, creating decks, or compiling .tex presentation files.
Scaffold a new research project with standard directory structure, CLAUDE.md template, and documented README. Use this at the start of every new project to ensure consistent organization.
Download, split, and deeply read academic PDFs. Use when asked to read, review, or summarize an academic paper. Splits PDFs into 4-page chunks, reads them in small batches, and produces structured reading notes — avoiding context window crashes and shallow comprehension.
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.
| name | academic-writing-refiner |
| description | Checklist-driven academic English polishing and Chinglish correction |
| metadata | {"openclaw":{"emoji":"🔍","category":"writing","subcategory":"polish","keywords":["academic English grammar check","readability improvement","academic writing style","Chinglish correction"],"source":"https://github.com/SYSUSELab/academic-writing-guide"}} |
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.
| Error Type | Example (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" |
These are interference patterns common when translating from Chinese thought patterns into English:
Topic-comment structure. Chinese allows "As for X, Y does Z." English prefers "Y does Z to X."
Redundant verbs. Chinese often uses verb-verb compounds that become redundant in English.
Missing determiners. Chinese has no articles, leading to dropped "the/a/an."
Overuse of "respectively."
"With the development of..." This opening is overused to the point of cliche.
Apply this checklist before submitting any manuscript:
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 | Type | Academic Focus | Free Tier |
|---|---|---|---|
| Grammarly | Cloud | General + academic | Yes (limited) |
| Writefull | Plugin | High (trained on papers) | Yes |
| LanguageTool | Local/Cloud | General | Yes (full) |
| textlint | CLI | Configurable rules | Yes (open source) |
| Trinka | Cloud | High (academic-specific) | Yes (limited) |
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)."