Agent skill · data analytics · aradotso
harvard-art-museums-data-engineering
Build end-to-end data pipelines with Harvard Art Museums API, ETL workflows, SQL analytics, and Streamlit dashboards
Why this skill is useful
Adds a complete data engineering pipeline with executable scripts for API integration, ETL processing, and SQL analytics that are not commonly found in public resources.
What it needs
Requires mysql-connector-python, pandas, plotly, requests, streamlit installed locally. Requires harvardartmuseums account access. About 6k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Harvard Art Museums Data Engineering & Analytics Skill by ara.so — Data Skills collection. This skill enables you to build production-ready data engineering pipelines using the Harvard Art Museums API. The project demonstrates real-world ETL patterns, relational database design, SQL analytics, and interactive visualization with Streamlit. What It Does API Integration: Fetches artifact data from Harvard Art Museums API with pagination and rate limiting ETL Pipeline: Transforms nested JSON into normalized relational tables Database Design: Creates properly structured MySQL/TiDB tables with foreign keys SQL Analytics: Executes 20+ analytical queries for insights Visualization: Builds interactive Streamlit dashboards with Plotly charts Installation Configuration Environment Variables Create a .env file in the project root: Get Harvard API Key 1. Visit https://www.harvardartmuseums.org/collections/api 2. Request an API key (free) 3. Add to your .env file Database Setup Project Structure Core Components 1. API Data Extraction 2. Data Transformation 3. Database Schema Creation 4. Batch Data Loading 5. Analytics Queries 6. Streamlit Dashboard Running the Application Common Patterns Pattern 1: Incremental Data Collection Pattern 2: Error Handling for API Calls Pattern 3: Data Quality Validation Troubleshooting Issue: API Rate Limiting Issue: Database Connection Timeout Issue: Large Dataset Memory Errors Issue: NULL Values in Foreign Keys Key Commands This skill provides everything needed to build production-grade data pipelines for cultural heritage 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-art-museums-data-engineering