# parameter-optimization

Builds design of experiments plans, ranks parameter sensitivity, and selects optimization strategies. Geared toward simulation calibration under tight run limits.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 83
- Author: HeshamFS
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/simulation-workflow/parameter-optimization/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/parameter-optimization

## What it does

This skill structures experiment design and calibration runs for simulation models. It automates sampling point generation, parameter ranking, and strategy selection when compute time is scarce. Supported sampling schemes include Latin Hypercube (`lhs`), quasi-random sequences, and full factorial grids.

Execution depends on Python 3.10+. No third-party packages are needed because the toolset relies entirely on the Python standard library. Four standalone utilities handle the steps: `scripts/doe_generator.py`, `scripts/sensitivity_summary.py`, `scripts/optimizer_selector.py`, and `scripts/surrogate_builder.py`. Each script outputs structured JSON data.

Optimizer selection matches the evaluation budget and parameter dimensions. For problems with 10 or fewer parameters and at most 100 simulation evaluations, it suggests Bayesian optimization. Systems reaching up to 20 dimensions route to CMA-ES. Higher dimensional tasks fall back to random search with screening.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill parameter-optimization -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill parameter-optimization -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill parameter-optimization -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install HeshamFS/materials-simulation-skills parameter-optimization
```

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

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill parameter-optimization -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/HeshamFS/materials-simulation-skills --path skills/simulation-workflow/parameter-optimization
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill parameter-optimization -a opencode
```

### 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/HeshamFS/materials-simulation-skills/tree/HEAD/skills/simulation-workflow/parameter-optimization/SKILL.md

## When to use it
- generate Latin Hypercube or quasi-random sampling plans
- rank parameter importance through sensitivity scores
- select an optimization algorithm based on problem dimension and budget
- fit a surrogate model to approximate expensive simulation runs

## Example prompts
- Calibrate thermal conductivity and diffusivity for my FEM simulation within 30 runs.
- Generate a Latin Hypercube sample plan for 3 parameters with a budget of 20 evaluations.
- Recommend an optimizer for a 4-dimensional problem with 50 runs and low noise.

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: e5aec3198bcf2b5a.

BEGIN THIRD-PARTY FILE e5aec3198bcf2b5a
````text
---
name: parameter-optimization
description: >
  Explore and optimize simulation parameters via design of experiments (DOE),
  sensitivity analysis, and optimizer selection — generate Latin Hypercube,
  quasi-random, or factorial sample plans, rank parameter influence with
  sensitivity scores, recommend Bayesian optimization, CMA-ES, or gradient-
  based methods based on dimension and budget, and fit surrogate models for
  expensive evaluations. Use when calibrating material properties against
  experimental data, planning a parameter sweep, performing uncertainty
  quantification, or choosing an optimization strategy for a simulation
  with a limited evaluation budget, even if the user only says "which
  parameters matter most" or "how do I calibrate my model."
allowed-tools: Read, Write, Grep, Glob
metadata:
  last_deterministic_eval: "2026-10-20"
  agent_evaluation_status: "not_revalidated"
  author: HeshamFS
  version: "1.2.3"
  security_tier: medium
  security_reviewed: true
  tested_with:
    - claude-code
  last_evaluated: "2026-06-24"
  eval_cases: 5
  last_reviewed: "2026-10-19"
  standards:
    - "Latin Hypercube Sampling (McKay, Beckman & Conover 1979)"
    - "Sobol (1967) low-discrepancy quasi-random sequences"
    - "Morris (1991) Elementary Effects screening method"
    - "Saltelli et al. (2008), Global Sensitivity Analysis: The Primer (Sobol indices / Saltelli estimator)"
    - "CMA-ES (Hansen & Ostermeier 2001)"
---

# Parameter Optimization

## Goal

Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.

## Requirements

- Python 3.10+
- No external dependencies (uses Python standard library only)

## Inputs to Gather

Before running any scripts, collect from the user:

| Input | Description | Example |
|-------|-------------|---------|
| Parameter bounds | Min/max for each parameter with units | `kappa: [0.1, 10.0] W/mK` |
| Evaluation budget | Max number of simulations allowed | `50 runs` |
| Noise level | Stochasticity of simulation outputs | `low`, `medium`, `high` |
| Constraints | Feasibility rules or forbidden regions | `kappa + mobility < 5` |

## Decision Guidance

### Choosing a DOE Method

```
Is dimension <= 3 AND full coverage needed?
├── YES → Use factorial
└── NO → Is sensitivity analysis the goal?
````
END THIRD-PARTY FILE e5aec3198bcf2b5a

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