analyze-results

Parse ML experiment results files and summarize performance differences.

Updated Apr 1, 2026

Search Tags:#comparison #statistics #analyze #ml #data-processing #experiments #results

Ready to run

npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill analyze-results-wenwen555

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/Wenwen555/ARIS-LVLM/tree/main/skills/analyze-results
Command: npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill analyze-results-wenwen555

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing ML experiment results often requires manually collating metrics from diverse sources and visualizing trends. This Skill automates parsing results, computing statistics, and generating structured insights to accelerate interpretation.

Core Features & Use Cases

Automated Results Parsing: collects metrics from CSV/JSON logs and dashboard exports into a unified view.
Statistical Summaries & Comparisons: computes mean, std, deltas, and significance indicators across seeds or configurations for clear decision criteria.
Use Case: Reviewer asks to compare two models across multiple datasets; the Skill produces a concise summary table and recommended actions.

Quick Start

Run the analysis on the provided results folder to generate a compact, decision-oriented 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 ML experiment results from multiple CSV and JSON logs?▼

To analyze ML experiment results, you can automatically parse metrics from CSV and JSON logs into a unified view. This process collates diverse data sources, computes statistics, and generates structured insights to accelerate interpretation.

Can I compare model performance across different datasets and hyperparameters?▼

Yes, you can compare model performance across different datasets and hyperparameters. The analysis computes mean, standard deviation, deltas, and significance indicators across seeds or configurations to provide clear decision criteria.

What is the best way to summarize variance and detect trends in machine learning experiments?▼

Summarizing variance and detecting trends in machine learning experiments involves computing statistical summaries across multiple configurations. This approach identifies performance differences and generates interpretable insights with reproducible steps.

How do I generate a decision-oriented report for comparing two models?▼

Generating a decision-oriented report for comparing two models requires processing results files to identify performance differences. The output is a concise summary table with recommended actions based on statistical summarization.

Does this results analysis approach work with dashboard exports and different file configurations?▼

Yes, this results analysis approach works with dashboard exports and different file configurations. It implements robust data parsing to collect metrics from various formats into a unified view for comprehensive comparisons.

Are there limitations when parsing results from diverse sources for statistical summarization?▼

Limitations when parsing results from diverse sources for statistical summarization depend on the robustness of the data parsing logic. Accurate variance analysis and trend detection require consistently structured metrics across all processed files.