Regex Templates

Starting patterns you can adapt to your own input. Test them in the Regex Tester before you deploy.

^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$

^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-4[0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$

^\d{4}-\d{2}-\d{2}$

^\(?\d{3}\)?[\s.-]?\d{3}[\s.-]?\d{4}$

^[a-z0-9]+(?:-[a-z0-9]+)*$

^#(?:[0-9a-fA-F]{3}|[0-9a-fA-F]{6})$

^(?:25[0-5]|2[0-4]\d|[01]?\d\d?)(?:\.(?:25[0-5]|2[0-4]\d|[01]?\d\d?)){3}$

How to Adapt Templates

Use the Regex Tester to validate each template before you adapt it to production data.

If you need explanation, the guide provides step by step context for each pattern type.

For definitions of terms used here, the glossary is a quick reference.

If you want examples in context, the blog includes walkthroughs using these templates.

When questions arise, the FAQ offers short answers.

Ever wondered why a pattern that looked right still fails? You're not imagining it; small shifts in whitespace and casing change matches more than most people expect.

In my experience, the quickest fix is to simplify the pattern and rebuild it in layers. Each layer should be verified with a real sample, not just a single clean line.

Sound familiar? You test a pattern once and think it's done, then real input proves otherwise. That is why a tester is valuable even when the syntax feels familiar.

Don't rely on a single sample. Add negative cases and edge cases so you can see exactly where the match stops and what the pattern still allows.

We're often tempted to compress everything into one clever line. A readable pattern is usually faster to maintain and easier to explain to the next person.

If you're teaching a teammate, show the match window and the captured groups. That small demo turns an abstract rule into a concrete result.

A good test includes edge cases, not just happy paths. Empty lines, extra punctuation, and mixed casing expose gaps a perfect sample will hide.

Regex is powerful because it's expressive, yet that power can hide mistakes. A tester makes those effects visible before the pattern touches production data.

Ever wondered why a pattern that looked right still fails? You're not imagining it; small shifts in whitespace and casing change matches more than most people expect.

In my experience, the quickest fix is to simplify the pattern and rebuild it in layers. Each layer should be verified with a real sample, not just a single clean line.

Sound familiar? You test a pattern once and think it's done, then real input proves otherwise. That is why a tester is valuable even when the syntax feels familiar.

Don't rely on a single sample. Add negative cases and edge cases so you can see exactly where the match stops and what the pattern still allows.

We're often tempted to compress everything into one clever line. A readable pattern is usually faster to maintain and easier to explain to the next person.

If you're teaching a teammate, show the match window and the captured groups. That small demo turns an abstract rule into a concrete result.

A good test includes edge cases, not just happy paths. Empty lines, extra punctuation, and mixed casing expose gaps a perfect sample will hide.

Regex is powerful because it's expressive, yet that power can hide mistakes. A tester makes those effects visible before the pattern touches production data.

Ever wondered why a pattern that looked right still fails? You're not imagining it; small shifts in whitespace and casing change matches more than most people expect.

In my experience, the quickest fix is to simplify the pattern and rebuild it in layers. Each layer should be verified with a real sample, not just a single clean line.

Sound familiar? You test a pattern once and think it's done, then real input proves otherwise. That is why a tester is valuable even when the syntax feels familiar.

Don't rely on a single sample. Add negative cases and edge cases so you can see exactly where the match stops and what the pattern still allows.

We're often tempted to compress everything into one clever line. A readable pattern is usually faster to maintain and easier to explain to the next person.

If you're teaching a teammate, show the match window and the captured groups. That small demo turns an abstract rule into a concrete result.

A good test includes edge cases, not just happy paths. Empty lines, extra punctuation, and mixed casing expose gaps a perfect sample will hide.

Regex is powerful because it's expressive, yet that power can hide mistakes. A tester makes those effects visible before the pattern touches production data.

Ever wondered why a pattern that looked right still fails? You're not imagining it; small shifts in whitespace and casing change matches more than most people expect.

In my experience, the quickest fix is to simplify the pattern and rebuild it in layers. Each layer should be verified with a real sample, not just a single clean line.

Sound familiar? You test a pattern once and think it's done, then real input proves otherwise. That is why a tester is valuable even when the syntax feels familiar.

Don't rely on a single sample. Add negative cases and edge cases so you can see exactly where the match stops and what the pattern still allows.

We're often tempted to compress everything into one clever line. A readable pattern is usually faster to maintain and easier to explain to the next person.

If you're teaching a teammate, show the match window and the captured groups. That small demo turns an abstract rule into a concrete result.

A good test includes edge cases, not just happy paths. Empty lines, extra punctuation, and mixed casing expose gaps a perfect sample will hide.

Regex is powerful because it's expressive, yet that power can hide mistakes. A tester makes those effects visible before the pattern touches production data.