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analyze-results

Analyze JSON/CSV ML experiment outputs to compute statistics and generate comparison tables.

1|Updated Mar 26, 2026
Search Tags:#insights#comparison#statistics#analyze#ml#experiments#results
One-click install
npx skills add https://github.com/Lingrongye/federated-learning --skill analyze-results-lingrongye
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/Lingrongye/federated-learning/tree/main/Auto-claude-code-research-in-sleep/skills/analyze-results
Command: npx skills add https://github.com/Lingrongye/federated-learning --skill analyze-results-lingrongye

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quickly analyze ML experiment results, compute statistics, and generate comparison tables with actionable insights.

Core Features & Use Cases

  • Compute mean and std across seeds to assess reproducibility.
  • Generate comparison tables and highlight deltas vs baselines.
  • Provide structured insights and recommended next steps for improving experiments.

Quick Start

Provide the path or description of the results directory and let the Skill generate tables, statistics, and 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 across different hyperparameter seeds and baselines?▼

To compare ML experiment results, this Skill reads JSON and CSV outputs to compute mean and standard deviation across seeds, highlighting deltas against baselines to produce a clear comparison table with actionable insights.

What is the best way to compute statistics and reproducibility metrics for machine learning experiments?▼

Computing statistics for ML experiments involves calculating mean and standard deviation across multiple seeds to assess reproducibility. This Skill automates that by parsing result files to generate structured statistical summaries and trend flags.

Can I analyze machine learning outputs from standard directories like figures, results, and outputs?▼

Yes, you can analyze machine learning outputs from standard directories. The Skill specifically reads result files from common folders like figures/, results/, and outputs to identify experimental data and compute statistics.

How do I generate an insights table from JSON and CSV experiment outputs?▼

To generate an insights table from JSON and CSV experiment outputs, provide the results directory path. The Skill identifies experimental data, computes deltas, and produces a structured table with recommended next steps for improving experiments.

Does analyzing ML experiment results with this approach provide significance testing and trend flags?▼

Yes, analyzing ML experiment results provides significance testing and trend flags where available. The Skill computes these metrics alongside standard statistics to help evaluate the reliability and direction of your experimental outcomes.

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