Scientific Toolkit Skill
Research computing toolkit for optoelectronic information science and engineering...
Install
npx skills add zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill -g -a claude-code -y- Repository
- zLanqing/codex-claude-academic-skills5.5k
- Category
- Data and ML
- Updated
- 6mo ago
- License
- MIT
- Context cost
- Lightabout 1,060 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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
- Read the provided code, data, README, docs, and project instructions before changing anything.
- Identify variables, dimensions, units, input/output paths, random seeds, and expected figures.
- Make small, verifiable changes and avoid unrelated refactors.
- Prefer mature libraries over hand-rolled numerical methods.
- Run a script-level or test-level verification when possible.
- Report environment, commands, output paths, generated figures, and known limitations.
