Taste-Skill is an AI-specific prompt engineering package and open-source framework released in March 2026 by Leon Lin (Leonxlnx), a front-end development specialist. This tool works by providing structured markdown files (SKILL.md) that contain specific design rules and constraints that the latest AI coding agents, such as Cursor, Claude Code, and Codex, can reference before generating code. It functions similarly to a highly trained professional designer sitting next to an AI developer, providing real-time guidance on typography ratios, margin rules, and micro-animation design directions. By injecting this package as context, AI agents can autonomously analyze the project type and establish a sophisticated design direction through a brief inference process before generating code. Existing AI coding agents, despite their excellent logical implementation capabilities, have limitations in producing aesthetically pleasing designs, resulting in a large number of uniform and clumsy designs, often referred to as "AI slop UI." Common examples include standardized card layouts, irrelevant flashy gradients, and excessive use of emojis. Taste-Skill addresses these issues by acting as a design filter that fundamentally adjusts the output of AI. Just as GPT processes natural text based on a vast corpus, Taste-Skill combines accumulated modern web design best practices with the AI's context to create extremely refined and elegant interfaces. Users can easily control the design intensity optimized for their project using a dial setting that allows them to fine-tune component architecture rules and subtle responsive variables from 1 to 10. This tool is particularly valuable for bioinformatics researchers or data engineers developing complex genomic analysis dashboards or molecular visualization tools. When building visually fatiguing interfaces, such as heatmaps or 3D protein structure viewers with extremely high information density, applying Taste-Skill allows the AI to automatically calculate the optimal margins and color contrast, maximizing data readability. By enabling AI agents to perform a visual curation role in the development process of research web applications where the precision of data visualization is critical, it supports the independent construction of web-based bio-platforms with a level of completeness suitable for conference presentations or global service deployment, even in research environments with a lack of design specialists.