String Search Evomap

What This Skill Does

The String Search Evomap skill provides a high-performance, algorithmic approach to text processing within the OpenClaw ecosystem. Developed by gatsby047-oss, this tool transcends basic string matching by implementing adaptive strategies and multi-level pruning techniques. Unlike naive search methods that suffer from O(n*m) complexity in worst-case scenarios, String Search Evomap is architected for large-scale data environments, achieving O(n) performance. It intelligently selects the most efficient search path based on the input text structure, offers statistical awareness, and includes robust redundancy detection, making it an essential utility for developers dealing with high-volume text or logs.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/gatsby047-oss/string-search-evomap

Ensure your development environment is correctly configured to interface with OpenClaw skills. Once installed, the skill can be invoked directly through the agent interface to process text blocks or log files.

Use Cases

• Large-scale Text Searching: Rapidly find patterns across gigabytes of documentation or raw text files.
• Log Analysis: Efficiently parse massive server or application logs to identify error patterns or specific event IDs.
• Pattern Matching: Utilize advanced filtering for complex data mining tasks where traditional regex might be too resource-intensive.
• Data Deduplication: Identify recurring patterns within datasets to clean information or identify redundant data entries.

Example Prompts

1. "OpenClaw, use String Search Evomap to find every occurrence of the 'CRITICAL' error string in the attached server log file and list their line numbers."
2. "I have a massive codebase; please scan the directory using the String Search Evomap skill to identify all instances of deprecated function calls matching the pattern 'deprecated_v1'."
3. "Analyze the following text block for repeated sequence patterns using the String Search Evomap utility and summarize the most frequent occurrences."

Tips & Limitations

To maximize performance, ensure that the input text is sanitized for encoding consistency. While this tool is highly efficient, it is optimized for textual data; avoid passing binary blobs or non-UTF-8 formatted files to ensure predictable results. The O(1) memory footprint makes it ideal for memory-constrained environments, but users should still be mindful of input size when processing extremely large files to prevent system-level buffer bottlenecks. If you encounter performance degradation, verify that the 'statistical awareness' feature is active to allow the algorithm to tune itself to your specific data entropy.