カスタム監査とアサーション
プロジェクトに関連する特定のパフォーマンスやベストプラクティスのルールを適用する、独自のLighthouse監査を開発します。
「カスタム監査とアサーション」はCoddyKit上の無料Web Performance Optimization & Lighthouseレッスンです。 これはレッスン2/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはWeb Performance Optimization & Lighthouse学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Web Performance Optimization & Lighthouseコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
Extend Lighthouse's Power
Welcome to creating custom Lighthouse audits! While Lighthouse offers many built-in checks, sometimes you need to enforce project-specific rules or validate unique performance requirements.
This lesson shows you how to build your own.
Why Create Custom Audits?
Custom audits allow you to tailor Lighthouse to your exact needs. They help you:
- Enforce Standards: Check for specific coding patterns or component usage.
- Validate Unique Metrics: Measure performance aspects not covered by default.
- Ensure Compliance: Verify project-specific accessibility or SEO rules.
- Automate Checks: Integrate tailored performance feedback directly into your development workflow.
Audit Anatomy: Gatherers & Audits
A custom Lighthouse audit typically has two main parts:
- Gatherer: This component collects raw data about the page. It runs inside the browser and extracts specific information, like DOM elements, network requests, or JavaScript variables.
- Audit: This component analyzes the data collected by the gatherer. It applies logic to determine a score, a pass/fail state, and provides recommendations.
Both are then linked in a Lighthouse configuration file.
Building a Gatherer: Data Collection
A gatherer is responsible for collecting data. It extends Lighthouse.Gatherer and uses methods like afterPass to run JavaScript in the page's context.
Let's create a gatherer that checks for a specific <meta> tag.
Gatherer Code Example
This gatherer looks for an <meta name="author"> tag and returns its content. If not found, it returns null.
// gatherers/author-meta-gatherer.js
'use strict';
const Gatherer = require('lighthouse').Gatherer;
class AuthorMetaGatherer extends Gatherer {
afterPass(options) {
const driver = options.driver;
// Evaluate JS in the browser context
return driver.evaluateAsync(() => {
const metaTag = document.querySelector('meta[name="author"]');
return metaTag ? metaTag.content : null;
});
}
}
module.exports = AuthorMetaGatherer;Building an Audit: Evaluation Logic
An audit takes the data provided by gatherers and applies your custom logic. It extends Lighthouse.Audit and implements a static audit method.
This method receives the collected "artifacts" (data) and determines the audit's result.
Audit Code Example
This audit uses the data from our AuthorMetaGatherer to check if the author meta tag is present and has content. It scores 1 (pass) or 0 (fail).
// audits/author-meta-audit.js
'use strict';
const Audit = require('lighthouse').Audit;
class AuthorMetaAudit extends Audit {
static get meta() {
return {
id: 'author-meta-tag',
title: 'Author meta tag is present',
failureTitle: 'Author meta tag is missing or empty',
description: 'Ensures an author meta tag is present for attribution.',
requiredArtifacts: ['AuthorMetaGatherer'],
};
}
static audit(artifacts) {
const authorMetaContent = artifacts.AuthorMetaGatherer;
const passed = !!authorMetaContent && authorMetaContent.trim().length > 0;
return {
score: passed ? 1 : 0,
details: {
type: 'debuginfo',
headings: [
{key: 'content', itemType: 'text', text: 'Author Meta Content'},
],
items: [{content: authorMetaContent || 'Not found'}],
},
};
}
}
module.exports = AuthorMetaAudit;Integrating into Lighthouse Config
To make Lighthouse aware of your custom gatherer and audit, you need a custom configuration file. This file tells Lighthouse which gatherers to run and which audits to include, often extending the default Lighthouse checks.
// custom-config.js
'use strict';
module.exports = {
extends: 'lighthouse:default', // Inherit default audits
gatherers: [
'./gatherers/author-meta-gatherer.js',
],
audits: [
'./audits/author-meta-audit.js',
],
categories: {
'custom-category': {
title: 'Custom Checks',
description: 'Project-specific performance and best practice audits.',
auditRefs: [
{id: 'author-meta-tag', weight: 1, group: 'metrics'},
],
},
},
};Running Lighthouse with Custom Audits
Finally, you can run Lighthouse programmatically using Node.js, referencing your custom configuration file. This script launches Chrome, runs Lighthouse, and generates a report.
Make sure you have lighthouse and chrome-launcher installed via npm.
// run-custom-audit.js
const lighthouse = require('lighthouse');
const chromeLauncher = require('chrome-launcher');
(async () => {
const chrome = await chromeLauncher.launch({chromeFlags: ['--headless']});
const options = {
logLevel: 'info',
output: 'html',
onlyCategories: ['custom-category'], // Run ONLY our custom category
port: chrome.port
};
const config = require('./custom-config.js'); // Load your custom config
const runnerResult = await lighthouse('https://example.com', options, config);
console.log('Report is done for', runnerResult.lhr.requestedUrl);
// Accessing your custom audit score:
const customAuditScore = runnerResult.lhr.audits['author-meta-tag'].score;
console.log('Author Meta Tag Audit Score:', customAuditScore);
await chrome.kill();
})();Custom Audit Components
You've learned about the key pieces needed to build a custom Lighthouse audit.
Which of the following are essential components when creating a custom Lighthouse audit?
Recap: Your Custom Audit Toolkit
You've successfully learned how to extend Lighthouse's capabilities!
By understanding gatherers, audits, and custom configurations, you can now build powerful, project-specific checks. This allows you to enforce unique best practices and gain deeper, tailored insights into your web performance.
よくある質問
「カスタム監査とアサーション」レッスンは無料ですか？
はい。「カスタム監査とアサーション」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Web Performance Optimization & Lighthouseコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Web Performance Optimization & Lighthouseコースには全4レッスンが含まれています。
「カスタム監査とアサーション」で何を学びますか？
プロジェクトに関連する特定のパフォーマンスやベストプラクティスのルールを適用する、独自のLighthouse監査を開発します。 ブラウザで直接実行するハンズオンコードでWeb Performance Optimization & Lighthouseを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
Web Performance Optimization & Lighthouseを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのWeb Performance Optimization & Lighthouseは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン2/4です。
「カスタム監査とアサーション」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このWeb Performance Optimization & Lighthouseレッスンでコードを書いて実行できますか？
はい。すべてのWeb Performance Optimization & Lighthouseレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。