n8n Workflow: Clean Messy URL Lists & Find Shopify Clothing Stores

Clean up messy URL lists and quickly find Shopify clothing stores worth contacting

This manual n8n workflow takes a pasted pile of messy links, extracts clean domains, verifies which ones are live Shopify stores, and filters for clothing-focused shops that match your product, pricing, activity, and currency rules—returning two outputs: qualified store candidates and rejected sites with clear reasons.

What this workflow does

• Run manually in n8n: click-to-execute using a Manual Trigger.
• Parse and normalize pasted links: extracts domains, removes duplicates and junk (social links, publishers, tracking URLs, and non-store pages), drops likely non-US/foreign domains, and enforces a batch size limit per run (via MAX_PER_RUN).
• Confirm Shopify catalog availability: for each remaining domain, requests /products.json to verify it is a live Shopify storefront and to collect product/vendor/pricing data.
• Compute clothing and storefront quality signals: calculates product count, median variant price, clothing share based on product titles/types/tags, print-on-demand signals, and recent activity from product timestamps.
• Validate homepage context: fetches the homepage to detect store currency and adds warning flags such as store locator, wholesale/stockists, or careers pages.
• Split into two results: one list of stores that pass the catalog checks and filters, and another list of rejected domains including the exact (or capped) product count and a specific rejection reason.

Use cases

• Prospecting for Shopify clothing stores from scraped or exported URL lists.
• Shortlisting potential partners by product count, median variant price, and recent activity.
• Building outreach targets with transparent rejection reasons (e.g., non-clothing focus, weak signals, or unexpected currency/context).

Technical details

• Nodes/logic: if, code, sticky note, manual trigger (in n8n).
• Data checks: domain parsing, Shopify /products.json verification, homepage currency detection, and rules-based filtering.