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Taste Skill: An anti-slop frontend framework for AI agents, enabling unique designs and preventing generic outputs. Integrates with various AI coding tools.
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Taste Skill is an open-source frontend framework designed to combat "slop" in AI-generated code, enabling AI agents to produce unique and high-quality user interfaces. It focuses on providing "anti-slop" skill files that guide AI tools like Cursor, Claude Code, Codex, Gemini CLI, v0, and Lovable to generate less generic and more polished frontends.
taste-skill (v2 experimental) for inferring design direction, gpt-tasteskill for stricter GPT/Codex output, image-to-code-skill for design-first implementation, redesign-skill for audits, and visual style skills like soft-skill and minimalist-skill.redesign-skill to audit and improve existing AI-generated codebases.Taste Skill is compatible with:
"Slop" refers to generic, uninspired, or low-quality code and design that AI agents often produce by default, lacking uniqueness and polish.
It provides specialized "skill files" that act as instructions or constraints for AI agents, guiding them to adhere to specific design principles, audit processes, and stylistic requirements, thus avoiding templated results.
Yes, if your AI tool supports SKILL.md files or a similar mechanism for custom instructions, Taste Skill can be integrated. It explicitly lists compatibility with popular agents like Cursor, Claude Code, and Gemini CLI.
Version 2 is an experimental rewrite focusing on better brief inference, mapping briefs to design systems, improved dark mode handling, stricter redesign protocols, and a more robust pre-flight check for generated code.
You can install it using npm with the command: $npx skills add Leonxlnx/taste-skill. For specific versions or skills, additional flags or paths might be used, such as --skill "design-taste-frontend" for v2.
You can support Taste Skill by sponsoring the project on GitHub.