automated-sweep

What problem does it solve?

This Skill automates systematic parameter sweeps and optimization for mathematical models to identify optimal parameter values, reducing guesswork and accelerating experimentation.

Core Features & Use Cases

• Parameter-space exploration: Define bounds for 1-3 key parameters and generate comprehensive grids or samples.
• Robust optimization: Compute a loss metric against historical or simulated data to identify stable optima.
• Visualization & reporting: Produce heatmaps and a JSON summary of best parameters for reproducibility.
• Use Case: Suppose you are tuning growth-rate, decay-factor, and elasticity in a population model; this Skill will systematically explore the space and return the best settings with visualizations.

Quick Start

Define 1-3 parameter ranges (e.g., growth_rate, decay_factor, elasticity) and a loss function. Run the sweep to produce a parameter heatmap and a best-parameter JSON. Inspect results in results/parameter_sweep/ and export the best values for documentation.