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MichaelsEngineering
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gpd-compare-results

Compare two result sources and emit structured comparison verdicts.

Updated Mar 15, 2026
Search Tags:#automation#workflow#analytics#comparison#results#baselines#gpd
One-click install
npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-compare-results-michaelsengineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpd-compare-results
Source: https://github.com/MichaelsEngineering/get-physics-done-test/tree/main/.agents/skills/gpd-compare-results
Command: npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-compare-results-michaelsengineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Compare internal results in a machine-readable way: analytics vs numerics, method A vs method B, benchmark vs produced quantity, expected vs observed behavior, or baseline vs modified run.

Core Features & Use Cases

  • Decisive, machine-readable verdicts for result comparisons.
  • Automated generation of comparison artifacts with thresholds and follow-up actions.
  • Use Case: Validating a model against a baseline to produce a formal comparison artifact.

Quick Start

Run the compare-results workflow to generate a machine-readable comparison artifact and verdict.

Frequently Asked Questions about gpd-compare-results

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

FAQPage Schema
How do I compare analytics results against a baseline in a machine-readable format?▼

To compare analytics against a baseline, the workflow identifies Source A and Source B, verifies shared parameters and conventions, applies a metric threshold, and emits a structured artifact with comparison verdicts and routing guidance.

What is a machine-readable comparison artifact for validating model results?▼

A machine-readable comparison artifact is a structured output containing comparison verdicts, sources, and routing guidance, generated after verifying that two sides measure the same observable under identical conventions against a threshold.

How do I automate result comparisons between method A and method B?▼

You automate result comparisons by specifying the two sides, shared parameters, metric, and threshold, allowing the workflow to verify measurement conventions and compute decisive verdicts automatically.

Can I use this comparison workflow for benchmark versus produced quantity validation?▼

Yes, you can validate a benchmark against a produced quantity by specifying them as Source A and Source B, verifying they measure the same observable, and generating a formal comparison artifact with decisive outcomes.

What happens if two sides of a comparison do not measure the same observable?▼

The workflow verifies that both sides measure the same observable under the same conventions before computing the verdict, ensuring mismatched observables are identified rather than producing invalid comparison artifacts.

Does the comparison workflow require specifying a threshold to generate a verdict?▼

Yes, you must specify a shared threshold alongside the metric and parameters, because the workflow uses the threshold to verify both sides and compute decisive comparison verdicts in the output artifact.

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