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scholar-experiment

Analyze experimental results into validated statistics and publication-ready reporting.

76|5|Updated Jul 7, 2026
Search Tags:#model comparison#research reporting#statistical testing#ablation study#experimental analysis#results drafting#visualization specs
One-click install
npx skills add https://github.com/catlog22/pi-maestro-flow --skill scholar-experiment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scholar-experiment
Source: https://github.com/catlog22/pi-maestro-flow/tree/main/.pi/skills/scholar-experiment
Command: npx skills add https://github.com/catlog22/pi-maestro-flow --skill scholar-experiment

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps researchers and AI practitioners turn raw experimental outputs into rigorous statistical analysis, publication-ready Results text, and journal-quality visualization specs without manually assembling the full reporting workflow.

Core Features & Use Cases

  • Experimental Data Validation: Loads and checks CSV, JSON, TensorBoard, and pickle results for completeness, consistency, missing values, and outliers.
  • Statistical Testing Pipeline: Computes descriptive statistics, runs assumption checks, selects the right hypothesis tests, calculates effect sizes, and applies multiple-comparison corrections.
  • Visualization and Writing Support: Produces figure and table specifications plus a structured Results draft with proper statistical reporting and references.
  • Use Case: A researcher comparing multiple model runs can use this Skill to validate the data, verify significance, generate comparison tables and plots, and draft the paper’s Results section in one workflow.

Quick Start

Ask the Skill to analyze your experimental results dataset and generate a publication-ready statistical report, results draft, and visualization specifications.

Frequently Asked Questions about scholar-experiment

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

FAQPage Schema
How do I turn raw machine learning model outputs into a publication-ready Results section?▼

You can convert raw experimental results into a statistically validated findings report by running assumption checks, applying multiple-comparison corrections, and generating structured Results text with visualization specifications.

How do I perform statistical testing for an ablation study?▼

Statistical testing for an ablation study requires computing descriptive statistics, verifying assumptions, selecting appropriate hypothesis tests, and applying multiple-comparison corrections to validate model comparison significance.

Can I analyze TensorBoard logs and pickle files for model comparison?▼

Yes, you can load and validate CSV, JSON, TensorBoard, and pickle files to check for completeness and outliers before running statistical tests and generating model comparison outputs.

What is the best way to draft statistical reporting for ML research?▼

Drafting statistical reporting for ML research involves validating experimental data, running hypothesis tests with multiple-comparison corrections, and generating structured Results text with proper statistical references.

Does experimental analysis work with CSV and JSON datasets?▼

Experimental analysis supports CSV and JSON datasets by validating data completeness and consistency before running descriptive statistics and hypothesis tests.

When do I need multiple-comparison correction for model comparison?▼

You need multiple-comparison correction during model comparison when multiple hypothesis tests are run simultaneously across different model runs to control error rates and validate significance.

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