sensitivity-analysis-uq

Global sensitivity analysis methods

509 stars

Best use case

sensitivity-analysis-uq is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Global sensitivity analysis methods

Teams using sensitivity-analysis-uq 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/sensitivity-analysis-uq/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/sensitivity-analysis-uq/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/sensitivity-analysis-uq/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How sensitivity-analysis-uq Compares

Feature / Agentsensitivity-analysis-uqStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Global sensitivity analysis methods

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

# Sensitivity Analysis (UQ)

## Purpose

Provides global sensitivity analysis methods for understanding model behavior and input importance.

## Capabilities

- Sobol indices computation
- Morris screening method
- FAST (Fourier amplitude sensitivity test)
- Correlation-based sensitivity
- Derivative-based sensitivity (DGSM)
- Variance-based decomposition

## Usage Guidelines

1. **Method Selection**: Choose based on computational budget
2. **Input Ranges**: Define appropriate input ranges
3. **Sample Size**: Ensure sufficient samples for convergence
4. **Interpretation**: Correctly interpret sensitivity indices

## Tools/Libraries

- SALib
- OpenTURNS
- UQLab

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