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analyze-results

Analyze ML experiment results from JSON and CSV files with statistics and comparison tables.

Updated Jun 7, 2026
Search Tags:#machine learning#data analysis#experiment analysis#statistical analysis#insights generation
One-click install
npx skills add https://github.com/czh-ee-2023/zotero-aris --skill analyze-results-czh-ee-2023
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/czh-ee-2023/zotero-aris/tree/main/.claude/skills/analyze-results
Command: npx skills add https://github.com/czh-ee-2023/zotero-aris --skill analyze-results-czh-ee-2023

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill addresses the challenge of interpreting complex ML experiment results, offering a structured approach to computing statistics, generating comparison tables, and drawing insights from experimental data.

Core Features & Use Cases

  • Automated Data Analysis: Automatically analyze and process JSON/CSV files from experiment results.
  • Comparison Tables: Generate side-by-side comparison tables for various models and parameters.
  • Statistical Analysis: Compute means, standard deviations, and flag outliers for reproducibility.
  • Insights Generation: Synthesize findings with explanations, implications, and next steps.
  • Documentation Update: Suggest updates to project notes or experiment reports if significant findings are identified.
  • Use Case: Imagine you have run a series of experiments to test a new model. Use this skill to analyze the results, identify trends, and document the findings.

Quick Start

Use the analyze-results skill to interpret the experimental results found at '/path/to/results/directory'.

Frequently Asked Questions about analyze-results

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

FAQPage Schema
How do I analyze machine learning experiment results from JSON and CSV files?▼

To analyze machine learning experiment results, you can automate the processing of JSON and CSV files to compute statistics, generate comparison tables, and extract insights for structured reporting.

How do I generate side-by-side comparison tables for different ML models?▼

Generating side-by-side comparison tables for ML models involves computing means, standard deviations, and flagging outliers from experimental data to evaluate reproducibility and parameter performance.

Can I use pandas and numpy for statistical analysis of my experiment data?▼

Yes, you can use pandas and numpy for statistical analysis of experiment data to compute descriptive statistics, identify outliers, and process data frames for reproducibility checks.

What is the best way to generate insights and documentation from statistical analysis?▼

The best way to generate insights from statistical analysis is to synthesize findings with explanations, implications, and next steps, suggesting updates directly to project notes or experiment reports.

Does automated experiment analysis work with matplotlib for data visualization?▼

Yes, automated experiment analysis works with matplotlib to support statistical processing, allowing you to visualize trends and distributions directly from the computed experiment data.

Why do I need to flag outliers in machine learning experiment results?▼

Flagging outliers in machine learning experiment results is necessary to ensure reproducibility, verify statistical significance, and prevent anomalous data from skewing model comparison tables.

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