# pymoo

Use this skill to work through Python optimization problems, from a single objective to competing goals. It helps when you need to compare trade-offs or account for constraints.

- skill · `type=skill` · Claude Code · 125 installs
- Author: davila7
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/scientific/pymoo
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/pymoo

## What it does

The skill guides Claude Code through setting up optimization tasks with pymoo in Python. For a single objective, it covers options such as genetic algorithms, differential evolution, and PSO. For competing objectives, it discusses NSGA-II and MOEA/D, with results that include decision variables, objective values, and constraint violations when constraints are part of the problem.

For multi-objective tasks, pymoo can produce a Pareto front for comparing candidate solutions. The many-objective example uses NSGA-III and requires reference directions; those must be supplied when configuring the algorithm. The skill also explains how to define a custom problem with `ElementwiseProblem`, variable bounds, objectives, and constraints.

It covers continuous, discrete, and mixed-variable problems, plus ZDT and DTLZ benchmarks. Python and pymoo are needed. Without reference directions, the described NSGA-III setup cannot run as shown.

## Install

### Claude Code (native platform)

1. **claude-code-templates**
```bash
npx claude-code-templates@latest --skill scientific/pymoo
```

2. **Skills CLI (Vercel)**
```bash
npx skills add davila7/claude-code-templates --skill pymoo -a claude-code
```

3. **Manually: put the files in place**
- File: `~/.claude/skills/pymoo/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/pymoo/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/pymoo/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/pymoo/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install davila7/claude-code-templates pymoo
```

2. **Manually: put the files in place**
- File: `.github/skills/pymoo/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/davila7/claude-code-templates --path cli-tool/components/skills/scientific/pymoo
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/pymoo/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/scientific/pymoo

## When to use it
- optimize a function with one objective
- compare trade-offs across competing goals
- encode constraints in an optimization problem
- benchmark algorithms on ZDT or DTLZ
- plot a Pareto front

## Example prompts
- Help me define a Python optimization problem with two objectives and constraints.
- Choose a pymoo algorithm for five objectives and show how to provide its reference directions.
- Compare the Pareto solutions for the ZDT1 problem.

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## Other versions with the same name
- [pymoo · k-dense-ai](https://affpapa.org/en/nevibecode/skills/k-dense-ai-pymoo.md) — 1900 installs

From the same set: [davila7/claude-code-templates](https://affpapa.org/en/nevibecode/by/davila7/claude-code-templates.md) — 495 cards

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