derivative-free-optimization

Optimization without gradient information

509 stars

Best use case

derivative-free-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Optimization without gradient information

Teams using derivative-free-optimization should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/derivative-free-optimization/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/derivative-free-optimization/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/derivative-free-optimization/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How derivative-free-optimization Compares

Feature / Agent	derivative-free-optimization	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Optimization without gradient information

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Derivative-Free Optimization

## Purpose

Provides optimization capabilities for problems where gradient information is unavailable or unreliable.

## Capabilities

- Nelder-Mead simplex method
- Powell's method
- Surrogate-based optimization
- Bayesian optimization
- Pattern search methods
- Trust region methods

## Usage Guidelines

1. **Method Selection**: Choose based on problem characteristics
2. **Function Evaluations**: Minimize expensive function calls
3. **Surrogate Models**: Build and refine surrogate approximations
4. **Exploration-Exploitation**: Balance search strategies

## Tools/Libraries

- scipy.optimize
- Optuna
- GPyOpt

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