pycse
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install jkitchin-pycse-pycse
Repository
Skill path: src/pycse
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
Open repositoryBest for
Primary workflow: Design Product.
Technical facets: Full Stack, Data / AI, Designer.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: jkitchin.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install pycse into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/jkitchin/pycse before adding pycse to shared team environments
- Use pycse for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.