pymoo
This skill provides a Python interface to the pymoo library for solving multi-objective optimization problems. It implements algorithms like NSGA-II and NSGA-III, handles constraints, and includes benchmark problems for testing. The documentation offers clear workflows for single, multi, and many-objective optimization with practical code examples.
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 k-dense-ai-claude-scientific-skills-pymoo
Repository
Skill path: scientific-skills/pymoo
This skill provides a Python interface to the pymoo library for solving multi-objective optimization problems. It implements algorithms like NSGA-II and NSGA-III, handles constraints, and includes benchmark problems for testing. The documentation offers clear workflows for single, multi, and many-objective optimization with practical code examples.
Open repositoryBest for
Primary workflow: Design Product.
Technical facets: Data / AI, Designer, Testing.
Target audience: AI/ML teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: K-Dense-AI.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install pymoo into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/K-Dense-AI/claude-scientific-skills before adding pymoo to shared team environments
- Use pymoo for ai/ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.