Expert guide for automated and manual Web Accessibility (a11y) testing — axe-core, Pa11y, Playwright a11y, screen reader testing, and WCAG 2.2 Level AA/AAA compliance / Panduan ahli pengujian aksesibilitas web.
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Expert guide for Composable MACH Architecture (Microservices, API-first, Cloud-native, Headless) — dynamic UI composition, federated API mesh, Backend-for-Frontend patterns, plugin/extension architectures, event-driven composition, and composable AI routing / Panduan ahli Arsitektur MACH Komposabel — komposisi UI dinamis, API mesh federasi, pola Backend-for-Frontend, arsitektur plugin/ekstensi, komposisi event-driven, dan routing AI komposabel.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
MACH architecture is a set of technology principles emphasizing best-of-breed, composable enterprise technology ecosystems.
Arsitektur MACH adalah seperangkat prinsip teknologi yang menekankan ekosistem teknologi enterprise yang komposabel dan best-of-breed.
Dynamic UI composition allows building runtime-composed dashboards and applications from independent micro-frontends. Use Module Federation v2 (Webpack/Vite) for dynamic remote loading and Server-Driven UI (SDUI) where the backend defines the layout payload. The Shell application acts as the host for remote modules.
Komposisi UI dinamis memungkinkan pembuatan dashboard dan aplikasi yang dikomposisi saat runtime dari micro-frontend independen. Gunakan Module Federation v2 untuk pemuatan remote dinamis dan Server-Driven UI di mana backend menentukan payload layout. Aplikasi Shell bertindak sebagai host.
// Vite Module Federation dynamic import setup (vite.config.js - Host App)
import { defineConfig } from 'vite';
import react from '@vitejs/plugin-react';
import federation from '@originjs/vite-plugin-federation';
export default defineConfig({
plugins: [
react(),
federation({
name: 'host-app',
remotes: {
// Dynamic loading syntax for Vite federation
inventory_app: 'http://localhost:5001/assets/remoteEntry.js',
analytics_app: 'http://localhost:5002/assets/remoteEntry.js'
},
shared: ['react', 'react-dom']
})
]
});
// React Component importing remote dynamically
import React, { Suspense } from 'react';
const InventoryWidget = React.lazy(() => import('inventory_app/Widget'));
export const Dashboard = () => (
<div>
<h1>Composable Dashboard</h1>
<Suspense fallback={<div>Loading Widget...</div>}>
<InventoryWidget />
</Suspense>
</div>
);
Apollo Federation v2 allows composing distributed GraphQL schemas into a single unified supergraph. This creates an API mesh for service-to-service communication. Gateways (e.g., Kong, Traefik, Cloudflare) manage routing. Federation differs from schema stitching by pushing composition logic to the subgraphs via directives.
Apollo Federation v2 memungkinkan komposisi skema GraphQL terdistribusi menjadi satu supergraph terpadu. Ini menciptakan API mesh. Gateway mengatur routing. Federasi berbeda dari schema stitching dengan mendorong logika komposisi ke subgraph melalui direktif.
// Apollo Federation v2 Subgraph setup (Node.js)
import { ApolloServer } from '@apollo/server';
import { buildSubgraphSchema } from '@apollo/subgraph';
import { gql } from 'graphql-tag';
const typeDefs = gql`
extend schema
@link(url: "https://specs.apollo.dev/federation/v2.0", import: ["@key", "@shareable"])
type User @key(fields: "id") {
id: ID!
purchaseHistory: [Product]
}
type Product @key(fields: "upc") {
upc: String!
name: String! @shareable
}
`;
const resolvers = {
User: {
__resolveReference(userRepresentation) {
return fetchUserById(userRepresentation.id);
}
}
};
const server = new ApolloServer({
schema: buildSubgraphSchema({ typeDefs, resolvers })
});
BFFs aggregate, transform, and optimize data for specific client types (Web, Mobile, Admin). This reduces payload size and over-fetching. Deploy Edge BFFs using Cloudflare Workers or Vercel Edge Middleware for lowest latency.
BFF mengagregasi, mentransformasi, dan mengoptimalkan data untuk jenis klien tertentu. Ini mengurangi ukuran payload dan over-fetching. Deploy Edge BFF menggunakan Cloudflare Workers atau Vercel Edge Middleware.
// Edge BFF using Cloudflare Workers aggregating microservices
export default {
async fetch(request, env) {
const url = new URL(request.url);
if (url.pathname === '/api/mobile-dashboard') {
// Parallel requests to internal microservices
const [userResponse, productsResponse] = await Promise.all([
fetch('https://api.internal.com/users/me', { headers: request.headers }),
fetch('https://api.internal.com/products/featured')
]);
const user = await userResponse.json();
const products = await productsResponse.json();
// Transform and subset data for mobile client
const bffResponse = {
greeting: `Hello, ${user.firstName}`,
quickBuy: products.map(p => ({ id: p.id, name: p.name, price: p.price })) // Strip heavy descriptions
};
return new Response(JSON.stringify(bffResponse), {
headers: { 'Content-Type': 'application/json' }
});
}
return new Response('Not Found', { status: 404 });
}
};
Leverage platforms like commercetools, Medusa.js, or Saleor for headless commerce. Combine with composable content platforms (Sanity, Contentful). Orchestrate integrations using webhooks, event buses, and CDC to maintain synchronization across best-of-breed tools.
Manfaatkan platform seperti commercetools, Medusa.js, atau Saleor. Kombinasikan dengan platform konten (Sanity, Contentful). Orkestrasi integrasi menggunakan webhook, event bus, dan CDC.
// Composable product page fetching from Commerce and CMS
async function getProductPageData(productSlug) {
// 1. Fetch CMS content (Rich text, marketing imagery) from Sanity
const cmsQuery = `*[_type == "product" && slug.current == $slug][0]{ title, marketingCopy, gallery }`;
const cmsPromise = sanityClient.fetch(cmsQuery, { slug: productSlug });
// 2. Fetch Commerce data (Pricing, availability) from Medusa.js / commercetools
const commercePromise = fetch(`https://api.commerce.com/store/products?handle=${productSlug}`)
.then(res => res.json());
const [cmsData, commerceData] = await Promise.all([cmsPromise, commercePromise]);
const product = commerceData.products[0];
// 3. Compose response
return {
id: product.id,
title: cmsData.title || product.title,
description: cmsData.marketingCopy,
images: cmsData.gallery,
price: product.variants[0].prices[0].amount,
inStock: product.variants[0].inventory_quantity > 0
};
}
Design extensible systems using plugin registries. The lifecycle involves discover → load → initialize → execute → teardown. Secure execution by sandboxing (iframe or Web Worker). Provide hook/event systems for integration points.
Desain sistem ekstensibel menggunakan registri plugin. Siklus hidup: temukan → muat → inisialisasi → eksekusi → teardown. Amankan eksekusi dengan sandboxing. Sediakan sistem hook/event.
// Plugin registry with lifecycle management
interface Plugin {
name: string;
init: (context: PluginContext) => Promise<void>;
execute: (payload: any) => Promise<any>;
teardown: () => Promise<void>;
}
class PluginRegistry {
private plugins: Map<string, Plugin> = new Map();
async register(plugin: Plugin) {
if (this.plugins.has(plugin.name)) throw new Error("Plugin exists");
// Sandbox or pass restricted context
const context = { logger: console, api: restrictedApiObject };
await plugin.init(context);
this.plugins.set(plugin.name, plugin);
}
async runHook(hookName: string, payload: any) {
const results = [];
for (const plugin of this.plugins.values()) {
// Execute plugins in parallel or sequentially based on hook semantics
results.push(await plugin.execute({ hook: hookName, data: payload }));
}
return results;
}
}
Achieve loose coupling via async event buses (Redis Streams, Kafka). Use Change Data Capture (CDC) like Debezium for real-time data sync. Understand choreography (decentralized) vs orchestration (centralized) and employ eventual consistency and saga patterns for distributed transactions.
Capai loose coupling melalui event bus asinkron. Gunakan CDC (Debezium) untuk sinkronisasi data real-time. Pahami koreografi vs orkestrasi dan terapkan eventual consistency serta saga pattern untuk transaksi terdistribusi.
// Event-driven service composition with Redis Streams (Node.js)
import { createClient } from 'redis';
const redis = createClient();
await redis.connect();
// Service A: Producer (e.g., Order Service)
async function placeOrder(orderData) {
const orderId = generateId();
// Save to local DB first...
// Emit event to stream
await redis.xAdd('events:orders', '*', {
eventType: 'OrderCreated',
orderId: orderId,
payload: JSON.stringify(orderData)
});
}
// Service B: Consumer (e.g., Inventory Service)
async function listenForOrders() {
let lastId = '$'; // Listen for new messages
while (true) {
const response = await redis.xRead(
{ key: 'events:orders', id: lastId },
{ BLOCK: 5000, COUNT: 1 }
);
if (response) {
const stream = response[0];
const message = stream.messages[0];
lastId = message.id;
const event = message.message;
if (event.eventType === 'OrderCreated') {
const order = JSON.parse(event.payload);
// Reserve inventory based on order...
}
}
}
}
Implement dynamic AI model routing based on request context (complexity, cost, latency). Chain specialized models for complex tasks. Build fallback chains (Primary → Secondary → Local SLM) for resilience. Manage model rollouts with feature flags.
Terapkan routing model AI dinamis berdasarkan konteks permintaan (kompleksitas, biaya, latensi). Rangkai model spesialis untuk tugas kompleks. Buat rantai fallback (Primer → Sekunder → SLM Lokal). Kelola peluncuran model dengan feature flag.
// Dynamic AI model router with cost and latency optimization
async function routePrompt(prompt, context) {
const complexityScore = evaluateComplexity(prompt); // Fast heuristic check
if (complexityScore < 0.3) {
// Simple task: Route to fast, cheap model (e.g., Gemini 3.8 Flash / Claude 3.5 Haiku / GPT-4o-mini)
return await callModel('fast-cheap-model', prompt);
}
if (context.requiresReasoning) {
// Complex reasoning: Route to advanced model (e.g., Gemini 4 Pro / Claude 3.7 Sonnet / Claude 5.1 / GPT-5)
try {
return await callModel('advanced-reasoning-model', prompt);
} catch (error) {
// Fallback chain implementation
console.warn("Primary model failed, falling back to secondary...");
return await callModel('secondary-fallback-model', prompt);
}
}
// Feature-flag controlled routing for A/B testing new models
if (featureFlags.get('use_experimental_model_v2')) {
return await callModel('experimental-model', prompt);
}
return await callModel('default-balanced-model', prompt);
}
Integrate this architectural pattern with the following skills:
micro-frontend-architectevent-driven-architectapi-design-expertheadless-cms-expertecommerce-expertsaas-architectapi-gateway-proxy-expertcloud-hosting-expertbrainstormingzero-to-prod-orchestratorzero-to-prod-orchestratorbrainstorming还没有评价。欢迎分享你的使用感受。
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Expert guide for automated and manual Web Accessibility (a11y) testing — axe-core, Pa11y, Playwright a11y, screen reader testing, and WCAG 2.2 Level AA/AAA compliance / Panduan ahli pengujian aksesibilitas web.
中文简介正在准备中。当前显示原文说明。
Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.
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Expert guide for Affective Computing, emotional AI, and real-time sentiment analysis through native multimodal tokens (voice intonation and facial micro-expressions) / Panduan ahli komputasi afektif, AI emosional, dan analisis sentimen real-time melalui token multimodal native.
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Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.
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Expert guide for autonomous Machine-to-Machine (M2M) agent settlement, dual-rail fiat & stablecoin micro-clearing, dynamic budget envelopes, and cryptographic agent invoicing / Panduan ahli settlement otonom antar-agen (M2M), kliring mikro dual-rail fiat & stablecoin, pagu anggaran dinamis, dan penagihan agen kriptografis.
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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.
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