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gpd-error-propagation

Propagate uncertainties through computational derivation chains and generate error budgets.

1|Updated Mar 29, 2026
Search Tags:#error analysis#error budget#scientific research#uncertainty propagation#computational workflow
One-click install
npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-error-propagation-chargrnmn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpd-error-propagation
Source: https://github.com/CharGrnmn/roomtemp-superconductor-gpd/tree/main/.agents/skills/gpd-error-propagation
Command: npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-error-propagation-chargrnmn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, sympy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of tracking and managing uncertainty propagation in complex computational workflows, ensuring accurate error analysis and improved precision in scientific research.

Core Features & Use Cases

  • Uncertainty Propagation: Traces how uncertainties flow through a chain of calculations, providing a comprehensive error budget.
  • Dependency Tree Analysis: Builds a dependency tree from inputs to the target quantity, identifying all intermediate steps.
  • Error Source Identification: Catalogs uncertainty sources at each step, including input parameters, approximations, and numerical computations.
  • Error Budget Generation: Produces a detailed error budget table, highlighting dominant error sources and improvement potential.
  • Use Case: For a physics simulation, this Skill can trace the propagation of uncertainties from input parameters through various computational steps, ultimately providing a reliable error estimate for the final result.

Quick Start

Run the gpd-error-propagation skill with the target quantity 'temperature' and phase range '1:10'.

Frequently Asked Questions about gpd-error-propagation

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

FAQPage Schema
How do I propagate uncertainties through a chain of scientific calculations?▼

To propagate uncertainties through computational derivation chains, you can use Python scripting with numerical analysis libraries to trace error flow from input parameters to the final target quantity.

What is an error budget and how is it generated for a physics simulation?▼

An error budget catalogs uncertainty sources at each computational step, identifying dominant errors and improvement potential. It is generated by building a dependency tree from inputs to the final target quantity.

How do I identify dominant error sources in my computational workflow?▼

You identify dominant error sources by cataloging uncertainties from input parameters, approximations, and numerical computations at each step, then generating a detailed error budget table to highlight the largest contributors.

Do I need Python and specific libraries to perform systematic uncertainty propagation?▼

Yes, systematic uncertainty propagation requires Python for scripting and various computational libraries including numpy, scipy, and sympy to perform the numerical analysis and dependency tree building.

Can I trace uncertainties for a specific target variable and calculation phase range?▼

Yes, you can trace uncertainties for a specific target variable like 'temperature' by specifying the target quantity and defining a phase range such as '1:10' to scope the computational derivation chain analysis.

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