How to use it

1. Download the file and put it in a folder called shopify-refund-pattern-analysis.
2. Claude: zip the folder and upload it under Settings, Capabilities, Skills. Claude Code, Codex, Gemini CLI or Cursor: drop the folder in your skills directory.
3. Connect your store with the Shopify connector (and GA4 or ad accounts where the skill mentions them).
4. Ask for the job in plain English. The skill loads itself when the request matches.

What's inside

Refund Pattern Analysis

When the user asks to analyze refunds, use the connected Shopify MCP to:

1. Pull refund data

Last 90 days: - Refund ID, order ID, line items, refund amount, refund reason - Customer ID, first-order date, acquisition channel (UTM source from original order) - Time between order and refund

2. Segment by SKU

For each SKU with 5+ refunds: - Total units sold - Refund rate (refunds / units sold) - Avg refund amount - Top 3 refund reasons

3. Segment by acquisition channel

• Refund rate by source (Meta / Google / organic / email / direct)
• Flag channels with >2x baseline refund rate

4. Refund-adjusted ROAS

For SKUs sold via paid ads: - Gross ROAS - refund value = net ROAS - Flag SKUs where net ROAS <1.0 (paid ads losing money)

5. Output

• Top 10 SKUs by refund rate (with $ impact)
• Channel-level refund table
• “Pull from Meta” list — SKUs where paid acquisition is unprofitable
• Top 3 product issues to fix (sizing, quality, expectation gap)

Always separate refunds from returns / exchanges if Shopify data allows.

Also on GitHub.