Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Design system lookup, CSS best practices, and AI-driven UI component generation via MCP tools
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。