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, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.
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name: scientific-toolkit-skill
description: 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, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.
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# Scientific Toolkit Skill
## Scope
Use this skill for科研计算 and software-assisted research:
- MATLAB/Octave scripts, debugging, refactoring, signal/image processing, FFT, filtering, matrix computation, simulation, and figure export.
- Python scientific workflows with NumPy, SciPy, pandas, matplotlib, seaborn, scikit-learn, statsmodels, SymPy, and related tools.
- Statistics, exploratory data analysis, sensor/time-series forecasting, optimization, discrete-event simulation, quantum optics/open quantum systems, materials data, and graph/network analysis.
- Literature lookup, citation metadata, BibTeX, and reference verification when it supports coding or research analysis.
Use `research-writing-skill` for manuscript prose. Use `office-academic-skill` for Word/PPT deliverables.
## Domain Defaults
The user's field is光电信息科学与工程. Prefer examples and checks relevant to:
- Optics, optoelectronics, optical communication, optical sensing, fiber sensing, BOTDR/BOTDA, BGS, SPM, dispersion, noise, and deconvolution.
- Signal processing, image processing, spectroscopy, detector data, sensor time series, calibration, and uncertainty.
- MATLAB simulation and reproducible figure generation for论文/答辩.
Do not fabricate physical parameters, material constants, software menu operations, experimental results, or paper conclusions. When uncertain, ask for the source file or mark the assumption.
## General Workflow
1. Read the provided code, data, README, docs, and project instructions before changing anything.
2. Identify variables, dimensions, units, input/output paths, random seeds, and expected figures.
3. Make small, verifiable changes and avoid unrelated refactors.
4. Prefer mature libraries over hand-rolled numerical methods.
5. Run a script-level or test-level verification when possible.
6. Report environment, commands, output paths, generated figures, and known limitations.
## MATLAB And Figures
- Preserve the original code structure when possible.
- Add concise comments for physical meaning, units, assumptions, or formula sources.
- Centralize key parameters and avoid hardcoded absolute paths.
- Add `rng` for stochastic simulations when reproducibility matters.
- For publication figures, export both high-resolution `.png` and vector `.svg` when feasible.
- Check axes, units, legends, sampling rate, line width, font, color, and image resolution.
For MATLAB/Octave details, use `references/scientific-skills/matlab/SKILL.md`.
## Python Scientific Modules
Load only the relevant bundled reference:
- Plotting and publication figures: `matplotlib`, `seaborn`, `scientific-visualization`.
- Statistics and time series: `statistical-analysis`, `statsmodels`, `timesfm-forecasting`.
- Machine learning: `scikit-learn`.
- Symbolic math and formulas: `sympy`.
- Exploratory data analysis: `exploratory-data-analysis`.
- Optimization: `pymoo`.
- Simulation: `simpy`.
- Quantum optics/open quantum systems: `qutip`.
- Materials/crystal/band/DOS workflows: `pymatgen`.
- Graphs/networks/citation graphs: `networkx`.
- FITS or astronomical/optical imaging style data: `astropy`.
- Spreadsheet/PDF utilities: `xlsx`, `pdf`.
- Literature/citation support: `paper-lookup`, `citation-management`, `literature-review`.
Some bundled references mention optional installs such as `uv pip install ...` or optional API keys for higher rate limits. Do not install packages, use cloud APIs, or send user data to external services unless the current task requires it and the user agrees.
## Safety Rules
- Never expose or commit API keys, tokens, private data, or unpublished paper content.
- Do not overwrite original data, code, Word/PPT, or figures. Write versioned outputs.
- Do not delete or recursively clean user files without explicit confirmation.
- For external lookups, prefer open APIs and official documentation; clearly distinguish live lookup results from local inference.
## Verification
For code:
- Run the relevant script or a minimal example.
- Check generated files exist and are readable.
- Inspect plots for axes, units, legends, and plausible dimensions.
For research analysis:
- State software versions when known.
- List input files and commands.
- Mark assumptions and uncertain parameters.
When Claude will use it
Use when the user asks for MATLAB code, scientific Python, data analysis, plots, simulations, formulas, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.
---
name: office-academic-skill
description: 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 produce Chinese academic presentation/report deliverables. Preserve English paper titles, formulas, variable names, software commands, and references.
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name: research-writing-skill
description: 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 titles, terms, citations, and measured results.
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name: matlab
description: 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 MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
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name: pdf
description: 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 or asks to produce one, use this skill.
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