Where are the final figures and tables stored?

Artifacts are first saved to a specific results directory and then automatically copied to a dedicated '0-display' folder for easy inclusion in your documents.

What file formats does this skill generate?

It generates Python analysis scripts, LaTeX (.tex) tables, and figures in both PDF and PNG formats, ready for academic publication.

Can I use this for non-academic projects?

Yes, while optimized for research papers, it is highly effective for any project requiring structured performance reporting, benchmarking, and professional data visualization.

How does it handle different evaluation metrics?

The skill includes a multi-metric loop pattern that automatically iterates through metrics like RMSE, MAE, or CRPS based on your YAML configuration settings.

Does it support interactive data exploration?

Yes, generated scripts can be automatically converted to Jupyter Notebooks (.ipynb), allowing you to interactively explore your data after the initial generation.

Research Evaluation & Visualization

Name: Research Evaluation & Visualization
Author: jluo41

byjluo41

0•

데이터 과학 및 ML

Generates standardized Python scripts for analyzing model performance and creating publication-ready LaTeX tables and figures.

This skill streamlines the research evaluation pipeline by automating the creation of analysis scripts that transform model metrics into paper-ready assets. It provides a structured framework for reading CSV results, performing multi-metric aggregations, and generating consistent Matplotlib figures and LaTeX tables. By enforcing strict naming conventions and configuration-driven workflows, it ensures that research outputs are reproducible, professionally formatted, and automatically organized into display directories for academic papers or technical reports.

주요 기능

01Configuration-driven analysis for model exclusions and metric filtering

020 GitHub stars

03Automatic script-to-notebook conversion for interactive data exploration

04Automated LaTeX table and Matplotlib figure generation

05Built-in support for dimension breakdowns and model grouping

06Standardized pipeline from raw CSV results to publication-ready artifacts

사용 사례

01Comparing LLM performance against statistical baselines in academic papers

02Automating the update of figures and tables when underlying model results change

03Generating ablation study tables and performance breakdown figures

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jluo41/tools evaluation-display-skill

For use in Claude.ai and ChatGPT

Download Skill