Updated 5mo ago
by zLanqing
codex-claude-academic-skills
本仓库包含三个面向学术科研人员的Skills,覆盖从文献阅读、论文写作到科学计算的完整研究工作流。office-academic-skill 负责论文阅读报告与学术 PPT/Word 文档生成;research-writing-skill 提供论文写作、润色与审稿回复辅助;scientific-toolkit-skill 整合 MATLAB/Python 科学计算与期刊级图表生成。三个 Skill 专为中文科研用户设计,可在 Claude Code 和 codex 中一键调用。
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- 24 skills (24 skills found in its SKILL.md files)
- 259 forks (259 forks)
- 5mo ago last update (Last updated 5mo ago)
Install
Skills CLI
npx skills add zLanqing/codex-claude-academic-skills
Asks which skills and agents to install. Just one: npx skills add zLanqing/codex-claude-academic-skills --skill office-academic-skill — add -g for every project.
Claude Code
git clone https://github.com/zLanqing/codex-claude-academic-skillscp -r "codex-claude-academic-skills/office-academic-skill" ~/.claude/skills/
Copies the office-academic-skill skill into ~/.claude/skills/ for every project (use .claude/skills/ for one project). Repeat for the other skills listed below.
Plugin
/plugin marketplace add zLanqing/codex-claude-academic-skills
Run inside Claude Code if the repository ships a plugin marketplace, then pick plugins from /plugin.
Skills can run code on your machine — read the SKILL.md first.
What it does
In the author's own words, from each skill's SKILL.md.
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office-academic-skill
Chinese-first academic Word and PowerPoint workflow for paper reading reports, thesis or group-meeting PPTs, editable DOCX/PPTX generation, Office file inspection, template matching, speaker notes, and layout quality checks. Use when the user asks to read papers into Word reports, create or polish PPT/PPTX, convert paper/thesis materials into slides, edit DOCX/PPTX, inspect Office files, or produ…
office-academic-skill/SKILL.md on GitHub (opens in a new tab) -
research-writing-skill
Chinese-first research paper writing, revision, polishing, section drafting, rebuttal, peer-review response, thesis prose improvement, and manuscript argument planning. Use when the user asks to write or revise论文正文, abstracts, introductions, methods, results, discussion, conclusions, related work, responses to reviewers, LaTeX/Overleaf text, or academic prose. Preserve formulas, English paper tit…
research-writing-skill/SKILL.md on GitHub (opens in a new tab) -
scientific-toolkit-skill
Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation, optimization, publication figures, sensor/time-series data, citation lookup, and common scientific libraries. Use when the user asks for MATLAB code, scientific Python, data analysis, plots, simulations, formulas…
scientific-toolkit-skill/SKILL.md on GitHub (opens in a new tab) -
astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmologi…
scientific-toolkit-skill/references/scientific-skills/astropy/SKILL.md on GitHub (opens in a new tab) -
citation-management
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
scientific-toolkit-skill/references/scientific-skills/citation-management/SKILL.md on GitHub (opens in a new tab) -
exploratory-data-analysis
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Cover…
scientific-toolkit-skill/references/scientific-skills/exploratory-data-analysis/SKILL.md on GitHub (opens in a new tab) -
literature-review
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs wi…
scientific-toolkit-skill/references/scientific-skills/literature-review/SKILL.md on GitHub (opens in a new tab) -
matlab
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between…
scientific-toolkit-skill/references/scientific-skills/matlab/SKILL.md on GitHub (opens in a new tab) -
matplotlib
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
scientific-toolkit-skill/references/scientific-skills/matplotlib/SKILL.md on GitHub (opens in a new tab) -
networkx
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological ne…
scientific-toolkit-skill/references/scientific-skills/networkx/SKILL.md on GitHub (opens in a new tab) -
paper-lookup
Search 10 academic paper databases via REST APIs for research papers, preprints, and scholarly articles. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID lookups, abstracts, full text, open access, preprints, citation graphs, author search, or any scholarly literature query. Triggers…
scientific-toolkit-skill/references/scientific-skills/paper-lookup/SKILL.md on GitHub (opens in a new tab) -
pdf
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file o…
scientific-toolkit-skill/references/scientific-skills/pdf/SKILL.md on GitHub (opens in a new tab) -
pymatgen
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
scientific-toolkit-skill/references/scientific-skills/pymatgen/SKILL.md on GitHub (opens in a new tab) -
pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
scientific-toolkit-skill/references/scientific-skills/pymoo/SKILL.md on GitHub (opens in a new tab) -
qutip
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
scientific-toolkit-skill/references/scientific-skills/qutip/SKILL.md on GitHub (opens in a new tab) -
scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
scientific-toolkit-skill/references/scientific-skills/scientific-visualization/SKILL.md on GitHub (opens in a new tab) -
scikit-learn
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
scientific-toolkit-skill/references/scientific-skills/scikit-learn/SKILL.md on GitHub (opens in a new tab) -
seaborn
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
scientific-toolkit-skill/references/scientific-skills/seaborn/SKILL.md on GitHub (opens in a new tab) -
simpy
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
scientific-toolkit-skill/references/scientific-skills/simpy/SKILL.md on GitHub (opens in a new tab) -
statistical-analysis
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
scientific-toolkit-skill/references/scientific-skills/statistical-analysis/SKILL.md on GitHub (opens in a new tab) -
statsmodels
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
scientific-toolkit-skill/references/scientific-skills/statsmodels/SKILL.md on GitHub (opens in a new tab) -
sympy
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable co…
scientific-toolkit-skill/references/scientific-skills/sympy/SKILL.md on GitHub (opens in a new tab) -
timesfm-forecasting
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
scientific-toolkit-skill/references/scientific-skills/timesfm-forecasting/SKILL.md on GitHub (opens in a new tab) -
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especia…
scientific-toolkit-skill/references/scientific-skills/xlsx/SKILL.md on GitHub (opens in a new tab)
No skill matches that filter.
Review before you install
A skill is a set of instructions and scripts your agent will follow, and it can run code on your machine. Read the SKILL.md and any scripts first — this listing is not a security review.