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Generate regex from plain English and verify it with Python re tests.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "verified-regex-generator" yet — see the docs or source repo.
Generate a regex from this description: match common email addresses, allow letters, numbers, dots, underscores, and hyphens in the local part, and support subdomains in the domain; then create positive and negative tests with Python's re engine until it passes.
A tested email regex plus positive and negative test results verified with Python's re engine.
Create a regex that matches log dates in YYYY-MM-DD format, but excludes slash-separated dates or non-zero-padded versions. Validate with positive and negative examples and automatically refine on failures.
A regex for the target date format along with validated passing test examples.
Generate a regex for product codes like PRD-2024-XXXX, where XXXX is four uppercase letters or digits; then verify it with Python re tests to ensure it rejects wrong lengths or incorrect prefixes.
A ready-to-use, validated regex for the product code format.
Developers or analysts can describe a matching rule in plain language and let the tool generate the regex, reducing manual drafting and debugging. It fits text patterns that are well defined but tedious to express directly.
When users are unsure whether a generated regex is correct, the tool validates it with Python's real re engine using positive and negative test cases. If tests fail, it iterates until they pass.
In MCP clients such as Claude Desktop or Claude Code, users can call it as a tool to generate and validate regex directly in their workflow. This keeps rule design and testing in the same interaction.
It generates regex patterns from plain-English descriptions and verifies them against generated positive and negative examples using Python's re engine. If tests fail, it keeps iterating until they pass.
It is exposed as an MCP tool that can be called from MCP clients such as Claude Desktop or Claude Code. Users simply describe the text pattern they want to match.
It does more than generate a regex: it validates the pattern with Python's real re engine using positive and negative examples, then refines it on failure. That verification loop is a key differentiator.
Test, explain, validate, and optimize regular expressions for text-processing tasks.
Extract, validate, and mask PII with regex for safer, efficient processing.
Choose regex first, then add LLMs for low-confidence parsing edge cases.
Automate email verification workflows and organize validation results via Rube MCP.
Fact-check claims, verify citations, and assess source freshness across trusted databases.
Validate JSON and SQL schemas with AI-driven customizable lint rules.