Agent skill · data analytics · aradotso
harvard-artifacts-etl-analytics
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with Python, SQL, and Streamlit
Why this skill is useful
Adds executable ETL scripts and SQL queries for integrating and analyzing Harvard Art Museums API data.
What it needs
Requires mysql-connector-python, pandas, plotly, python-dotenv, requests, streamlit installed locally. Requires harvardartmuseums account access. About 5k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
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 ETL pipelines and analytics applications using the Harvard Art Museums API. The project demonstrates real-world data engineering patterns including API integration, data transformation, SQL database design, and interactive visualization with Streamlit. What This Project Does The Harvard Artifacts Collection application: Extracts artifact data from Harvard Art Museums API with pagination and rate limiting Transforms nested JSON into normalized relational tables Loads data into MySQL/TiDB Cloud databases Provides 20+ predefined analytical SQL queries Visualizes results with interactive Plotly charts in Streamlit Architecture Flow: API → ETL → SQL → Analytics → Visualization Installation Required packages: Configuration Environment Variables Create a .env file in the project root: Get Harvard API Key Register at: https://www.harvardartmuseums.org/collections/api Database Setup Running the Application The Streamlit dashboard will open at http://localhost:8501 Key Code Patterns API Data Extraction ETL Pipeline with Pagination Database Loading Streamlit Analytics Dashboard Complete ETL Workflow Common Analytical Queries Troubleshooting API Rate Limits: Add time.sleep(0.5) between requests Implement exponential backoff for 429 errors Database Connection Issues: Missing Data Fields: Streamlit Caching: This skill provides everything needed to build production-ready ETL pipelines and analytics dashboards for museum collection data using modern Python data engineering tools.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/harvard-artifacts-etl-analytics