Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Design system lookup, CSS best practices, and AI-driven UI component generation via MCP tools
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Access design token libraries, CSS documentation, and AI-driven component generation.
Semantic search across 188+ design systems (Material, Ant Design, Carbon, Chakra, etc.):
# Setup
npx @southleft/design-systems-mcp
# Example queries via MCP
mcp__design_systems__search_components({ query: "button with loading state" })
mcp__design_systems__get_tokens({ system: "material", category: "color" })
mcp__design_systems__get_patterns({ pattern: "form validation error" })
Use cases: Find existing component patterns before building custom, get design token values, compare component API across systems.
MDN CSS documentation + browser compatibility via MCP:
mcp__css__query_property({ property: "container-type" }) # Container queries
mcp__css__check_compat({ property: "has()", browsers: ["chrome", "firefox", "safari"] })
mcp__css__get_examples({ selector: ":is()" })
Generate UI components from natural language:
# Configure Magic MCP with 21st-dev API key
mcp__magic__create_component({ description: "dark mode toggle with animation", framework: "react" })
mcp__magic__find_component({ query: "pricing table with feature comparison" })
/* CSS Custom Properties (recommended) */
--color-primary: #6366f1;
--spacing-md: 1rem;
--radius-lg: 0.5rem;
--font-sans: 'Inter', system-ui, sans-serif;
/* Token categories: color, spacing, typography, radius, shadow, z-index */
Skill({ skill: 'design-systems' }) for design token lookup, component pattern research, CSS best practices, AI component generation
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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