academic-writing-refiner
Checklist-driven academic English polishing and Chinglish correction
Best use case
academic-writing-refiner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Checklist-driven academic English polishing and Chinglish correction
Teams using academic-writing-refiner should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/academic-writing-refiner/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How academic-writing-refiner Compares
| Feature / Agent | academic-writing-refiner | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Checklist-driven academic English polishing and Chinglish correction
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 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" | ### 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." 2. **Redundant verbs.** Chinese often uses verb-verb compounds that become redundant in English. - Wrong: "We can be able to achieve..." - Better: "We can achieve..." 3. **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." 4. **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." 5. **"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: ```bash # 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 | 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) | ## 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 - [academic-writing-guide](https://github.com/SYSUSELab/academic-writing-guide) -- Writing checklist and tutorials (327+ stars) - [Common Errors in Technical Writing](https://www.cs.columbia.edu/~hgs/etc/writing-bugs.html) -- Henning Schulzrinne, Columbia University - [Writefull](https://writefull.com/) -- AI writing assistant trained on published papers - [LanguageTool](https://languagetool.org/) -- Open-source grammar checker
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