# retail-etl-medallion-pipeline

This skill helps build a retail ETL pipeline with Bronze, Silver, and Gold layers. Use it for sales, inventory, and supplier data flows.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1800 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/retail-etl-medallion-pipeline/SKILL.md
- Updated: 2026-10-09
- HTML: https://affpapa.org/en/nevibecode/skills/retail-etl-medallion-pipeline

## What it does

The skill lays out an end-to-end retail data pipeline. Bronze lands CSV records with little change, Silver cleans and standardizes them, and Gold produces analytics-ready aggregates and models. The workflow covers branch sales, product recipes and yields, stock tracking, and supplier rebate calculations.

The project includes TSQL scripts for the `bronze`, `silver`, `gold`, and `staging` schemas, a `Retail-Data-Warehouse` layout, SQL Server container setup, and source CSV files. The description also names PySpark and Airflow as parts of the ETL stack.

You need SQL Server 2017+ or the container defined in `docker-compose.yml`; database setup uses `$SQL_SA_PASSWORD`, and the CSV files must be placed where the database can access them.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill retail-etl-medallion-pipeline -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/retail-etl-medallion-pipeline/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/retail-etl-medallion-pipeline/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill retail-etl-medallion-pipeline -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/retail-etl-medallion-pipeline/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill retail-etl-medallion-pipeline -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/retail-etl-medallion-pipeline/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install reason-machines/data-skills retail-etl-medallion-pipeline
```

2. **Manually: put the files in place**
- File: `.github/skills/retail-etl-medallion-pipeline/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill retail-etl-medallion-pipeline -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/reason-machines/data-skills --path skills/retail-etl-medallion-pipeline
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/retail-etl-medallion-pipeline/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill retail-etl-medallion-pipeline -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/retail-etl-medallion-pipeline/SKILL.md

## When to use it
- build a retail ETL flow across Bronze, Silver, and Gold
- load sales, product, and stock CSV files
- clean and standardize records before analysis
- calculate inventory turnover and shrinkage metrics
- prepare aggregated data for BI dashboards

## Example prompts
- Build a retail warehouse with Bronze, Silver, and Gold data layers.
- Set up SQL Server ingestion for sales, product, and stock CSV files.
- Organize the ETL scripts and produce Gold-level retail analytics tables.

## Similar
- [ETL Pipeline](https://affpapa.org/en/nevibecode/skills/etl-pipeline.md) — skill · 4800 installs
- [pyspark-etl-best-practices-cursorrules-prompt-file](https://affpapa.org/en/nevibecode/rules/pyspark-etl-best-practices-cursorrules-prompt-file.md) — rule · ★ 41024
- [retail-expert](https://affpapa.org/en/nevibecode/skills/retail-expert.md) — skill · 526 installs
- [spark-engineer](https://affpapa.org/en/nevibecode/skills/spark-engineer.md) — skill · 3800 installs
- [airflow-dag-patterns](https://affpapa.org/en/nevibecode/skills/airflow-dag-patterns.md) — skill · 11000 installs
- [etl-connector-strategy](https://affpapa.org/en/nevibecode/skills/etl-connector-strategy.md) — skill · 8000 installs

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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