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前端设计方向

为生产级 UI 工作设定 ECC 专属的前端设计方向。在构建或改进网站、仪表盘、应用、组件、落地页、可视化工具或任何需要更强产品特定设计判断的 Web UI 时使用。

前端设计方向 是什么?

该技能旨在指导开发者或 AI 代理在进行前端 UI 工作时,将焦点从单纯的功能实现转向更具目的性和精致感的设计。它特别适用于 ECC (特定组织) 的上下文环境,是官方 Anthropic `前端设计` 技能的本地化定制版本。技能的核心是要求在设计前明确“目的、受众、基调、记忆点和约束”五个方向,并据此做出有品位的设计决策。它支持所有 Web UI 开发场景,尤其适合构建面向特定产品领域的仪表盘、工具、组件和落地页。通过提供详细的设计方向、实施指南和反模式清单,该技能帮助避免生成千篇一律的“紫色渐变、装饰性斑点、超大卡片”等常见 AI 生成模式,确保最终 UI 具有视觉层次、响应式约束、克制而有效的动效,并与现有项目的设计系统保持一致。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/frontend-design-direction

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打开一个已预加载此 Agent Skill 的新对话。

文档

前端设计方向 是做什么的?

当工作不仅仅是让 UI 功能可用,更要使其感觉有目的性、精致且适合产品领域时,请使用此技能。

来源:linus707 从陈旧社区 PR #1659 中拯救。

注意:ECC 有意不重新打包规范版的 Anthropic frontend-design 技能。如需官方上游技能,请从 anthropics/skills 安装。此技能是 ECC 对 #1659 中有用的本地指导进行的设计方向性拯救。

使用时机

  • 用户要求构建一个网页、应用、仪表盘、制品、组件或 UI。
  • 用户要求让界面更精致、独特、美观或减少通用感。
  • 实现需要视觉层次、排版、颜色、动效、布局和交互方面的决策。
  • 当前 UI 虽能工作,但显得扁平、通用、模板化或与受众不匹配。

设计方向

在编码之前,选择一个具体的方向:

  1. 目的:这个界面完成什么任务?
  2. 受众:谁重复使用这个工作流程,他们首先需要扫描什么?
  3. 基调:实用主义、编辑风格、趣味性、工业感、精致感、技术感、极繁、极简、密集、安静,或其他明确的方向。
  4. 难忘的细节:一个能让结果显得有意识的设计想法。
  5. 约束:框架、可访问性、性能、响应式以及现有设计系统。

让方向与领域相匹配。SaaS 运营工具通常应该密集、安静且易于扫描。作品集、发布页面、游戏或编辑类作品可以更具表现力。不要将落地页的构图强加给需要反复日常使用的工具。

实施指南

  • 除非用户明确要求营销文案,否则将第一屏构建为实际可用的体验。
  • 在引入新视觉系统之前,优先使用现有项目组件、令牌、图标库和路由模式。
  • 当界面依赖图像、产品、地点、人物、游戏画面、图表或可检查媒体时,使用真实或生成的视觉资产。
  • 优先使用上下文相关的排版和间距,而不是通用的超大英雄文本。
  • 保持调色板多维:避免 UI 主要由一个色系主导。
  • 使用 CSS 变量或现有设计令牌,使方向在状态之间保持一致。
  • 明确设计响应式约束:网格、宽高比、最小/最大尺寸、稳定工具栏和固定格式控件不应在标签或悬停状态出现时移动。
  • 审慎但有目的地使用动效。优先选择能明确状态的高信号过渡,而非装饰性动画。
  • 在移动端和桌面端验证文本适配。长标签必须整洁地换行或调整大小,而不是溢出。

反模式

  • 不要默认使用常见的生成模式:紫色渐变、装饰性斑点、超大卡片、模糊的英雄文案或类似存库的氛围媒体。
  • 不要在卡片内部添加 UI 卡片。
  • 当领域需要克制时,不要到处使用单一装饰风格。
  • 不要将主要产品、工具、对象或工作流隐藏在通用营销部分之后。
  • 除非装饰能为产品带来明确价值,否则不要为设计效果增加新依赖。
  • 不要描述 UI 内部的功能特性,因为控件本身就能表达自己。

审查清单

  • 首屏能立即传达产品、工作流或对象。
  • 视觉层次支持扫描和反复使用。
  • 排版适配容器,不与相邻内容重叠。
  • 颜色选择具有对比度,不收缩为单一色调调色板。
  • 对于熟悉的工具操作,在可用时使用图标。
  • 响应式布局在面板、网格、工具栏、控件、磁贴和计数器上具有稳定的尺寸。
  • 资产能正常渲染并承载主题内容,而非充当填充物。
  • 动效能改善方向感,而非掩盖卡顿。
  • 结果与仓库现有前端约定保持一致,除非有明确理由进行偏离。

Individual skills in this repo

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

accessibility

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.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

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.

agent-eval

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.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

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.

agentic-os

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.

agent-introspection-debugging

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.

agent-payment-x402

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.

agent-self-evaluation

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.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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