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HeXiao-55
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analyze-results

Analyzes ML experiment results to compute statistics, compare models, and flag anomalies.

1|Updated Mar 25, 2026
Search Tags:#comparison#statistics#reproducibility#analyze#data-interpretation#ml-metrics#experiment-results
One-click install
npx skills add https://github.com/HeXiao-55/Auto-SurveyMind --skill analyze-results-hexiao-55
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/HeXiao-55/Auto-SurveyMind/tree/main/skills/skills-codex/analyze-results
Command: npx skills add https://github.com/HeXiao-55/Auto-SurveyMind --skill analyze-results-hexiao-55

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.

Core Features & Use Cases

  • Statistical summaries across runs: compute mean, std, and confidence intervals across seeds or runs.
  • Cross-model comparisons: align models by metrics, hyperparameters, and data configurations to produce comparison tables.
  • Use Case: evaluate several ML models with varied hyperparameters to choose the best performing setup for a given task.

Quick Start

Provide the experiment results directory and ask it to analyze results.

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 multiple runs?▼

You can compare ML experiment results by aligning models by metrics and hyperparameters to produce structured comparison tables, computing summary statistics like mean and standard deviation across runs.

What is the best way to compute summary statistics for machine learning models?▼

Computing summary statistics for machine learning models involves calculating mean, standard deviation, and confidence intervals across seeds or runs to identify trends and flag anomalies.

How do I interpret machine learning metrics to choose the best model setup?▼

You interpret machine learning metrics to choose the best model setup by evaluating varied hyperparameters against multiple runs, generating concise insights that identify the highest performing configuration.

Can I flag anomalies and identify trends in experimental ML data automatically?▼

Yes, you can flag anomalies and identify trends in experimental ML data automatically by analyzing results across multiple runs with varied metrics to generate statistics and structured interpretations.

Do I need a specific framework to analyze hyperparameter configurations across runs?▼

No specific framework is required to analyze hyperparameter configurations across runs; you simply provide your experiment results directory to align models by metrics and configurations.

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