analyze-results

Name: analyze-results
Availability: InStock
Author: dogekiki

Parse JSON and CSV experiment files to compute performance statistics and comparative metrics.

1|Updated Jul 21, 2026

Search Tags:#data visualization #analysis #machine learning #reporting #statistics #experimentation

Ready to run

npx skills add https://github.com/dogekiki/SP-test --skill analyze-results-dogekiki

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the challenge of interpreting complex ML experiment outputs by automating the tedious process of data aggregation, statistical validation, and comparative analysis.

Core Features & Use Cases

Automated Comparison: Instantly generate tables comparing model performance across different hyperparameters or datasets.
Statistical Rigor: Automatically compute means, standard deviations, and identify trends or outliers in experimental results.
Use Case: When you have multiple JSON result files from a hyperparameter sweep, use this skill to generate a summary table and identify which configuration yielded the best performance improvement over the baseline.

Quick Start

Use the analyze-results skill to process the experiment data located in the results directory and generate a comparison report.

Frequently Asked Questions about analyze-results

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

FAQPage Schema

How do I analyze machine learning experiment results from JSON files?▼

To analyze machine learning experiment results from JSON files, you can parse and aggregate structured data outputs to automatically compute performance statistics and comparative metrics across different experimental configurations. This facilitates the systematic interpretation of model behavior.

What is the best way to compare model performance across a hyperparameter sweep?▼

The best way to compare model performance across a hyperparameter sweep is to aggregate multiple structured result files to generate comparative summary tables. This approach automatically identifies which configuration yielded the best performance improvement over the baseline.

How do I compute statistics for machine learning experiments using CSV output files?▼

You can compute statistics for machine learning experiments using CSV output files by parsing the structured data to calculate means, standard deviations, and comparative performance metrics. This systematic evaluation identifies trends and outliers across various experimental configurations.

Does this data analysis approach require file system access to locate experiment outputs?▼

Yes, this data analysis approach requires file system access to locate and aggregate JSON or CSV output files for systematic evaluation. It needs this access to parse structured data files and compute performance statistics across various experimental configurations.

Can I automatically generate a comparison report for raw experiment data?▼

Yes, you can automatically generate a comparison report for raw experiment data by parsing structured files to compute performance statistics. This turns raw experiment data into actionable insights by automating data aggregation, statistical validation, and comparative analysis.