Communitygithub.com

github/premium-frontend-ui

GitHub Copilot이 고급 모션, 타이포그래피 및 아키텍처 기법으로 몰입감 있고 고성능의 웹 경험을 제작할 수 있도록 안내하는 종합 가이드입니다.

premium-frontend-ui란 무엇인가요?

이 스킬은 GitHub Copilot이 최신 웹 UI를 개발할 때 고급 애니메이션, 타이포그래피, 컴포넌트 아키텍처를 활용하여 몰입감과 성능을 극대화하도록 돕습니다. 프론트엔드 개발자들이 흔히 간과하는 모션 디자인, 폰트 최적화, 상태 관리, 재사용 가능한 컴포넌트 구조 등에 대한 모범 사례를 제공합니다. 지원 플랫폼은 GitHub Copilot에 한정되며, Copilot이 코드를 생성하거나 제안할 때 이 가이드의 규칙을 따르도록 합니다. 따라서 사용자는 더 일관되고 고품질의 UI 코드를 얻을 수 있으며, 유지보수성과 확장성 역시 개선됩니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/premium-frontend-ui

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

premium-frontend-ui은(는) 무엇을 하나요?

GitHub Copilot이 고급 모션, 타이포그래피 및 아키텍처 기법으로 몰입감 있고 고성능의 웹 경험을 제작할 수 있도록 안내하는 종합 가이드입니다.

개요

이 스킬은 GitHub Copilot이 프런트엔드 개발 시 미적 감각과 성능을 동시에 만족시키는 코드를 생성하도록 돕습니다. Copilot이 제안하는 모든 UI 관련 코드는 다음 원칙을 따라야 합니다.

  • 모션 디자인: 사용자 경험을 향상시키기 위해 CSS 애니메이션, 전환, keyframes를 적극 활용합니다. 애니메이션은 60fps를 유지하며, 불필요한 리플로우를 피합니다.
  • 타이포그래피: 시스템 폰트 스택을 우선 사용하며, 웹 폰트는 필요한 경우에만 중요 CSS로 로드합니다. 타이포그래피 스케일은 일관된 리듬을 유지합니다.
  • 컴포넌트 아키텍처: 작고 재사용 가능한 컴포넌트로 분할하며, 각 컴포넌트는 단일 책임 원칙을 따릅니다. 상태는 가능한 한 가까운 곳에서 관리하고, props는 명시적으로 정의합니다.

주요 기능

기능설명
동적 애니메이션인터랙션에 반응하는 부드러운 애니메이션 생성
타이포그래피 시스템일관된 폰트 크기, 간격, 두께 관리
성능 최적화코드 스플리팅, 이미지 최적화, 지연 로딩 패턴
접근성ARIA 속성, 키보드 네비게이션, 색상 대비 보장

사용 예시

다음은 Copilot이 이 스킬을 활성화한 상태에서 생성할 수 있는 코드 예시입니다.

import { motion } from 'framer-motion';

const FadeIn = ({ children }) => (
  <motion.div
    initial={{ opacity: 0, y: 20 }}
    animate={{ opacity: 1, y: 0 }}
    transition={{ duration: 0.6, ease: 'easeOut' }}
  >
    {children}
  </motion.div>
);

export default FadeIn;

환경 변수

이 스킬은 특별한 환경 변수를 요구하지 않습니다.

참고 자료

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

관련 스킬