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automated-sweep

Automate parameter sweeps and optimization for mathematical models.

Updated Jan 26, 2026
Search Tags:#optimization#robustness#modeling#parallel-processing#heatmap#parameter-sweep#grid-search
One-click install
npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill automated-sweep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: automated-sweep
Source: https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000/tree/main/.github/skills/automated-sweep
Command: npx skills add https://github.com/SPIRAL-EDWIN/MCM-ICM-2601000 --skill automated-sweep

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about automated-sweep

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate a parameter sweep to find optimal values for a mathematical model?▼

An automated parameter sweep systematically explores a defined parameter space for 1-3 variables, computing a loss metric against data to identify optimal values and generate heatmaps.

What is parameter sensitivity analysis and when do I need a grid search for model optimization?▼

Parameter sensitivity analysis evaluates how model outputs change with varying inputs. Use grid search when tuning 1-3 parameters like growth or decay factors to ensure robust model evaluation.

Can I run parallel processing for parameter sweeps across multiple model variables?▼

Yes, the parameter sweep supports parallel computation to efficiently explore 1-3 key parameters, applying robust evaluation and data validation across the defined parameter space.

How do I visualize parameter optimization results to identify the best performing settings?▼

Parameter optimization results are visualized using heatmaps that map parameter combinations to loss metrics. A best-parameter JSON file is also generated for reproducible documentation.

What's the best way to tune growth, decay, and elasticity parameters in a population model?▼

Define bounds for growth, decay, and elasticity, then run a systematic parameter sweep. The tool computes a loss metric against historical data to identify stable optima and produces visualizations.

What are the limitations of using a grid search for parameter optimization?▼

This parameter sweep is limited to 1-3 key parameters. Bounds justification and data validation are required, meaning higher-dimensional parameter spaces may not be suitable for this specific approach.

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