codex-claude-academic-skills

name codex-claude-academic-skills description Chinese-first academic research skills for paper writing, Office document generation, and scientific computing (MATLAB/Python) triggers ["help me write a research paper in Chinese","generate academic PPT from my paper","create literature review Word document","run MATLAB simulation for my experiment","polish my paper abstract and introduction","make a thesis defense presentation","analyze scientific data with Python","respond to reviewer comments"] Codex Claude Academic Skills Skill by ara.so — Codex Skills collection. A collection of three complementary skills for Chinese academic researchers covering paper writing, academic Office document generation, and scientific computing. All skills work in both Claude Code and Codex platforms. What This Project Does This project provides three specialized skills for academic workflows: research-writing-skill : Paper writing, editing, and reviewer response in Chinese office-academic-skill : Generate editable Word reports and PowerPoint presentations scientific-toolkit-skill : Scientific computing with MATLAB/Python and publication-quality figures The skills are designed to work together across the research pipeline: data analysis → paper writing → presentation generation. Installation For Claude Code # Clone the repository git clone https://github.com/zLanqing/codex-claude-academic-skills.git # Install all three skills globally cd codex-claude-academic-skills cp -r research-writing-skill ~/.claude/skills/ cp -r office-academic-skill ~/.claude/skills/ cp -r scientific-toolkit-skill ~/.claude/skills/ # Or install via plugin (if supported) /plugin install zLanqing/codex-claude-academic-skills For Codex # Install to global skills directory cd codex-claude-academic-skills cp -r research-writing-skill ~/.codex/skills/ cp -r office-academic-skill ~/.codex/skills/ cp -r scientific-toolkit-skill ~/.codex/skills/ # Or load temporarily with plugin URL codex --plugin-url https://github.com/zLanqing/codex-claude-academic-skills Project-Level Installation Place skill directories in your project root: mkdir -p .claude/skills/ cp -r path/to/research-writing-skill .claude/skills/ # Or for Codex mkdir -p .codex/skills/ cp -r path/to/office-academic-skill .codex/skills/ Skill 1: research-writing-skill Core Capabilities Write paper sections: abstract, introduction, related work, methods, experiments, discussion, conclusion Edit and polish existing drafts for logic, consistency, and terminology Generate reviewer response letters (rebuttal) Plan paper structure from vague ideas to detailed outlines Usage Patterns Writing a new section: # User prompt example: # "帮我写一个关于BOTDR传感器的方法章节，已有的实验数据在data.csv" # The skill will: # 1. Ask for key information (sensor parameters, measurement setup) # 2. Check if data.csv exists and analyze it # 3. Draft the methods section in Chinese # 4. Preserve English terms: BOTDR, Brillouin scattering, etc. # 5. Mark inferred vs. confirmed information Polishing existing text: # User provides a Chinese draft: """ 我们提出了一种新的算法，它很有效，实验结果显著优于传统方法。 """ # Skill rewrites to: """ 我们提出了基于小波去噪的 BGS 重构算法。在 100 组实验数据集上， 相比传统高斯拟合方法，本方法的温度测量误差从 ±2.5°C 降低至 ±0.8°C， 空间分辨率从 1m 提升至 0.5m。 """ # (Removes vague words, adds measurable baselines) Reviewer response: # User: "审稿人说我的实验样本量不够，帮我回复" # Skill generates structured response: """ **Reviewer Comment:** The sample size (n=30) is insufficient for statistical significance. **Response:** 感谢审稿人的建议。我们已补充实验至 n=100（新增数据见附录 A）。 更新后的统计检验结果（t-test, p<0.01）已添加至第 4.2 节表 3。 修订稿中已说明样本量选择依据 Cohen's d 效应量计算（d=0.85, power=0.95）。 **Changes in manuscript:** - Line 203-205: 补充样本量计算依据 - Table 3: 更新统计检验结果 - Appendix A: 新增 70 组实验数据 """ Key Principles Chinese-first : Explanations, body text in Chinese; preserve English for paper titles, formulas, variable names, software commands, citations No fabrication : Never invent DOI, journal names, experimental values, or figure numbers Source marking : Label claims as "from user", "inferred", "suggested extension", or "original text" Measurable claims : Replace "significant improvement" with "error reduced from X to Y" Reference Files The skill includes built-in references under paper-writing/ : references/ ├── section_rhetorical_moves/ │ ├── abstract.md # IMRaD structure for abstracts │ ├── introduction.md # Hook → Gap → Contribution pattern │ ├── methods.md # Replicability checklist │ └── results.md # Figure-first narrative ├── writing_checklists/ │ ├── before_submission.md # Pre-submission self-check │ └── revision_guide.md # Responding to major revisions ├── figure_templates/ │ └── multi_panel.md # Standards for composite figures └── brainstorming_guide.md # From idea to paper blueprint Skill 2: office-academic-skill Core Capabilities Word Documents: PDF → structured literature review report (.docx) Generate editable reports with headings, tables, figure placeholders, citations Version-controlled editing of existing .docx files PowerPoint Presentations: Literature report slides, group meeting presentations Thesis defense slides (proposal, mid-term, final defense) Scientific poster presentations, outreach slides Usage Patterns Generate literature report from PDF: # User: "把这篇论文转成文献阅读报告" # Uploads: paper.pdf # Skill workflow: # 1. Extract text and figures from PDF # 2. Generate structured report.docx: from docx import Document from docx.shared import Pt, Inches from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() # Title title = doc.add_heading( '文献阅读报告' , level= 1 ) title.alignment = WD_ALIGN_PARAGRAPH.CENTER # Metadata section doc.add_heading( '基本信息' , level= 2 ) table = doc.add_table(rows= 4 , cols= 2 ) table.cell( 0 , 0 ).text = '标题' table.cell( 0 , 1 ).text = 'Distributed Optical Fiber Sensing Using BOTDR' table.cell( 1 , 0 ).text = '作者' table.cell( 1 , 1 ).text = 'Zhang, Y., et al.' table.cell( 2 , 0 ).text = '期刊' table.cell( 2 , 1 ).text = 'J. Lightwave Tech., 2023, 41(5), 1234-1245' table.cell( 3 , 0 ).text = 'DOI' table.cell( 3 , 1 ).text = '10.1109/JLT.2023.1234567' # Main sections doc.add_heading( '研究背景' , level= 2 ) doc.add_paragraph( '【来源: 论文第1节】分布式光纤传感技术...' ) doc.add_heading( '核心方法' , level= 2 ) doc.add_paragraph( '【来源: 论文图2及第3.1节】本文提出...' ) doc.add_paragraph( '[图2占位符: BOTDR系统示意图]' ) doc.add_heading( '实验结果' , level= 2 ) doc.add_paragraph( '【来源: 论文表1】在50km光纤上测试...' ) doc.add_heading( '个人评价' , level= 2 ) doc.add_paragraph( '【评价】优点: 空间分辨率达到0.5m；局限: 仅测试单模光纤...' ) doc.save( '文献报告_BOTDR_2023.docx' ) Create thesis defense PPT: # User: "用我的论文PDF生成答辩PPT，用学校模板template.pptx" # Skill workflow: from pptx import Presentation from pptx.util import Inches, Pt # 1. Clone template master slides template = Presentation( 'template.pptx' ) prs = Presentation() prs.slide_master = template.slide_master # Preserve school branding # 2. Extract paper structure # Reads paper.pdf → sections, figures, key results # 3. Generate slides with action titles slide = prs.slides.add_slide(prs.slide_layouts[ 1 ]) title = slide.shapes.title title.text = 'BOTDR空间分辨率提升至0.5m' # Conclusion, not topic # Add figure placeholder left = Inches( 1 ) top = Inches( 2 ) pic_placeholder = slide.shapes.add_textbox(left, top, Inches( 8 ), Inches( 4 )) pic_placeholder.text = '[插入论文图3: 分辨率对比实验结果]' # Add source annotation source = slide.shapes.add_textbox(Inches( 0.5 ), Inches( 6.5 ), Inches( 9 ), Inches( 0.3 )) source.text = '数据来源: 论文第4.2节, 表2' source.text_frame.paragraphs[ 0 ].font.size = Pt( 10 ) prs.save( '答辩PPT_初稿.pptx' ) # 4. Run overflow check (built-in script) # python references/thesis-defense-pptx/scripts/check_text_overflow.py 答辩PPT_初稿.pptx Edit existing PowerPoint: # User: "把第5页的标题改成中文，图表移到右边" from pptx import Presentation prs = Presentation( '答辩PPT_初稿.pptx' ) slide = prs.slides[ 4 ] # 0-indexed # Change title slide.shapes.title.text = '实验验证与结果分析' # Move figure to right half for shape in slide.shapes: if shape.has_text_frame and '[插入论文图' in shape.text: shape.left = Inches( 5 ) shape.top = Inches( 1.5 ) shape.width = Inches( 4 ) prs.save( '答辩PPT_修订.pptx' ) PPT Quality Standards The skill enforces these rules: Action titles : "温度测量误差降低60%" not "实验结果" One idea per slide : No bullet-point essays Figure-driven : Technical claims backed by charts/equations Scientific rigor : Axes labels, units, legends, data sources Academic aesthetics : White/neutral backgrounds, color for emphasis only Built-in Tools Under references/ : office-docx/ ├── ooxml_validator.py # Check DOCX against Office Open XML schema └── schemas/ # XSD schemas for validation office-pptx/ ├── pptx_structure.md # PPTX OOXML anatomy └── layout_examples/ # Common academic slide layouts thesis-defense-pptx/scripts/ ├── extract_thesis_context.py # Parse LaTeX/PDF for key content ├── clone_template.py # Preserve master slide styles ├── export_slides_png.py # Slide → PNG for review └── check_text_overflow.py # Detect text box overflow Skill 3: scientific-toolkit-skill Core Capabilities MATLAB/Octave: Signal/image processing, FFT, filtering, matrix operations Simulation, numerical methods Publication-quality figure export Python Scientific Stack: NumPy, SciPy : Numerical computing, optimization pandas : Data manipulation and analysis matplotlib, seaborn : Publication-grade plots scikit-learn : Machine learning (classification, regression, clustering, PCA) statsmodels : Statistical modeling, time series SymPy : Symbolic math, equation derivation QuTiP : Quantum optics simulations pymatgen : Materials science (crystal structures, band structure) TimesFM : Time series forecasting NetworkX : Graph and network analysis Astropy : Astronomical/optical imaging data Literature & Citations: Search: arXiv, PubMed, CrossRef, Semantic Scholar, OpenAlex DOI → BibTeX conversion, citation validation Usage Patterns MATLAB signal processing: % User: "帮我用MATLAB分析这个BOTDR信号，提取BGS并计算温度" % Skill generates: % === BOTDR Signal Analysis === % Parameters (用户需确认): fs = 1e9; % 采样率 [Hz] - 来源: 用户确认 fiber_length = 50e3; % 光纤长度 [m] - 来源: 用户确认 v_B0 = 10.86e9; % 参考Brillouin频移 [Hz] - 来源: 文献默认值 (SMF-28) C_T = 1.0e6; % 温度系数 [Hz/°C] - 来源: 文献默认值 % Load signal data = load('botdr_signal.mat'); raw_signal = data.raw; % [时间 x 频率扫描] % Extract BGS (Brillouin Gain Spectrum) [n_points, n_freq] = size(raw_signal); freq_axis = linspace(10.5e9, 11.5e9, n_freq); % 频率扫描范围 [Hz] BGS_peak_freq = zeros(n_points, 1); for i = 1:n_points [~, peak_idx] = max(raw_signal(i, :)); BGS_peak_freq(i) = freq_axis(peak_idx); end % Convert to temperature delta_v_B = BGS_peak_freq - v_B0; % 频移差 [Hz] temperature = delta_v_B / C_T; % 温度 [°C] % Calculate spatial resolution spatial_res = 0.5 * 3e8 / fs; % = c/(2*fs) [m] % Plot results figure('Position', [100, 100, 800, 600]); subplot(2,1,1); plot(1:n_points, BGS_peak_freq/1e9, 'LineWidth', 1.5); xlabel('测量点', 'FontSize', 12); ylabel('Brillouin频移 (GHz)', 'FontSize', 12); title('沿光纤的BGS频移分布', 'FontSize', 14); grid on; subplot(2,1,2); plot((1:n_points)*spatial_res/1000, temperature, 'LineWidth', 1.5); xlabel('距离 (km)', 'FontSize', 12); ylabel('温度 (°C)', 'FontSize', 12); title(sprintf('温度分布 (空间分辨率: %.2f m)', spatial_res), 'FontSize', 14); grid on; % Export figure for paper print('BGS_temperature_profile.png', '-dpng', '-r300'); % 【注释】未编造数据，所有物理量已标注来源 Python data analysis with scikit-learn: # User: "用机器学习分类这些光谱数据，特征在features.csv，标签在labels.csv" import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.metrics import classification_report, confusion_matrix import matplotlib.pyplot as plt import seaborn as sns # Load data features = pd.read_csv( 'features.csv' ) # 假设每行是一个光谱样本 labels = pd.read_csv( 'labels.csv' ).values.ravel() # Split dataset X_train, X_test, y_train, y_test = train_test_split( features, labels, test_size= 0.2 , random_state= 42 , stratify=labels ) # Preprocessing scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train)