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Pymoo Optimization Toolkit

Lets the agent set up and solve single- and multi-objective optimization problems with pymoo.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
14 files

What it does

Once installed, the agent knows how to frame an optimization problem (variables, objectives, constraints), pick a suitable evolutionary algorithm such as NSGA-II, NSGA-III or MOEA/D, run it with the pymoo library, and explain the results. It can also show trade-off solutions (a Pareto front) and help choose one using decision-making methods. Think of it as a guided expert for finding best-possible designs when several goals conflict.

Great for

  • Engineers or researchers tuning designs with competing goals such as cost versus performance
  • Students learning multi-objective optimization with ready-made ZDT and DTLZ benchmarks
  • Developers who want working pymoo code templates for custom problems

Skip it if

  • Simple one-variable problems a spreadsheet or basic script can solve, since the setup adds unnecessary complexity
  • Safety-critical engineering decisions taken without expert review, because evolutionary results are approximations with no guarantee of optimality
  • Environments where installing Python packages is not permitted, since the skill depends on pymoo 0.6.2 and its dependencies

Try asking

  • “Find the trade-off curve between cost and weight for my bridge design parameters using NSGA-II”
  • “Set up a many-objective problem with 5 goals and solve it with NSGA-III”
  • “Write a custom pymoo problem with two inequality constraints and solve it”
  • “Help me pick one solution from this Pareto front using pseudo-weights”
Step-by-step guide: how to use Pymoo Optimization ToolkitInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 14 original files, the MIT licence and an attribution note.

    pymoo.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip pymoo.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip pymoo.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • State your objectives, variable bounds and constraints clearly so the agent can write a correct problem definition
  • Run the bundled example scripts first to confirm pymoo works in your environment before adapting them
  • Check result.CV and feasibility before trusting selected solutions, since no feasible solution may exist

What's inside

  • references/algorithms.md
  • references/constraints_mcdm.md
  • references/lifecycle.md
  • references/operators.md
  • references/parallelization.md
  • references/problems.md
  • references/quick_start_workflows.md
  • references/visualization.md
  • scripts/custom_problem_example.py
  • scripts/decision_making_example.py
  • scripts/many_objective_example.py
  • scripts/multi_objective_example.py
  • scripts/single_objective_example.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.