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scientist-high

Perform ML modeling, causal inference, and time-series analysis with scikit-learn and statsmodels.

12|4|Updated Jan 22, 2026
Search Tags:#research#time-series#cross-validation#model-selection#machine-learning#causal-inference#feature-engineering
One-click install
npx skills add https://github.com/TurnaboutHero/oh-my-antigravity --skill scientist-high
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientist-high
Source: https://github.com/TurnaboutHero/oh-my-antigravity/tree/main/skills/scientist-high
Command: npx skills add https://github.com/TurnaboutHero/oh-my-antigravity --skill scientist-high

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables researchers to perform advanced data analysis, ML modeling, and statistical evaluation within the platform, streamlining complex inquiry workflows.

Core Features & Use Cases

  • Advanced ML Modeling: Build, evaluate, and tune models with pipelines and cross-validation.
  • Causal Inference & Time Series: Apply causal tests and time-series analysis to experimental data.
  • Research Prototyping: Rapidly prototype analyses for hypotheses and publishable insights.

Quick Start

Use scientist-high to analyze dataset.csv with cross-validated ML modeling, feature engineering, and causal inference.

Frequently Asked Questions about scientist-high

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

FAQPage Schema
How do I build and evaluate machine learning models with cross-validation?▼

Cross-validation splits your dataset into training and test folds to evaluate model performance reliably. This Skill builds ML pipelines using scikit-learn, applies cross-validated model selection, and returns performance metrics across folds to prevent overfitting and ensure generalizable results.

Can I perform causal inference and time-series analysis on experimental data?▼

Yes. This Skill applies causal inference tests and time-series analysis methods to experimental datasets using statsmodels and scikit-learn, enabling you to identify causal relationships and temporal patterns while validating statistical significance.

What's the best way to engineer features and prototype research analyses?▼

Feature engineering extracts and transforms raw variables to improve model predictive power. This Skill automates feature engineering within ML pipelines and supports rapid prototyping of analyses for hypotheses, delivering publishable statistical insights.

Do I need to prepare my dataset before running ML modeling and statistical testing?▼

Yes. Prepare input data in tabular format (CSV or similar), ensuring variables are cleaned and formatted for scikit-learn and statsmodels. The Skill then applies preprocessing, feature engineering, and statistical testing within its ML and causal inference workflows.

What advanced ML techniques does this support beyond basic model training?▼

Advanced ML modeling includes hyperparameter tuning, pipeline composition, cross-validated model selection, and statistical testing. Combined with causal inference and time-series methods, these techniques handle complex research questions beyond standard supervised learning.

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