analyze-results

Name: analyze-results
Availability: InStock
Author: lightrain-a

Parse JSON/CSV ML experiment outputs into comparable statistics and numbered findings.

Updated May 20, 2026

Search Tags:#data parsing #statistical reporting #baseline comparison #results analysis #ml experiments #hyperparameter sweeps #insights generation

Ready to run

npx skills add https://github.com/lightrain-a/medtrace-aris --skill analyze-results-lightrain-a

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/lightrain-a/medtrace-aris/tree/main/.vendor/aris/skills/analyze-results
Command: npx skills add https://github.com/lightrain-a/medtrace-aris --skill analyze-results-lightrain-a

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the guesswork of interpreting machine learning experiment outputs by turning raw result files into comparable statistics and actionable findings.

Core Features & Use Cases

Result discovery & parsing: Finds JSON/CSV outputs across common directories and converts them into structured tables.
Baseline-aware comparisons: Organizes independent/dependent variables and computes deltas versus a baseline for quick performance interpretation.
Statistical insight generation: Reports mean±std across seeds, identifies trends in sweeps, flags outliers, and drafts follow-up experiments.

Quick Start

Ask the skill to analyze your experiment results by providing the path or description of the folder that contains your JSON/CSV outputs.

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 and CSV outputs?▼

To analyze machine learning experiment results, you parse JSON and CSV output files into structured tables, compute baseline deltas for performance comparison, and summarize mean±std across seeds to generate actionable findings.

What's the best way to compare model configurations across multi-seed hyperparameter sweeps?▼

The best way to compare model configurations across hyperparameter sweeps is to compute baseline deltas and summarize mean±std metrics across multiple seeds, which reveals true performance trends and flags outliers.

How does statistical reporting work for ml experiments with multiple random seeds?▼

Statistical reporting for ml experiments aggregates metrics across random seeds by calculating the mean and standard deviation, providing a reliable baseline comparison that captures variance and identifies true performance trends.

Can I generate follow-up experiment suggestions from raw results analysis?▼

Yes, results analysis can generate follow-up experiment suggestions by evaluating parameter sweep trends and baseline comparisons from your parsed outputs, drafting actionable next steps to optimize model configurations.

Do I need a specific folder structure to parse experiment result files?▼

No specific folder structure is strictly required, as the parsing mechanism discovers JSON and CSV outputs across common project directories, automatically converting them into structured tables for baseline-aware comparison.