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端到端測試

Playwright 端到端測試模式:Page Object Model、設定、CI/CD 整合、測試產物管理及處理不穩定測試的策略。適用於撰寫 Playwright 測試、建構頁面物件或修復 CI 中不穩定的 E2E 測試。

端到端測試 是什麼?

此技能提供了建構穩定、快速且易於維護的端到端測試套件的全面 Playwright 模式。它涵蓋了測試檔案組織、Page Object Model (POM) 的實作、穩固的 Playwright 設定,以及處理不穩定測試的具體策略。技能中還包含了與 CI/CD 系統(如 GitHub Actions)的整合指引、測試產物(螢幕截圖、軌跡、影片)的管理,以及測試報告範本。此外,它也提供了針對錢包/Web3 連線和金融交易等關鍵流程的測試範例,適用於 Claude Code、Cursor 和 Codex 等 AI 編碼助手平台。此技能的目的是幫助開發者快速產出高品質的 E2E 測試,減少因測試不穩定而耗費的除錯時間。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/e2e-testing

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

端到端測試模式

全面的 Playwright 模式,用於建構穩定、快速且易於維護的 E2E 測試套件。

測試檔案組織

tests/
├── e2e/
│   ├── auth/
│   │   ├── login.spec.ts
│   │   ├── logout.spec.ts
│   │   └── register.spec.ts
│   ├── features/
│   │   ├── browse.spec.ts
│   │   ├── search.spec.ts
│   │   └── create.spec.ts
│   └── api/
│       └── endpoints.spec.ts
├── fixtures/
│   ├── auth.ts
│   └── data.ts
└── playwright.config.ts

Page Object Model (POM)

import { Page, Locator } from '@playwright/test'

export class ItemsPage {
  readonly page: Page
  readonly searchInput: Locator
  readonly itemCards: Locator
  readonly createButton: Locator

  constructor(page: Page) {
    this.page = page
    this.searchInput = page.locator('[data-testid="search-input"]')
    this.itemCards = page.locator('[data-testid="item-card"]')
    this.createButton = page.locator('[data-testid="create-btn"]')
  }

  async goto() {
    await this.page.goto('/items')
    await this.page.waitForLoadState('networkidle')
  }

  async search(query: string) {
    await this.searchInput.fill(query)
    await this.page.waitForResponse(resp => resp.url().includes('/api/search'))
    await this.page.waitForLoadState('networkidle')
  }

  async getItemCount() {
    return await this.itemCards.count()
  }
}

測試結構

import { test, expect } from '@playwright/test'
import { ItemsPage } from '../../pages/ItemsPage'

test.describe('項目搜尋', () => {
  let itemsPage: ItemsPage

  test.beforeEach(async ({ page }) => {
    itemsPage = new ItemsPage(page)
    await itemsPage.goto()
  })

  test('應該能依關鍵字搜尋', async ({ page }) => {
    await itemsPage.search('test')

    const count = await itemsPage.getItemCount()
    expect(count).toBeGreaterThan(0)

    await expect(itemsPage.itemCards.first()).toContainText(/test/i)
    await page.screenshot({ path: 'artifacts/search-results.png' })
  })

  test('應該處理無搜尋結果的情況', async ({ page }) => {
    await itemsPage.search('xyznonexistent123')

    await expect(page.locator('[data-testid="no-results"]')).toBeVisible()
    expect(await itemsPage.getItemCount()).toBe(0)
  })
})

Playwright 設定

import { defineConfig, devices } from '@playwright/test'

export default defineConfig({
  testDir: './tests/e2e',
  fullyParallel: true,
  forbidOnly: !!process.env.CI,
  retries: process.env.CI ? 2 : 0,
  workers: process.env.CI ? 1 : undefined,
  reporter: [
    ['html', { outputFolder: 'playwright-report' }],
    ['junit', { outputFile: 'playwright-results.xml' }],
    ['json', { outputFile: 'playwright-results.json' }]
  ],
  use: {
    baseURL: process.env.BASE_URL || 'http://localhost:3000',
    trace: 'on-first-retry',
    screenshot: 'only-on-failure',
    video: 'retain-on-failure',
    actionTimeout: 10000,
    navigationTimeout: 30000,
  },
  projects: [
    { name: 'chromium', use: { ...devices['Desktop Chrome'] } },
    { name: 'firefox', use: { ...devices['Desktop Firefox'] } },
    { name: 'webkit', use: { ...devices['Desktop Safari'] } },
    { name: 'mobile-chrome', use: { ...devices['Pixel 5'] } },
  ],
  webServer: {
    command: 'npm run dev',
    url: 'http://localhost:3000',
    reuseExistingServer: !process.env.CI,
    timeout: 120000,
  },
})

處理不穩定測試的模式

隔離 (Quarantine)

test('不穩定:複雜搜尋', async ({ page }) => {
  test.fixme(true, '不穩定 - Issue #123')
  // 測試程式碼...
})

test('條件性跳過', async ({ page }) => {
  test.skip(process.env.CI, '在 CI 中不穩定 - Issue #123')
  // 測試程式碼...
})

識別不穩定性

npx playwright test tests/search.spec.ts --repeat-each=10
npx playwright test tests/search.spec.ts --retries=3

常見原因與修正

競爭條件 (Race conditions):

// 不好:假設元素已就緒
await page.click('[data-testid="button"]')

// 好:使用自動等待的定位器
await page.locator('[data-testid="button"]').click()

網路時機:

// 不好:任意等待時間
await page.waitForTimeout(5000)

// 好:等待特定條件
await page.waitForResponse(resp => resp.url().includes('/api/data'))

動畫時機:

// 不好:在動畫期間點擊
await page.click('[data-testid="menu-item"]')

// 好:等待穩定狀態
await page.locator('[data-testid="menu-item"]').waitFor({ state: 'visible' })
await page.waitForLoadState('networkidle')
await page.locator('[data-testid="menu-item"]').click()

測試產物管理

螢幕截圖

await page.screenshot({ path: 'artifacts/after-login.png' })
await page.screenshot({ path: 'artifacts/full-page.png', fullPage: true })
await page.locator('[data-testid="chart"]').screenshot({ path: 'artifacts/chart.png' })

軌跡 (Traces)

await browser.startTracing(page, {
  path: 'artifacts/trace.json',
  screenshots: true,
  snapshots: true,
})
// ... 測試操作 ...
await browser.stopTracing()

影片

// 在 playwright.config.ts 中
use: {
  video: 'retain-on-failure',
  videosPath: 'artifacts/videos/'
}

CI/CD 整合

# .github/workflows/e2e.yml
name: E2E Tests
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - run: npx playwright install --with-deps
      - run: npx playwright test
        env:
          BASE_URL: ${{ vars.STAGING_URL }}
      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: playwright-report
          path: playwright-report/
          retention-days: 30

測試報告範本

# E2E Test Report

**日期:** YYYY-MM-DD HH:MM
**持續時間:** Xm Ys
**狀態:** 通過 / 失敗

## 摘要
- 總計:X | 通過:Y (Z%) | 失敗:A | 不穩定:B | 跳過:C

## 失敗的測試

### test-name
**檔案:** `tests/e2e/feature.spec.ts:45`
**錯誤:** 預期元素可見
**螢幕截圖:** artifacts/failed.png
**建議修正:** [描述]

## 產物
- HTML 報告:playwright-report/index.html
- 螢幕截圖:artifacts/*.png
- 影片:artifacts/videos/*.webm
- 軌跡:artifacts/*.zip

錢包 / Web3 測試

test('錢包連線', async ({ page, context }) => {
  // 模擬錢包提供者
  await context.addInitScript(() => {
    window.ethereum = {
      isMetaMask: true,
      request: async ({ method }) => {
        if (method === 'eth_requestAccounts')
          return ['0x1234567890123456789012345678901234567890']
        if (method === 'eth_chainId') return '0x1'
      }
    }
  })

  await page.goto('/')
  await page.locator('[data-testid="connect-wallet"]').click()
  await expect(page.locator('[data-testid="wallet-address"]')).toContainText('0x1234')
})

金融 / 關鍵流程測試

test('交易執行', async ({ page }) => {
  // 跳過生產環境 — 涉及真實金錢
  test.skip(process.env.NODE_ENV === 'production', '在生產環境中跳過')

  await page.goto('/markets/test-market')
  await page.locator('[data-testid="position-yes"]').click()
  await page.locator('[data-testid="trade-amount"]').fill('1.0')

  // 驗證預覽
  const preview = page.locator('[data-testid="trade-preview"]')
  await expect(preview).toContainText('1.0')

  // 確認交易並等待區塊鏈
  await page.locator('[data-testid="confirm-trade"]').click()
  await page.waitForResponse(
    resp => resp.url().includes('/api/trade') && resp.status() === 200,
    { timeout: 30000 }
  )

  await expect(page.locator('[data-testid="trade-success"]')).toBeVisible()
})

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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