analyze-results

Parse JSON/CSV ML results to compute statistics and generate comparison tables.

1|Updated Mar 24, 2026

Search Tags:#insights #comparison #statistics #reproducibility #analyze #ml-experiments

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze ML experiment results, compute statistics, generate comparison tables and insights to help researchers interpret data quickly and reliably.

Core Features & Use Cases

Locate & parse results: Find relevant result files in common directories and parse JSON/CSV outputs.
Build comparison tables: Organize results by model type, hyperparameters, and seeds to reveal performance trends.
Statistical analysis & insights: Compute mean, std, delta vs baseline, flag outliers, and produce actionable interpretations.
Use Case: When evaluating multiple experiments, automatically summarize findings and propose next steps.

Quick Start

Analyze the latest experiment results directory to generate a summary table and key insights.

Frequently Asked Questions about analyze-results

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

FAQPage Schema

How do I generate comparison tables from ML experiment results?▼

To generate comparison tables from ML experiment results, the Skill parses JSON and CSV output files to organize metrics by model type, hyperparameters, and seeds. It then computes statistics like mean and standard deviation to reveal performance trends.

What is the best way to calculate delta vs baseline for multiple model variants?▼

Calculating delta vs baseline for model variants involves extracting result metrics from experiment directories and comparing them against a reference run. This process produces structured reports highlighting performance differences and flagging outliers.

Can I use this to summarize hyperparameter tuning runs across different seeds?▼

Yes, you can summarize hyperparameter tuning runs across different seeds. The Skill computes mean and standard deviation across multiple seeds, identifies outliers, and generates reproducible summaries to help you interpret the stability of your models.

Do I need a specific framework to parse JSON and CSV files for machine learning statistics?▼

No specific framework is required to parse JSON and CSV files for machine learning statistics. The Skill locates relevant result files in common directories and processes them independently to produce actionable insights and trend analysis.

How do I automatically flag outliers in reproducible ML experiment summaries?▼

To automatically flag outliers in reproducible ML experiment summaries, the tool applies statistical analysis to the parsed results. It evaluates deviations in performance metrics across runs and marks anomalous data points in the final structured report.