Replace Text

Apply multiple find-and-replace rules simultaneously, with regex and whole-word support.

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Replace Rules
FindReplace with

Common Use Cases

Normalise a data export by replacing semicolons with commas and stripping extraneous quote pairs in one pass
Convert a list of variable names from camelCase to snake_case using a regex rule
Replace multiple placeholder tokens ({{name}}, {{date}}, {{company}}) in a template document with real values
Strip all HTML tags from a block of text using a single regex rule: find <[^>]+> and replace with empty string

About Replace Text

Multi-rule find-and-replace is one of the most productive text-processing operations available. Unlike a single find-and-replace pass, applying multiple rules simultaneously lets you transform an entire document in one step — no repeated Ctrl+H dialogs, no risk of missing a pattern because an earlier substitution changed the text in an unexpected way.

Rules are applied in sequence from top to bottom, which means later rules see the output of earlier ones. This ordering matters: if Rule 1 replaces "cat" with "dog" and Rule 2 replaces "dog" with "fish", the final output will have "fish" everywhere both "cat" and "dog" appeared in the original. This chained behaviour is intentional and powerful — it lets you build transformation pipelines.

The case-sensitive toggle controls whether all rules match case-insensitively (the default) or case-sensitively. The whole-words toggle wraps each find pattern in word boundary anchors (`\b`) so "cat" does not match "concatenate" or "tomcat". Both options are disabled when regex mode is active, since in regex mode you have full control over these behaviours by writing the pattern yourself (use `(?i)` for inline case-insensitivity, `\b` for word boundaries, etc.).

The per-rule replacement counter shown next to each row tells you how many matches each rule found, making it easy to spot rules that matched nothing (typo in the find field?) or rules that matched far more than expected.

Common workflows: normalising data exports (replace all `;` with `,` and all `""` with `"`), converting coding conventions (replace `camelCase` prefixes, swap `snake_case` separators), bulk-sanitising user-generated content (replace profanity with asterisks), and preparing text for a specific platform by substituting markdown, HTML entities, or special characters.

Frequently Asked Questions