analyze-results

Analyze ML experiment result files in JSON/CSV to compute statistics and compare models.

Updated Apr 2, 2026

Search Tags:#insights #comparison #statistics #analyze #ml #experiments #results

Ready to run

npx skills add https://github.com/dz306271098/ARIS_for_Robotics --skill analyze-results-dz306271098

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/dz306271098/ARIS_for_Robotics/tree/main/skills/analyze-results
Command: npx skills add https://github.com/dz306271098/ARIS_for_Robotics --skill analyze-results-dz306271098

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze ML experiment results to compute statistics, generate comparison tables, and surface actionable insights for decision-making.

Core Features & Use Cases

Compute statistics from experiment results (e.g., means, std, p-values) and summarize performance across models and seeds.
Build comparison tables that highlight delta vs baseline and highlight trends, reproducibility, and outliers.
Generate structured insights with observations, interpretations, and implications to guide next steps.

Quick Start

Provide a concise results summary by running the analysis on your latest results directory.

Frequently Asked Questions about analyze-results

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

FAQPage Schema

How do I analyze ML experiment results from JSON and CSV files?▼

To analyze ML experiment results, the Skill parses JSON and CSV files from results or figures directories, aggregates metrics by model and configuration, and computes statistics like means and standard deviations to summarize performance.

Can I compare multiple ML models and compute statistics across different seeds?▼

Yes, you can compare ML models across multiple seeds. The Skill aggregates metrics by model and configuration, computes delta versus baseline, assesses reproducibility, and generates structured comparison tables highlighting trends and outliers.

What is the best way to generate insights from hyperparameter sweep results?▼

The best way to generate insights from hyperparameter sweeps is to parse result files, aggregate metrics by configuration, and produce structured narrative findings with observations, interpretations, and implications to guide next steps.

Does this ML results analysis tool work without external dependencies?▼

Yes, this ML results analysis tool works without external dependencies. It independently parses result files in JSON and CSV formats across your directories to compute statistics and surface actionable insights.

How do I assess reproducibility and outliers in machine learning experiments?▼

To assess reproducibility and outliers in machine learning experiments, the Skill computes statistics across multiple seeds, evaluates delta versus baseline, and builds comparison tables to highlight trends and anomalies.

What statistics can I compute from my machine learning experiment results?▼

You can compute statistics such as means, standard deviations, and p-values from your machine learning experiment results. The Skill summarizes performance across models and seeds to support decision-making.