How does it handle configuration changes?

The skill uses a centralized 1-config.yaml file to manage model exclusions, task filters, figure sizes, and metric lists without needing to modify the underlying script logic.

What is the primary purpose of the evaluation-display-skill?

It is designed to automate the creation of Python scripts that process model evaluation metrics into formatted tables and figures for academic papers.

Can I use this skill for interactive data exploration?

Yes, the generated scripts can be automatically converted into Jupyter Notebooks, allowing you to interactively explore the data before finalizing your paper figures.

Does this skill support LaTeX output?

Yes, it includes specialized patterns for generating LaTeX table code, including features like bolding top-performing models and formatting mean/SEM values.

What kind of figures can this skill generate?

It primarily generates Matplotlib-based visualizations, including line plots, bar charts, and performance breakdowns, saved in both PDF for papers and PNG for presentations.

Academic Evaluation & Display

Name: Academic Evaluation & Display
Author: jluo41

byjluo41

0•

Data Science & ML

Automates the generation of publication-ready tables and figures from model performance data for academic research.

This skill streamlines the academic research pipeline by creating standardized Python scripts for model evaluation and result visualization. It enables researchers to transform raw metric CSV files into professional LaTeX tables and high-quality Matplotlib figures (PDF/PNG) using a configuration-driven approach. With built-in support for dimension breakdowns, model grouping, and multi-metric analysis, the skill ensures consistency across paper outputs while providing automatic conversion to Jupyter Notebooks for interactive data exploration.

Key Features

01Configuration-driven analysis via YAML to handle model exclusions and task filtering

020 GitHub stars

03Automated LaTeX table generation with professional formatting and bolding for best results

04Built-in patterns for complex data breakdowns such as demographics, task types, and model classes

05Standardized Matplotlib figure creation with consistent color mapping and styling

06Automatic script-to-notebook conversion for interactive debugging and exploration

Use Cases

01Generating model comparison tables for academic paper submissions and appendices

02Creating performance breakdown visualizations across different research dimensions

03Automating the refresh of paper-ready figures whenever new model metrics are computed

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jluo41/research-skills evaluation-display-skill

For use in Claude.ai and ChatGPT

Download Skill