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EmaRimoldi
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measurement-implementation

Automate ML experiment measurement pipelines with metrics, analytical references, and statistical tests.

Updated Mar 27, 2026
Search Tags:#metrics#statistics#reproducibility#machine-learning#hypothesis-testing#experimental-design
One-click install
npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill measurement-implementation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: measurement-implementation
Source: https://github.com/EmaRimoldi/Claude-scholar-extended/tree/main/skills/measurement-implementation
Command: npx skills add https://github.com/EmaRimoldi/Claude-scholar-extended --skill measurement-implementation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, torch, scipy.

What problem does it solve?

This Skill provides a production-ready framework to implement core measurements, analytical references, and rigorous testing for ML experiments, ensuring reproducible metrics and fair comparisons.

Core Features & Use Cases

  • Empirical metric implementations for classification, similarity, scaling, and information-theoretic measures.
  • Analytical reference modules (OLS, ridge, gradient descent, kernel regression, Bayes-optimal, NTK) to benchmark model behavior.
  • Comprehensive comparisons and statistical testing (bootstrap, permutation, multiple corrections) with robust numerical stability guidelines.

Quick Start

Load your experiment plan and hypotheses, then generate and validate metrics using the measurement-implementation workflow.

Frequently Asked Questions about measurement-implementation

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

FAQPage Schema
How do I implement reproducible metrics for machine learning experiments?▼

Reproducible metrics for machine learning experiments are implemented by automating measurement pipelines with numerical stability guidelines, analytical baselines, and rigorous significance testing across conditions and seeds.

What is the best way to run statistical testing across multiple experimental seeds?▼

Statistical testing across experimental seeds is handled using bootstrap, permutation, and multiple comparison corrections to ensure fair comparisons and robust significance evaluation for your machine learning results.

How do I benchmark model behavior against analytical references?▼

Benchmarking model behavior against analytical references utilizes modules for OLS, ridge, gradient descent, kernel regression, Bayes-optimal, and NTK baselines to evaluate empirical metric implementations.

Does this measurement framework support numerical stability for information-theoretic metrics?▼

Numerical stability for information-theoretic metrics is supported through robust computation guidelines and a clear MetricFactory API, ensuring stable calculations for classification, similarity, and scaling measures.

Can I use SciPy and PyTorch for hypothesis testing in my ML pipeline?▼

Hypothesis testing in your ML pipeline integrates SciPy, PyTorch, and NumPy to execute comprehensive comparisons, analytical references, and robust statistical tests for experimental design.

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