Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.
Turn papers and thesis materials into Chinese-first Word reports and PowerPoint presentations. Create editable files, preserve source evidence, match supplied templates, add speaker notes, and check layouts for overflow, overlap, and readability.
Extract text, tables, and images from PDFs, combine or split documents, rotate pages, fill forms, and create new files. It also covers password protection and OCR, which recognizes text in scanned pages for searching and reuse.
Create statistical charts from tabular data to explore distributions, relationships, and group differences. This skill guides plot selection, multi-panel layouts, and styling, including box plots, heatmaps, and charts with estimates and uncertainty bands.
Process astronomical data in Python with Astropy. This skill covers celestial coordinate, unit, and time conversions; reading and editing FITS astronomy files; matching object catalogs; and calculating cosmic distances using cosmological models.
Explore design options in Python when goals such as cost and performance compete. This skill guides optimization, constraint handling, and visualization of trade-offs, then helps compare candidate solutions using stated preferences.
Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.
Simulate quantum systems in Python with QuTiP, including open systems that interact with their surroundings. Calculate time evolution, energy loss, and entanglement, then visualize states and results for physics research or educational experiments.
Use SymPy in Python to solve equations, simplify formulas, calculate derivatives and integrals, and work with matrices. Keep exact results such as fractions and square roots, then turn expressions into executable code or LaTeX.
Create charts in Python with detailed control over colors, axes, labels, legends, and layouts. Use it to build scientific figures, arrange multiple plots, and export results as PNG, PDF, or SVG files for papers and presentations.
Prepare scientific figures for journal submission with consistent panel layouts, error bars showing uncertainty, statistical significance labels, and accessible colors. Use matplotlib, seaborn, or plotly to adjust figure dimensions and export publication formats.
Search ten academic databases for papers, preprints, author publications, and citation links. Look up specific papers by DOI, a paper identifier, and find abstracts, available full text, or openly accessible PDF links.
Search multiple academic databases, screen studies against documented criteria, and compare findings by theme. Produce literature reviews in Markdown and PDF with study quality assessments, verified citation details, and diagrams explaining the review process or findings.
Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.
Create and edit spreadsheet files, clean messy tables, and convert tabular formats. Add formulas, formatting, and charts while following existing template styles, then recalculate formulas and check for errors before delivering the spreadsheet.
Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.
Model queues, production lines, logistics, and other systems that share limited resources in Python. Use discrete-event simulation, which advances through individual events, to measure waiting times, resource use, and throughput when comparing staffing or equipment levels.
Draft and revise research papers, thesis sections, and responses to reviewers, primarily in Chinese or in English on request. Organize arguments around supplied evidence while preserving formulas, citations, terminology, and measured results.
Forecast sales, sensor readings, or other time series without training a custom model. Using Google's TimesFM, this skill produces forecasts and uncertainty ranges from CSV files or arrays, supports batch processing, and checks local computing resources before use.
Analyze relationships between people, objects, or papers as networks in Python. Use NetworkX to find shortest paths, identify important nodes and communities, visualize connections, exchange graph data, and generate sample networks for testing.
Work with crystal structures in Python: convert files, analyze symmetry, assess material stability, and examine electronic properties. Retrieve Materials Project data and prepare input files for materials calculations, with scripts for common analysis tasks.
Build Python machine learning workflows for classification, numerical prediction, and grouping similar records. Use scikit-learn guidance and examples to prepare data, compare algorithms, tune model settings, and evaluate results with repeatable processing steps.
Find academic papers through Google Scholar and PubMed, retrieve publication details from identifiers, and build BibTeX reference files. Check author and publication information, detect duplicates, and standardize formatting before using references in a manuscript.
Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.
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