Clean, explore, test, and model quantitative datasets reproducibly.
npx skills add https://github.com/Marazii/research-co-pilot --skill data-analysis-marazii
Please help me install this Agent Skill. Skill: data-analysis Source: https://github.com/Marazii/research-co-pilot/tree/main/skills/data-analysis Command: npx skills add https://github.com/Marazii/research-co-pilot --skill data-analysis-marazii
This Skill enables users to perform comprehensive data analysis, including cleaning, exploration, statistical testing, and modeling, in a reproducible manner.
Describe your dataset and analysis question to get started with data cleaning, visualization, and modeling steps.
High-intent search queries and answers about installing and using this skill.
Reproducible quantitative data analysis requires cleaning messy datasets by identifying duplicates, handling missing data, and encoding variables before exploration. This ensures robustness and honest communication of uncertainty in downstream statistical results.
Exploratory data analysis on diverse datasets uses univariate and bivariate visualizations, correlation assessments, and time series insights to understand data patterns. This structured workflow reveals underlying variable distributions before applying statistical models.
Yes, you can build linear, logistic, and mixed-effects statistical models with diagnostic checks to ensure robust and validated results. This supports applied analytics workflows across research and data science contexts.
End-to-end statistical testing is fully supported, facilitating everything from initial data preparation to final modeling tasks. It ensures reproducibility and robustness across diverse datasets and variables throughout the entire workflow.
While automated workflows streamline statistical modeling and data exploration, they still require honest communication of uncertainty in results. Users must interpret diagnostic checks carefully to validate model robustness across diverse datasets.
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