analyze-results

Parse JSON/CSV ML experiment outputs into structured comparison summaries with statistics.

Updated Apr 18, 2026

Search Tags:#comparison #statistics #data-analysis #reproducibility #analyze #ml #experiments

Ready to run

npx skills add https://github.com/THUFanZd/Sewed_pipeline --skill analyze-results-thufanzd

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze ML experiment results by parsing JSON/CSV outputs and building structured, comparable summaries that reveal performance trends and statistically significant differences.

Core Features & Use Cases

Structured comparison: organize results by model type, hyperparameters, and data config.
Statistical summaries: compute mean, standard deviation, and delta vs baseline.
Insights & recommendations: translate findings into concrete next steps for model selection and experiments.

Quick Start

Run this analysis on your experiment results directory to generate a concise comparison report with insights.

Frequently Asked Questions about analyze-results

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

FAQPage Schema

How do I compare ML experiment results from multiple models?▼

To compare ML experiment results, this Skill parses JSON and CSV outputs to build structured, comparable summaries. It organizes performance trends by model type, hyperparameters, and data configurations to reveal statistically significant differences.

Can I generate reproducible statistics like mean and standard deviation for my experiments?▼

Yes, you can generate reproducible statistics by parsing your experiment outputs. The Skill computes mean +/- std, calculates delta versus baseline, and detects outliers to ensure your ML experiment analysis remains statistically sound and reproducible.

What is the best way to translate machine learning experiment data into actionable insights?▼

The best way to translate machine learning experiment data into actionable insights is by generating structured comparison reports. This Skill provides concise observations, interpretations, implications, and concrete next steps for model selection and future experiments.

Does this analysis tool support both JSON and CSV file formats for ML outputs?▼

Yes, the analysis tool supports both JSON and CSV file formats. It directly parses these output files from your experiment directory to automatically generate concise comparison reports with statistical summaries and baseline deltas.

How do I detect outliers in my hyperparameter tuning experiment data?▼

To detect outliers in your hyperparameter tuning data, this Skill applies statistical analysis to your parsed JSON or CSV experiment results. It identifies performance anomalies and calculates delta versus baseline to inform your model selection decisions.