gpd-compare-experiment

Name: gpd-compare-experiment
Availability: InStock
Author: CharlieGreenman

Quantify agreement between theoretical predictions and experimental data with statistical metrics.

1|Updated Mar 29, 2026

Search Tags:#comparison #uncertainty #unit-conversion #statistical-testing #discrepancy-analysis #theory-data

Ready to run

npx skills add https://github.com/CharlieGreenman/roomtemp-superconductor-gpd --skill gpd-compare-experiment

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gpd-compare-experiment
Source: https://github.com/CharlieGreenman/roomtemp-superconductor-gpd/tree/main/.agents/skills/gpd-compare-experiment
Command: npx skills add https://github.com/CharlieGreenman/roomtemp-superconductor-gpd --skill gpd-compare-experiment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematically quantify the agreement between theoretical predictions and experimental or observational data, including robust handling of units, uncertainties, and discrepancy analysis to guide scientific conclusions.

Core Features & Use Cases

Systematic theory-data comparisons with quantified metrics (chi-squared, p-values, pulls) to determine compatibility.
Load predictions from workspace artifacts and data sources, perform unit/convention checks, and generate standardized comparison reports and figures.
Use cases include validating models against experiments, benchmarking between predictions, and producing publication-ready summaries for papers or debugging.

Quick Start

Ask me to compare a specific theoretical prediction with a dataset by providing the target name or data path.

Frequently Asked Questions about gpd-compare-experiment

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

FAQPage Schema

How do I quantify theory-data agreement and calculate discrepancy metrics?▼

To quantify theory-data agreement, this Skill computes statistical metrics like chi-squared, p-values, and pulls. It systematically identifies and quantifies compatibility between theoretical predictions and experimental or observational data.

How do I propagate uncertainties when comparing theoretical predictions to experimental data?▼

Uncertainty propagation is handled natively during theory-data comparisons. The Skill propagates measurement and prediction uncertainties to generate accurate statistical tests and standardized discrepancy analysis reports.

How do I perform unit conversion and convention checks for scientific data comparisons?▼

Unit conversion and convention checks are enforced automatically when loading predictions and datasets. This ensures accurate theory-data comparisons by standardizing units before calculating quantified agreement metrics.

Can I load theoretical predictions from workspace artifacts to validate models against experiments?▼

Yes, you can load theoretical predictions from workspace artifacts and data sources. The Skill validates models against experiments by comparing these loaded predictions with observational datasets.

What is the best way to generate publication-ready summaries for theory-data discrepancy analysis?▼

The best way to generate publication-ready summaries is using this Skill's standardized reporting. It produces quantified metrics and figures directly from your theory-data discrepancy analysis for papers or debugging.