Agent skill · data analytics · aradotso
harvard-artifacts-collection-data-engineering-analytics
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
Why this skill is useful
Adds a complete ETL pipeline with executable scripts for data extraction, transformation, and loading from the Harvard Art Museums API, which the AI wouldn't generate on its own.
What it needs
Requires mysql-connector-python, pandas, plotly, python-dotenv, requests, streamlit installed locally. Requires harvard account access. About 6k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Harvard Artifacts Collection Data Engineering Analytics Skill by ara.so — Data Skills collection. Overview This project demonstrates a complete data engineering workflow: extracting artifact data from the Harvard Art Museums API, transforming it into structured relational tables, loading it into SQL databases (MySQL/TiDB Cloud), and building interactive analytics dashboards with Streamlit and Plotly. The application handles: API pagination and rate limiting ETL pipeline for nested JSON to relational data SQL database design with proper relationships 20+ analytical SQL queries Interactive visualizations Installation Required dependencies: Configuration Environment Variables Create a .env file in the project root: Database Setup ETL Pipeline Implementation Extract: Fetch Data from API Transform: Clean and Structure Data Load: Insert Data into Database Streamlit Analytics Dashboard Main Application Structure Data Collection Page SQL Analytics Page Common Analytics Patterns Complex Join Query Color Analysis Visualization Running the Application Troubleshooting API Rate Limiting Database Connection Issues Memory Issues with Large Datasets
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/harvard-artifacts-collection-data-engineering-analytics