Agent skill · data analytics · aradotso
harvard-art-museums-etl-pipeline
Build ETL pipelines and analytics dashboards using Harvard Art Museums API with Python, SQL, and Streamlit
Why this skill is useful
Adds executable ETL scripts and SQL schema designs for integrating with the Harvard Art Museums API, which are not commonly found in public documentation.
What it needs
Requires mysql-connector-python, pandas, plotly, python-dotenv, requests, streamlit installed locally. Requires harvard-art-museums account access. About 7k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Harvard Art Museums ETL Pipeline Skill by ara.so — Data Skills collection. This project provides an end-to-end data engineering solution for extracting, transforming, and analyzing artifacts from the Harvard Art Museums API. It demonstrates real-world ETL patterns, SQL analytics, and interactive visualization using Streamlit. What It Does Extracts artifact data from Harvard Art Museums API with pagination and rate limiting Transforms nested JSON into normalized relational tables (metadata, media, colors) Loads data into MySQL/TiDB Cloud with proper foreign key relationships Analyzes data using predefined SQL queries for cultural insights Visualizes results through interactive Plotly charts in a Streamlit dashboard Installation Configuration API Key Setup Get your Harvard Art Museums API key from: https://www.harvardartmuseums.org/collections/api Database Connection Database Schema Create Tables Key API Patterns Extract: Fetch Artifacts with Pagination Transform: Normalize Nested JSON Load: Batch Insert into Database Streamlit Dashboard Main Application Structure ETL Pipeline Page Analytics Queries Common SQL Query Patterns Analytics Dashboard Page Visualization Patterns Create Interactive Charts Complete ETL Workflow Troubleshooting API Rate Limiting Database Connection Issues Handling Missing Data Running the Application This skill enables AI agents to help developers build complete ETL pipelines for cultural heritage data, from API extraction through SQL analytics to interactive visualization.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/harvard-art-museums-etl-pipeline