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Lingjie-wang
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analyze-results

Analyze ML experiment results and generate comparison tables with statistical insights.

Updated Jul 9, 2026
Search Tags:#research#machine learning#metrics#statistics#experiment analysis#hyperparameter sweep#comparison table
One-click install
npx skills add https://github.com/Lingjie-wang/autoRL --skill analyze-results-lingjie-wang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/Lingjie-wang/autoRL/tree/main/Auto-claude-code-research-in-sleep/skills/analyze-results
Command: npx skills add https://github.com/Lingjie-wang/autoRL --skill analyze-results-lingjie-wang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Interpreting raw ML experiment outputs scattered across JSON and CSV files is tedious and error-prone, especially when comparing runs, computing deltas against baselines, and checking reproducibility across seeds. ## Core Features & Use Cases - Result Discovery and Parsing: Locates JSON/CSV result files in figures/, results/, or project output directories and parses them into structured data. - Comparison Tables: Organizes results by independent variables (model type, hyperparameters, data config) and dependent metrics, always computing delta versus baseline. - Statistical Analysis: Reports mean and standard deviation across seeds, identifies parameter sweep trends, and flags outliers or suspicious results. - Use Case: After running a learning-rate sweep with three seeds each, ask the Skill to compare the runs; it produces a raw data table, key findings with interpretation and implications, and suggested follow-up experiments. ## Quick Start Analyze the experiment results in the results/ directory and compare them against the baseline run.

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?▼

Point the Skill at your results directory and it locates JSON or CSV files, organizes them by independent variables like model type and hyperparameters, and builds a comparison table with deltas computed against your baseline run.

How to analyze hyperparameter sweep results with multiple seeds?▼

The Skill reports mean plus or minus standard deviation across seeds to check reproducibility, then identifies trends in the sweep such as monotonic, U-shaped, or plateau behavior, flagging outliers or suspicious values.

What output does the experiment analysis produce?▼

Output always includes a raw data table, numbered key findings structured as observation, interpretation, implication, and next step, plus suggested follow-up experiments when relevant.

Where does the Skill look for experiment result files?▼

It searches common output locations such as figures/ and results/ directories, plus project-specific output folders, and parses JSON results into structured data for analysis.

What are the limitations of automated experiment result analysis?▼

The analysis depends on results being stored in parseable JSON or CSV formats and on consistent metric naming across runs. Interpretations are hypotheses about the data, so significant findings still warrant confirmatory experiments.

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