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data-analysis

Automate end-to-end data analysis workflows in R with reproducible outputs.

Updated Mar 2, 2026
Search Tags:#workflow#data analysis#regression#r#reproducibility#publication
One-click install
npx skills add https://github.com/AmbroseTriC/ECON53_DAC --skill data-analysis-ambrosetric
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/AmbroseTriC/ECON53_DAC/tree/main/.agents/skills_pre_codex_migration_2026-03-19/data-analysis
Command: npx skills add https://github.com/AmbroseTriC/ECON53_DAC --skill data-analysis-ambrosetric

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the entire data analysis process in R, from data loading through diagnostics, modeling, and creating publication-quality outputs, saving analysts time and ensuring reproducibility.

Core Features & Use Cases

  • Comprehensive Workflow Automation: Guides users through setup, exploratory analysis, modeling, and presentation steps in R.
  • Reproducibility & Standards: Ensures scripts adhere to coding conventions and outputs are ready for publication.
  • Use Case: An academic researcher wants to perform panel data regression analysis, generate diagnostics, create LaTeX tables, and produce high-quality figures following strict conventions.

Quick Start

Use the data-analysis skill to run an entire R data analysis pipeline from data loading to final outputs, ensuring compliance with standards and reproducibility.

Frequently Asked Questions about data-analysis

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

FAQPage Schema
How do I automate an R data analysis workflow for publication-ready outputs?▼

Automating an R data analysis workflow involves guiding scripts through data preparation, diagnostics, and modeling to generate publication-ready outputs. This ensures reproducibility and adherence to coding standards for research projects.

What is the best way to ensure reproducibility in R regression analysis?▼

Ensuring reproducibility in R regression analysis requires automating end-to-end workflows from data loading to reporting. This enforces coding conventions, clean script management, and comprehensive documentation for academic and professional projects.

Can I generate LaTeX tables and diagnostic figures directly in R?▼

Generating LaTeX tables and diagnostic figures directly in R is possible through comprehensive workflow automation. This streamlines panel data regression analysis and produces high-quality outputs following strict publication conventions.

Does this data analysis workflow handle panel data regression and diagnostics?▼

This data analysis workflow handles panel data regression and diagnostics by automating the modeling process. It generates necessary diagnostics, creates LaTeX tables, and produces high-quality figures suitable for academic research.

Do I need specific R packages to run reproducible data analysis pipelines?▼

Running reproducible data analysis pipelines requires an R environment capable of executing scripts for data preparation, diagnostics, and modeling. No specific external dependencies are mandated beyond the base R setup to ensure standards compliance.

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