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設計系統 — 產生與審核視覺系統

使用此技能來產生或審核設計系統、檢查視覺一致性,以及審查涉及樣式的 Pull Request。

設計系統 — 產生與審核視覺系統 是什麼?

此技能專為開發者與 AI 代理設計,旨在簡化設計系統的建立、審核與維護。它提供三種操作模式:第一,透過掃描 CSS、Tailwind 或 styled-components 程式碼庫,自動提煉出色票、排版、間距等設計 token,並參考競爭對手網站靈感,產出包含決策依據的 `DESIGN.md`、`design-tokens.json` 與自含的互動式 HTML 預覽頁面。第二,進行視覺審核,從色彩一致性、字體層級、間距韻律、元件一致性、響應式行為、深色模式、動畫、無障礙、資訊密度與打磨度等十個維度逐項評分(0-10 分),並附上具體範例與修正檔案行號。第三,提供 AI 內容檢測,識別常見的 AI 生成設計痕跡(如過度漸層、無意義的玻璃態卡片)。支援透過 `/design-system` 命令搭配參數使用,適用於新專案啟動、程式碼庫重構前評估,或審查與樣式相關的 Pull Request。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

設計系統 — 產生與審核視覺系統 是做什麼的?

使用時機

  • 開始需要設計系統的新專案
  • 審核現有程式碼庫的視覺一致性
  • 在重新設計之前,了解你現有的內容
  • 當 UI 看起來「不對勁」但你無法 pinpoint 問題時
  • 審查涉及樣式的 Pull Request

運作方式

模式 1:產生設計系統

分析你的程式碼庫並產生一致的設計系統:

1. 掃描 CSS/Tailwind/styled-components 中的現有模式
2. 提取:色彩、排版、間距、邊框圓角、陰影、斷點
3. 研究 3 個競爭者網站以獲得靈感(透過 browser MCP)
4. 提出一組設計 token(JSON + CSS 自訂屬性)
5. 產生 DESIGN.md,包含每個決策的依據
6. 建立一個互動式 HTML 預覽頁面(自含,無相依性)

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

模式 2:視覺審核

在 10 個維度上為你的 UI 評分(每個 0-10 分):

1. 色彩一致性 — 你使用調色盤還是隨機十六進位值?
2. 排版層級 — h1 > h2 > h3 > body > caption 清晰嗎?
3. 間距韻律 — 一致的尺度(4px/8px/16px)還是任意?
4. 元件一致性 — 相似的元素看起來相似嗎?
5. 響應式行為 — 在斷點處流暢還是破版?
6. 深色模式 — 完整實作還是半成品?
7. 動畫 — 有意義的還是無謂的?
8. 無障礙 — 對比度、焦點狀態、觸控目標
9. 資訊密度 — 雜亂還是乾淨?
10. 打磨度 — 懸停狀態、過渡、載入狀態、空狀態

每個維度得到一個分數、具體範例,以及帶有確切檔案:行號的修正建議。

模式 3:AI 內容檢測

識別通用的 AI 生成設計模式:

- 到處都是無意義的漸層
- 預設的紫色到藍色
- 沒有目的的「玻璃擬態」卡片
- 不該圓角的元素被強制圓角
- 滾動時過度的動畫
- 帶有置中文字與預設漸層的通用英雄區
- 沒有人格特色的無襯線字體堆疊

範例

為 SaaS 應用程式產生:

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

審核現有 UI:

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

檢查 AI 內容:

/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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