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E2Eテストパターン

Playwrightを使用したE2Eテストのためのページオブジェクトモデル、設定、CI/CD統合、アーティファクト管理、フレーキーテスト対策のパターン集。Playwrightテストの作成、ページオブジェクトの構造化、CIでの不安定なE2E実行の修正に役立ちます。

E2Eテストパターン とは?

このスキルは、Playwrightフレームワークを使用した安定した、高速で、保守しやすいE2Eテストスイートを構築するための包括的なパターンを提供します。テストファイルの整理方法、ページオブジェクトモデル(POM)の実装例、Playwright設定のベストプラクティス、フレーキーテストの特定と修正戦略(検疫、条件付きスキップ、レースコンディションやネットワークタイミングの問題への対処など)を網羅しています。また、スクリーンショット、トレース、動画などのアーティファクト管理方法、GitHub Actionsを使ったCI/CD統合の例、テストレポートテンプレートも含まれています。さらに、ウォレット/Web3アプリケーションのテストや、金融・クリティカルフローのテストといった特殊なシナリオの例も提供しており、あらゆるレベルのE2Eテスト開発者にとって貴重なリソースです。

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

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ドキュメント

E2Eテストパターン は何をしますか?

安定した、高速で、保守しやすいE2Eテストスイートを構築するための包括的なPlaywrightパターンです。

テストファイルの整理

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

ページオブジェクトモデル (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()
  }
}

テスト構造

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

test.describe('アイテム検索', () => {
  let itemsPage: ItemsPage

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

  test('キーワードで検索できること', 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('検索結果が0件の場合のハンドリング', 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,
  },
})

フレーキーテストパターン

検疫

test('flaky: 複雑な検索', async ({ page }) => {
  test.fixme(true, 'フレーキー - Issue #123')
  // テストコード...
})

test('条件付きスキップ', async ({ page }) => {
  test.skip(process.env.CI, 'CIでフレーキー - Issue #123')
  // テストコード...
})

フレーキーの特定

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

一般的な原因と修正

レースコンディション:

// 悪い例: 要素の準備ができていると想定
await page.click('[data-testid="button"]')

// 良い例: 自動待機ロケーター
await page.locator('[data-testid="button"]').click()

ネットワークタイミング:

// 悪い例: 恣意的なタイムアウト
await page.waitForTimeout(5000)

// 良い例: 特定の条件を待つ
await page.waitForResponse(resp => resp.url().includes('/api/data'))

アニメーションタイミング:

// 悪い例: アニメーション中にクリック
await page.click('[data-testid="menu-item"]')

// 良い例: 安定するまで待つ
await page.locator('[data-testid="menu-item"]').waitFor({ state: 'visible' })
await page.waitForLoadState('networkidle')
await page.locator('[data-testid="menu-item"]').click()

アーティファクト管理

スクリーンショット

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' })

トレース

await browser.startTracing(page, {
  path: 'artifacts/trace.json',
  screenshots: true,
  snapshots: true,
})
// ... テストアクション ...
await browser.stopTracing()

ビデオ

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

CI/CD連携

# .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

テストレポートテンプレート

# E2Eテストレポート

**日付:** YYYY-MM-DD HH:MM
**所要時間:** Xm Ys
**ステータス:** PASSING / FAILING

## サマリー
- 合計: X | 合格: Y (Z%) | 不合格: A | フレーキー: B | スキップ: C

## 失敗したテスト

### test-name
**ファイル:** `tests/e2e/feature.spec.ts:45`
**エラー:** 要素が可視であることを期待しました
**スクリーンショット:** artifacts/failed.png
**推奨修正:** [説明]

## アーティファクト
- HTMLレポート: playwright-report/index.html
- スクリーンショット: artifacts/*.png
- ビデオ: artifacts/videos/*.webm
- トレース: artifacts/*.zip

ウォレット / Web3テスト

test('ウォレット接続', async ({ page, context }) => {
  // ウォレットプロバイダーのモック
  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 / クリティカルフローテスト

test('取引実行', async ({ page }) => {
  // 本番環境ではスキップ — 実際の資金が関わるため
  test.skip(process.env.NODE_ENV === 'production', '本番環境ではスキップ')

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

  // プレビューの確認
  const preview = page.locator('[data-testid="trade-preview"]')
  await expect(preview).toContainText('1.0')

  // 確認し、ブロックチェーンを待つ
  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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