Agent skill · data analytics · aradotso
harvard-artifacts-etl-streamlit-analytics
Build ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit
Why this skill is useful
Adds a complete ETL pipeline implementation with SQL schema and Streamlit dashboard for Harvard Art Museums API data.
What it needs
Requires mysql-connector-python, pandas, plotly, python, python-dotenv, requests, streamlit installed locally. Requires harvard-art-museums account access. About 6k 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 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 Installation Prerequisites Python 3.8+ MySQL or TiDB Cloud database Harvard Art Museums API key (get from https://docs.harvardartmuseums.org/api-docs/) Setup Steps Dependencies (requirements.txt) Configuration Database Connection API Configuration Database Schema Create Tables ETL Pipeline Implementation Extract: Fetch Data from API Transform: Clean and Structure Data Load: Insert into SQL Database Analytics Queries Sample Analytical Queries Streamlit Dashboard Implementation Basic App Structure Common Patterns Pattern: Incremental Data Loading Pattern: Data Validation Troubleshooting API Rate Limiting Database Connection Issues Memory Issues with Large Datasets Handling Missing Data This skill provides comprehensive guidance for building ETL pipelines and analytics dashboards with the Harvard Art Museums API, enabling AI agents to assist developers in data engineering projects.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/harvard-artifacts-etl-streamlit-analytics