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设计系统

使用此技能来生成或审计设计系统、检查视觉一致性,以及审核涉及样式的 PR。

设计系统 是什么?

该技能帮助开发者创建和审查设计系统,确保 UI 一致性并防止视觉债务。它提供三种模式:生成模式会扫描代码库(CSS、Tailwind 或 styled-components),提取现有样式模式(颜色、排版、间距等),研究竞争对手网站作为灵感,然后输出一套完整的设计令牌(设计令牌 JSON + CSS 自定义属性)、一份带有决策理由的 DESIGN.md 文件以及一个交互式 HTML 预览页面。审计模式从 10 个维度(颜色一致性、排版层次、间距韵律、组件一致性、响应式行为、暗色模式、动画、无障碍、信息密度和精细度)对 UI 进行评分,并针对每个维度提供具体示例和修复建议(包含精确的文件:行号)。AI 内容检测模式可识别泛泛的 AI 生成设计模式(如过度渐变、紫色到蓝色默认配色、无意义的毛玻璃卡片、过度动画等),帮助团队保持设计意图而非生成式输出。该技能支持 Claude Code、Cursor 和 Codex 等平台,专为项目启动、重构前审计或样式相关 PR 审查而设计。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

设计系统 — 生成与审计视觉系统

何时使用

  • 启动需要设计系统的新项目时
  • 审计现有代码库的视觉一致性时
  • 在重新设计之前——了解你拥有什么
  • 当 UI 看起来“不对劲”但无法准确指出原因时
  • 审核涉及样式的 PR 时

工作原理

模式 1:生成设计系统

分析你的代码库并生成一致的设计系统:

1. 扫描 CSS/Tailwind/styled-components 以查找现有模式
2. 提取:颜色、排版、间距、圆角、阴影、断点
3. 研究 3 个竞争对手网站以获取灵感(通过浏览器 MCP)
4. 提出一套设计令牌(JSON + CSS 自定义属性)
5. 生成 DESIGN.md,并附上每个决策的理由
6. 创建一个交互式 HTML 预览页面(独立文件,无依赖)

输出:DESIGN.md + design-tokens.json + design-preview.html

模式 2:视觉审计

在 10 个维度上为你的 UI 打分(每个维度 0-10 分):

1. 颜色一致性 — 你使用的是调色板还是随机的十六进制值?
2. 排版层次 — 清晰的 h1 > h2 > h3 > body > caption?
3. 间距韵律 — 一致的尺度(4px/8px/16px)还是随意设置?
4. 组件一致性 — 相似的元素看起来是否相似?
5. 响应式行为 — 流畅还是断点处断裂?
6. 暗色模式 — 完整实现还是半成品?
7. 动画 — 有目的性还是无节制?
8. 无障碍 — 对比度、焦点状态、触摸目标
9. 信息密度 — 杂乱还是整洁?
10. 精细度 — 悬停状态、过渡、加载状态、空状态

每个维度都有一个分数、具体示例以及包含精确文件:行号的修复建议。

模式 3:AI 内容检测

识别泛泛的 AI 生成设计模式:

- 到处滥用渐变
- 紫色到蓝色默认配色
- 没有目的的“毛玻璃”卡片
- 不该有圆角的地方加圆角
- 过度的滚动动画
- 带居中文字和默认渐变的通用英雄区域
- 缺乏个性的无衬线字体堆栈

示例

为 SaaS 应用生成:

/design-system generate --style minimal --palette earth-tones

审计现有 UI:

/design-system audit --url http://localhost:3000 --pages / /pricing /docs

检查 AI 内容:

/design-system slop-check

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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