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harvard-artifacts-collection-analytics-app

Extract and transform Harvard Art Museums API data into a Streamlit dashboard.

5|1|Updated May 16, 2026
Search Tags:#data visualization#SQL#ETL#Streamlit#museum analytics
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-app
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-artifacts-collection-analytics-app
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-artifacts-collection-analytics-app
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-app

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires streamlit, pandas, requests, mysql-connector-python, plotly, python-dotenv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of analyzing complex data from the Harvard Art Museums API, providing a streamlined approach to ETL, SQL analytics, and interactive visualization.

Core Features & Use Cases

  • ETL Pipeline: Extract, transform, and load artifact data from the API.
  • SQL Analytics: Execute analytical SQL queries on the data.
  • Streamlit Dashboard: Create interactive visualizations using Streamlit.
  • Use Case: For a museum curator, this Skill can help analyze artifact collections, identify trends, and create informative dashboards for stakeholders.

Quick Start

Use the harvard-artifacts-collection-analytics-app skill to build an ETL pipeline and visualize data from the Harvard Art Museums API.

Frequently Asked Questions about harvard-artifacts-collection-analytics-app

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an ETL pipeline for Harvard Art Museums API data?▼

To build an ETL pipeline for Harvard Art Museums API data, this Skill extracts artifact records, transforms the raw data, and loads it into a SQL database for downstream analytics.

Can I visualize museum analytics data using Streamlit dashboards?▼

Yes, you can visualize museum analytics data using Streamlit dashboards. This Skill uses Streamlit to create interactive visualizations that analyze artifact collections and identify trends.

What do I need to run SQL analytics on Harvard Art Museums artifact data?▼

To run SQL analytics on Harvard Art Museums artifact data, you need Python, SQL, a MySQL database, and the Streamlit framework to execute the ETL pipeline and render the analytical dashboards.

How does data visualization handle complex museum API records?▼

Data visualization handles complex museum API records by using pandas to transform the extracted data and Plotly to render interactive charts within the Streamlit dashboard for stakeholder analysis.

What is the best way to analyze Harvard Art Museums collections for trends?▼

The best way to analyze Harvard Art Museums collections for trends is to use an integrated ETL pipeline that loads artifact data into a SQL database, enabling structured queries and interactive Streamlit visualizations.

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