pyopenms

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

This skill provides standardized templates for systematically documenting business logic and domain knowledge following Domain-Driven Design principles. It helps developers capture business rules, process flows, decision trees, and terminology glossaries to maintain consistency between requirements and implementation. Use it when documenting domain models, creating business rule repositories, or bridging communication between business and technical teams.

This skill automatically analyzes CSV files to generate comprehensive statistical summaries and visualizations using Python's pandas and matplotlib/seaborn. It should be triggered whenever a user uploads or references CSV data without prompting for analysis preferences. The tool provides immediate insights into data structure, quality, and patterns through automated analysis and visualization.

This skill helps developers create algorithmic art using p5.js, focusing on generative art, computational aesthetics, and interactive visualizations. It automatically activates for topics like "generative art" or "p5.js visualization" and guides you through creating unique algorithms with features like seeded randomness, flow fields, and particle systems. Use it when you need to build reproducible, code-driven artistic patterns.