skills.rest
raja21068
raja21068COMMUNITY
Explore all skills by this author →
WebsiteGitHub

analyze-results

Analyze ML experiment JSON/CSV outputs into comparison tables and statistical summaries.

2|1|Updated Apr 19, 2026
Search Tags:#statistics#insight generation#hyperparameter sweep#ml experiments#result analysis#comparison tables#model benchmarking
One-click install
npx skills add https://github.com/raja21068/AutoResearch --skill analyze-results-raja21068
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-results
Source: https://github.com/raja21068/AutoResearch/tree/main/skills/aris/analyze-results
Command: npx skills add https://github.com/raja21068/AutoResearch --skill analyze-results-raja21068

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you interpret messy ML experiment outputs by converting raw JSON/CSV results into clear comparisons, statistics, and decisions you can act on.

Core Features & Use Cases

  • Result discovery & structuring: Finds relevant result files (e.g., in results/ or figures/) and parses them into analysis-ready data.
  • Comparison tables & delta reporting: Groups results by independent variables and computes relative improvement versus a baseline.
  • Statistical summaries & insight writing: Produces mean±std (when seeds exist), flags outliers/suspicious trends, and drafts observation-to-next-step insights for further experiments.
  • Documentation updates: Converts significant findings into concise statements for experiment reports or notes.

Quick Start

Run analyze-results on your experiment output directory to generate a raw data table, key numbered findings with deltas, and suggested next experiments.

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 JSON and CSV output files?▼

Hyperparameter sweep analysis works by grouping raw JSON or CSV outputs by independent variables, computing relative deltas against a baseline, and identifying significant trends or suspicious outliers across the runs.

What is the best way to compare multiple model benchmarking runs with different seeds?▼

Comparing multiple model benchmarking runs involves aggregating multi-seed results to compute mean±std statistical summaries, flagging outliers, and producing structured comparison tables with relative improvement deltas against a baseline.

Can I generate experiment recommendations directly from raw result logs?▼

Yes, you can generate experiment recommendations from raw result logs by transforming the data into numbered findings with baseline deltas and drafting observation-to-next-step insights for further hyperparameter sweeps.

Does this approach to result analysis require a specific directory structure?▼

No specific directory structure is strictly required, but the analysis process automatically locates relevant result files by searching common output directories like results/ or figures/ to parse data into an analysis-ready format.

What are the limitations when interpreting messy ML experiment outputs?▼

Limitations include relying on the availability of raw JSON or CSV formats for parsing, and requiring multiple seeds or independent variables to accurately compute mean±std statistical summaries and identify meaningful baseline deltas.

Related Skills

Explore More Data & Analytics Skills→
Shallow-WShallow-W

analyze-results

Analyze ML experiment result files to compute statistics and generate comparison tables.

Community
Intermediate
Cuki2910Cuki2910
1

result-analyst

Analyze M2G-Net experiment outputs and produce structured summaries with metric tables.

Community
Intermediate
KYRIE66nbKYRIE66nb

analyze-results

Aggregate ML experiment metrics and generate statistical comparison reports.

Community
Intermediate
hasnahasna
24

Analyze Data

Analyze CSV and JSON files to generate statistical summaries and data quality audits.

Official
Intermediate