frontend-design은(는) 무엇을 하나요?
독특하고 프로덕션 수준의 프론트엔드 인터페이스를 높은 디자인 퀄리티로 제작합니다. 사용자가 웹 컴포넌트, 페이지 또는 애플리케이션 구축을 요청하고, 코드 품질만큼 시각적 방향성도 중요한 경우에 사용하세요.
독특하고 프로덕션 수준의 프론트엔드 인터페이스를 높은 디자인 퀄리티로 제작합니다. 사용자가 웹 컴포넌트, 페이지 또는 애플리케이션 구축을 요청하고, 코드 품질만큼 시각적 방향성도 중요한 경우에 사용하세요.
이 스킬은 웹 인터페이스의 시각적 방향성과 코드 품질을 동시에 중시하는 개발자 및 AI 에이전트 사용자를 위해 설계되었습니다. `affaan-m/frontend-design`은 독창적이고 프로덕션에 바로 투입 가능한 프론트엔드 인터페이스를 생성하는 데 중점을 둡니다. React, Vue, Angular 등 주요 프론트엔드 프레임워크와 호환되며, 사용자의 요청에 따라 웹 컴포넌트, 페이지, 또는 전체 애플리케이션의 UI를 구축할 수 있습니다. 이 스킬의 핵심은 단순한 코드 생성이 아닌, 일관된 디자인 시스템, 접근성, 반응형 레이아웃을 고려한 고품질의 시각적 결과물을 제공하는 데 있습니다. 또한, 사용자의 피드백을 반영하여 디자인을 반복적으로 개선하는 워크플로우를 지원합니다.
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/frontend-designInstalled? Explore more 이미지 skills: steipete/songsee, mattpocock/improve-codebase-architecture, lobehub/add-provider-doc · View all 6 →
독특하고 프로덕션 수준의 프론트엔드 인터페이스를 높은 디자인 퀄리티로 제작합니다. 사용자가 웹 컴포넌트, 페이지 또는 애플리케이션 구축을 요청하고, 코드 품질만큼 시각적 방향성도 중요한 경우에 사용하세요.
This repo contains 20 individual skills — each has its own dedicated page.
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
Example project-specific skill template based on a real production application.
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.
Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.
Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.