Agent skill · data analytics · aradotso
harvard-art-museum-etl-analytics
Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL workflows, SQL analytics, and Streamlit visualization.
Why this skill is useful
Adds executable ETL scripts and SQL analytics tailored for the Harvard Art Museums API that are not commonly found in public documentation.
What it needs
Requires mysql-connector-python, pandas, plotly, python, 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 Museum ETL Analytics Skill Skill by ara.so — Data Skills collection. This skill enables you to build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates real-world ETL pipelines, SQL database design, analytical queries, and interactive Streamlit dashboards for artifact collections. 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 databases SQL Analytics: Pre-built analytical queries for insights on culture, century, media, colors, and departments Visualization: Interactive Plotly charts and Streamlit dashboards for data exploration Database Design: Properly structured relational schema with foreign key relationships Architecture flow: API → ETL → SQL → Analytics → Visualization Installation Prerequisites Python 3.8+ MySQL or TiDB Cloud account Harvard Art Museums API key (get free at: https://www.harvardartmuseums.org/collections/api) Setup Required Dependencies Configuration Database Connection API Configuration Database Schema Create Tables ETL Pipeline Implementation Extract: Fetch Data from API Transform: Process JSON to Relational Format Load: Insert into Database Complete ETL Workflow SQL Analytics Queries Analytical Query Examples Streamlit Dashboard Implementation Main Application Run the Application Common Patterns Error Handling in ETL Incremental Data Loading Troubleshooting API Rate Limiting If you encounter rate limit errors: Database Connection Pool For better performance: Memory Management for Large Datasets This skill provides comprehensive coverage of building ETL pipelines, SQL analytics, and data visualization dashboards using the Harvard Art Museums API with Python, Streamlit, and SQL databases.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/harvard-art-museum-etl-analytics