Regex Tester

Regular Expression Basics

A regex is a pattern that matches text. Common patterns:

Regex in Automation Workflows

Regular expressions are used in every automation platform:

• Make/Zapier — extract specific values from text (e.g. extract an invoice number from email body)
• Python — re.findall(), re.sub() for text processing
• SQL — REGEXP_MATCH for pattern-based filtering
• JavaScript — validating form input on the frontend

Common Use Cases

• Email validation — ensure a string matches email format
• Phone number extraction — pull phone numbers from unstructured text
• URL parsing — extract domain, path, and query parameters
• Log analysis — filter error patterns from application logs
• Data cleaning — find and replace patterns in bulk text

Tips for Writing Reliable Regex

1. Use anchors (^ and $) when matching the full string
2. Escape special characters: ., *, +, ?, (, ), [, ]
3. Use non-greedy quantifiers (*?, +?) to avoid over-matching
4. Test against edge cases: empty string, very long input, special characters

Building the Extraction Into a Workflow

Testing the pattern here is usually step one — the next step is wiring it into a Make.com or n8n scenario so it runs automatically on every incoming email or webhook. If that's the piece you're stuck on, book a call and I'll help you get it working end to end.