Regex Generator

What This Skill Does

The Regex Generator is a powerful utility within the OpenClaw AI ecosystem, developed by honestqiao. Its primary purpose is to simplify the complex and often frustrating task of writing Regular Expressions (regex) by translating natural language descriptions and data samples into functional patterns. Whether you are validating user input in a web form, extracting specific data points from logs, or parsing unstructured text, this skill acts as a bridge between human intent and machine-readable patterns.

Installation

To integrate the Regex Generator into your OpenClaw environment, execute the following command in your terminal or command interface:

clawhub install openclaw/skills/skills/honestqiao/regex-generator

Ensure that you have the latest version of OpenClaw installed to maintain compatibility with updated patterns and validation logic.

Use Cases

This skill is indispensable for developers and data analysts who frequently deal with data cleaning and input validation. Common use cases include:

1. Input Validation: Ensuring users provide correctly formatted emails, phone numbers, or ID cards.
2. Log Analysis: Extracting timestamps, IP addresses, or error codes from massive server logs.
3. Data Scrubbing: Cleaning datasets by isolating specific string patterns for removal or transformation.
4. Web Scraping: Identifying specific URL structures or resource paths within HTML content.

Example Prompts

1. "正则表达式: 请为我生成一个可以匹配中国身份证号的正则模式，包含18位数字的校验规则。"
2. "regex: 我需要一个正则来提取文本中的所有URL，要求支持http和https协议，并且包含路径参数。"
3. "生成正则: 写一个正则表达式来匹配日期格式，支持YYYY-MM-DD和YYYY/MM/DD两种常见书写方式。"

Tips & Limitations

• Be Specific: When requesting a regex, provide clear examples of both strings that should match and strings that should not match to ensure higher accuracy.
• Verification: While the Regex Generator provides highly accurate patterns, always test the generated output against your specific application environment and edge cases using tools like Regex101.
• Complex Nesting: Extremely complex nested structures or look-behind assertions in some languages may require manual refinement after the initial generation. If a pattern doesn't work as expected, try breaking the request down into smaller, modular components.