regex-tester

What This Skill Does

The regex-tester skill is a powerful command-line interface utility integrated within the OpenClaw agent ecosystem, specifically designed for developers and data analysts to manage, validate, and debug regular expressions. It provides a robust sandbox to execute common regex operations without leaving the development environment. Whether you are dealing with complex log parsing, data cleansing, or structure validation, this skill provides atomic functions for matching, extracting, grouping, and replacing patterns in text strings.

Installation

To integrate this utility into your OpenClaw agent, execute the following installation command in your terminal:

clawhub install openclaw/skills/skills/crossallen/regex-tester

Ensure that your environment supports Python 3, as the internal logic relies on native Python regex libraries to ensure consistent performance and cross-platform compatibility.

Use Cases

This skill is highly versatile and serves several professional workflows:

• Log Analysis: Rapidly parse and extract specific tokens like timestamps, error codes, or IP addresses from unstructured log files.
• Data Cleaning: Normalize input data by performing batch replacements, such as fixing date formats (e.g., converting YYYY/MM/DD to YYYY-MM-DD) or stripping unwanted characters.
• Input Validation: Ensure user-provided data matches required patterns, such as email addresses, phone numbers, or credit card formats before they reach the backend.
• Rapid Prototyping: Generate standard regex patterns for common fields like UUIDs, IPv4 addresses, and alphanumeric usernames to reduce the time spent writing boilerplate code.

Example Prompts

1. "Validate if the string '[email protected]' follows a standard email format using the regex-tester skill."
2. "Extract all the numerical price values from the following text: 'Item A: 99, Item B: 150, Item C: 200'."
3. "Hide the sensitive digits in this phone number: 13800138000, keeping only the first three and last four digits visible."

Tips & Limitations

• Regex Complexity: While the skill handles most standard PCRE patterns, remember that extremely complex or nested lookahead/lookbehind operations may become difficult to maintain or debug. Use the groups function to verify that your capture groups are behaving as expected.
• Performance: For massive datasets (multi-gigabyte files), perform regex operations in smaller batches or piped streams to avoid memory exhaustion.
• Escaping: When passing strings with special regex characters to the tool via the CLI, ensure you properly quote your input to prevent the shell from interpreting symbols like $ or * prematurely.