Harvard Artifacts Etl Streamlit Analytics

You're looking at a complete ETL pipeline tutorial that pulls artifact data from Harvard Art Museums' public API into a MySQL database, then visualizes it with Streamlit and Plotly. It handles pagination and rate limiting on the API side, structures data across three normalized tables (metadata, media URLs, and color analysis), and includes pre-built analytical queries for culture distributions and color patterns. The repo comes with schema definitions and batch insert logic. Honestly, this is more of a learning project than production infrastructure, but it's a solid reference if you need to wire up any museum API to a dashboard and want working code for the whole stack.

skills.sh installs: 1.4K
GitHub stars: 2
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/data-skills --skill harvard-artifacts-etl-streamlit-analytics --agent claude-code

Installs into .claude/skills of the current project.

Files

Harvard Artifacts ETL & Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables AI coding agents to help developers build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates ETL pipelines, SQL database design, analytical queries, and interactive Streamlit visualizations for museum artifact data.

What This Project Does

The Harvard Artifacts Collection Data Engineering & Analytics App provides:

API Integration: Fetch artifact data from Harvard Art Museums API with pagination and rate limiting
ETL Pipeline: Extract, transform, and load artifact metadata, media, and color data into relational SQL tables
SQL Analytics: Pre-built analytical queries for insights on culture, century, media availability, and color patterns
Interactive Dashboard: Streamlit-based UI with Plotly visualizations for query results