harvard-artifacts-data-engineering-pipeline — Claude Code skill
Pulls artifact records from the Harvard Art Museums API, loads them into SQL tables, and renders a Streamlit dashboard on top.
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a claude-codeWhat harvard-artifacts-data-engineering-pipeline does
The skill fetches museum artifacts from the Harvard Art Museums API with paged requests and rate limiting, then splits the nested JSON into three relational tables covering item metadata, media files, and color palettes. Storage runs on MySQL or TiDB Cloud, with a schema of three linked tables: artifactmetadata, artifactmedia, artifactcolors.
Running it requires an API key from Harvard Art Museums and a .env file holding the database credentials. On the Python side it relies on requests for the API calls, pandas for reshaping records into dataframes, and mysql-connector-python for writing rows into the tables.
Once the data lands in SQL, the skill runs more than 20 prebuilt analytical queries and draws interactive Plotly charts inside a Streamlit app. Without that API key and a configured database, the pipeline has nothing to connect to.
What it helps with
- set up an ETL pipeline for the Harvard Art Museums API
- split nested artifact JSON into normalized SQL tables
- load a museum collection into MySQL or TiDB Cloud
- build a Streamlit dashboard with Plotly charts over artifact data
- write SQL analytics queries on culture, period, and technique fields
Example prompts for the agent
- Build an ETL pipeline for the Harvard Art Museums API with MySQL storage
- Split artifact JSON into metadata, media, and colors tables
- Set up a Streamlit dashboard with charts over the Harvard artifacts collection
How to install harvard-artifacts-data-engineering-pipeline
1Skills CLI (Vercel)
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a claude-code2Manually: put the files in place
~/.claude/skills/harvard-artifacts-data-engineering-pipeline/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/harvard-artifacts-data-engineering-pipeline/
1Skills CLI (Vercel)
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a codex2Manually: put the files in place
.agents/skills/harvard-artifacts-data-engineering-pipeline/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a cursor2Manually: put the files in place
.cursor/skills/harvard-artifacts-data-engineering-pipeline/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install reason-machines/data-skills harvard-artifacts-data-engineering-pipeline2Manually: put the files in place
.github/skills/harvard-artifacts-data-engineering-pipeline/SKILL.md1Skills CLI (Vercel)
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a gemini-cli2Platform CLI
gemini skills install https://github.com/reason-machines/data-skills --path skills/harvard-artifacts-data-engineering-pipelineManually: put the files in place
.devin/skills/harvard-artifacts-data-engineering-pipeline/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about harvard-artifacts-data-engineering-pipeline
How do I install harvard-artifacts-data-engineering-pipeline in Claude Code?
Run npx skills add reason-machines/data-skills --skill harvard-artifacts-data-engineering-pipeline -a claude-code; it installs to ~/.claude/skills/harvard-artifacts-data-engineering-pipeline/SKILL.md.
What does harvard-artifacts-data-engineering-pipeline do?
It pulls records from the Harvard Art Museums API, loads them into SQL tables split across metadata, media, and colors, then powers a Streamlit dashboard with SQL analytics and Plotly charts.
What do I need before running this skill?
An API key from Harvard Art Museums, a MySQL or TiDB Cloud database set up with the matching table schema, and a .env file with the connection details.