# harvard-artifacts-data-engineering-analytics

Builds an ETL workflow for Harvard Art Museums records and a Streamlit analytics dashboard. It fits projects that combine the museum API, Python, and SQL.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2000 installs · ★ 5
- Author: reason-machines
- License: LicenseRef-Unknown · policy=link-only · description only; the file stays with the author
- Source: https://github.com/reason-machines/data-skills/tree/HEAD/skills/harvard-artifacts-data-engineering-analytics/SKILL.md
- Updated: 2026-10-09
- HTML: https://affpapa.org/en/nevibecode/skills/harvard-artifacts-data-engineering-analytics

## What it does

This skill guides an end-to-end workflow for Harvard Art Museums data. It fetches paginated API results, reshapes nested records into separate metadata, media, and color datasets, then inserts them into MySQL or TiDB Cloud.

The analytics side uses SQL queries and a Streamlit interface with Plotly charts. The project expects Python packages from `requirements.txt`, the `HARVARD_API_KEY` variable, and database settings such as `DB_HOST`, `DB_USER`, `DB_PASSWORD`, and `DB_NAME`. Without the API key and database connection values, the pipeline cannot fetch and persist the collection data.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-analytics -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/harvard-artifacts-data-engineering-analytics/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/harvard-artifacts-data-engineering-analytics/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-analytics -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/harvard-artifacts-data-engineering-analytics/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-analytics -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/harvard-artifacts-data-engineering-analytics/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install reason-machines/data-skills harvard-artifacts-data-engineering-analytics
```

2. **Manually: put the files in place**
- File: `.github/skills/harvard-artifacts-data-engineering-analytics/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-analytics -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/reason-machines/data-skills --path skills/harvard-artifacts-data-engineering-analytics
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/harvard-artifacts-data-engineering-analytics/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-analytics -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/reason-machines/data-skills/tree/HEAD/skills/harvard-artifacts-data-engineering-analytics/SKILL.md

## When to use it
- build an ETL workflow for museum records
- turn nested API data into SQL tables
- load metadata, media, and color records into MySQL
- create a Streamlit view for collection analytics
- run predefined SQL analysis on artifact data

## Example prompts
- Build a Python and SQL ETL workflow for Harvard Art Museums records.
- Split the API response into metadata, media, and color tables, then load them into MySQL.
- Create a Streamlit dashboard with Plotly charts for the museum collection.

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From the same set: [reason-machines/data-skills](https://affpapa.org/en/nevibecode/by/reason-machines/data-skills.md) — 74 cards

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