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sensitivity-analysis

Quantify model output dependence on input parameters via OAT, Monte Carlo, and Sobol analyses.

1|Updated Apr 2, 2026
Search Tags:#robustness#monte-carlo#sensitivity-analysis#model-validation#parameter-perturbation#tornado-chart#sobol-indices
One-click install
npx skills add https://github.com/Best6668/AMIS --skill sensitivity-analysis-best6668
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sensitivity-analysis
Source: https://github.com/Best6668/AMIS/tree/main/skills/sensitivity-analysis
Command: npx skills add https://github.com/Best6668/AMIS --skill sensitivity-analysis-best6668

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Sensitivity analysis helps quantify how model outputs respond to changes in input parameters, enabling robust decision making in uncertain environments.

Core Features & Use Cases

  • One-at-a-time (OAT) sensitivity analysis to rank parameter influence.
  • Tornado charts and spider plots for intuitive visualization of parameter effects.
  • Monte Carlo simulations and optional Sobol indices to capture uncertainty and interactions.
  • Structured workflows to produce reproducible artifacts and reports for modeling, risk assessment, and publication.

Quick Start

Provide your model function and baseline parameter values, then run this skill to generate sensitivity insights and a formal report.

Frequently Asked Questions about sensitivity-analysis

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

FAQPage Schema
How do I quantify how my model outputs depend on input parameters?▼

Sensitivity analysis quantifies how model outputs depend on input parameters through one-at-a-time analysis, Tornado charts, Monte Carlo simulations, and Sobol indices to capture interactions and uncertainty.

What's the best way to rank parameter influence for model validation?▼

Ranking parameter influence for model validation is best done using one-at-a-time analysis and Tornado charts, which intuitively visualize parameter effects and generate ranked sensitivity insights.

How do I run Monte Carlo simulations to capture model uncertainty?▼

Running Monte Carlo simulations to capture model uncertainty involves providing your model function and baseline parameters to systematically perturb inputs and quantify output robustness.

Can I use Sobol indices to capture parameter interactions?▼

Yes, you can use optional Sobol indices to capture parameter interactions and uncertainty, providing deeper variance-based sensitivity insights alongside standard one-at-a-time analysis.

How do I generate reproducible sensitivity analysis reports for risk assessment?▼

Generating reproducible sensitivity analysis reports for risk assessment requires structured workflows that produce artifacts, parameter rankings, charts, and final recommendations detailing model robustness.

Do I need baseline parameter values to start sensitivity analysis?▼

Yes, you need baseline parameter values and your model function to start sensitivity analysis, enabling systematic perturbation and generation of sensitivity insights and a formal report.

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