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backend-patterns

Modèles d'architecture backend, conception d'API, optimisation de bases de données et bonnes pratiques côté serveur pour Node.js, Express et les routes API Next.js. Utilisez cette compétence lors de la construction ou de la révision de routes API Node.js, Express ou Next.js et de leur accès aux données.

Qu'est-ce que backend-patterns ?

Cette compétence regroupe les modèles d'architecture backend essentiels pour construire des applications serveur scalables et maintenables. Elle couvre la conception d'API RESTful et GraphQL, les couches Repository, Service et Middleware, ainsi que l'optimisation des requêtes, la gestion des transactions et l'évitement du problème N+1. Elle propose également des stratégies de mise en cache avancées (Redis, cache-aside), la gestion centralisée des erreurs, la validation JWT, le contrôle d'accès basé sur les rôles (RBAC), le rate limiting, les files d'attente de jobs en arrière-plan et la journalisation structurée. La compétence est applicable aux environnements Node.js, Express, Next.js et Supabase, principalement via les agents Claude Code, Cursor, et autres assistants de codage. Les exemples sont fournis en TypeScript et s'intègrent directement dans des projets existants.

Compatible avec~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/backend-patterns

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Documentation

Modèles de Développement Backend

Modèles d'architecture backend et bonnes pratiques pour des applications serveur scalables.

Quand Activer

  • Conception d'endpoints API REST ou GraphQL
  • Implémentation des couches Repository, Service ou Controller
  • Optimisation des requêtes de base de données (N+1, indexation, pooling de connexions)
  • Ajout de cache (Redis, mémoire, en-têtes de cache HTTP)
  • Configuration de jobs en arrière-plan ou de traitement asynchrone
  • Structuration de la gestion des erreurs et de la validation pour les API
  • Construction de middleware (authentification, journalisation, limitation de débit)

Modèles de Conception d'API

Structure API RESTful

// BON: URLs basées sur les ressources
GET    /api/markets                 # Lister les ressources
GET    /api/markets/:id             # Obtenir une ressource unique
POST   /api/markets                 # Créer une ressource
PUT    /api/markets/:id             # Remplacer une ressource
PATCH  /api/markets/:id             # Mettre à jour une ressource
DELETE /api/markets/:id             # Supprimer une ressource

// BON: Paramètres de requête pour le filtrage, le tri, la pagination
GET /api/markets?status=active&sort=volume&limit=20&offset=0

Modèle Repository

// Logique d'accès aux données abstraite
interface MarketRepository {
  findAll(filters?: MarketFilters): Promise<Market[]>
  findById(id: string): Promise<Market | null>
  create(data: CreateMarketDto): Promise<Market>
  update(id: string, data: UpdateMarketDto): Promise<Market>
  delete(id: string): Promise<void>
}

class SupabaseMarketRepository implements MarketRepository {
  async findAll(filters?: MarketFilters): Promise<Market[]> {
    let query = supabase.from('markets').select('*')

    if (filters?.status) {
      query = query.eq('status', filters.status)
    }

    if (filters?.limit) {
      query = query.limit(filters.limit)
    }

    const { data, error } = await query

    if (error) throw new Error(error.message)
    return data
  }

  // Autres méthodes...
}

Modèle de Couche Service

// Logique métier séparée de l'accès aux données
class MarketService {
  constructor(private marketRepo: MarketRepository) {}

  async searchMarkets(query: string, limit: number = 10): Promise<Market[]> {
    // Logique métier
    const embedding = await generateEmbedding(query)
    const results = await this.vectorSearch(embedding, limit)

    // Récupération des données complètes
    const markets = await this.marketRepo.findByIds(results.map(r => r.id))

    // Tri par similarité
    return markets.sort((a, b) => {
      const scoreA = results.find(r => r.id === a.id)?.score || 0
      const scoreB = results.find(r => r.id === b.id)?.score || 0
      return scoreA - scoreB
    })
  }

  private async vectorSearch(embedding: number[], limit: number) {
    // Implémentation de la recherche vectorielle
  }
}

Modèle Middleware

// Pipeline de traitement requête/réponse
export function withAuth(handler: NextApiHandler): NextApiHandler {
  return async (req, res) => {
    const token = req.headers.authorization?.replace('Bearer ', '')

    if (!token) {
      return res.status(401).json({ error: 'Unauthorized' })
    }

    try {
      const user = await verifyToken(token)
      req.user = user
      return handler(req, res)
    } catch (error) {
      return res.status(401).json({ error: 'Invalid token' })
    }
  }
}

// Utilisation
export default withAuth(async (req, res) => {
  // Le handler a accès à req.user
})

Modèles de Base de Données

Optimisation des Requêtes

// BON: Ne sélectionner que les colonnes nécessaires
const { data } = await supabase
  .from('markets')
  .select('id, name, status, volume')
  .eq('status', 'active')
  .order('volume', { ascending: false })
  .limit(10)

// MAUVAIS: Tout sélectionner
const { data } = await supabase
  .from('markets')
  .select('*')

Prévention du Problème N+1

// MAUVAIS: Problème de requête N+1
const markets = await getMarkets()
for (const market of markets) {
  market.creator = await getUser(market.creator_id)  // N requêtes
}

// BON: Récupération par lot
const markets = await getMarkets()
const creatorIds = markets.map(m => m.creator_id)
const creators = await getUsers(creatorIds)  // 1 requête
const creatorMap = new Map(creators.map(c => [c.id, c]))

markets.forEach(market => {
  market.creator = creatorMap.get(market.creator_id)
})

Modèle de Transaction

async function createMarketWithPosition(
  marketData: CreateMarketDto,
  positionData: CreatePositionDto
) {
  // Utiliser la transaction Supabase
  const { data, error } = await supabase.rpc('create_market_with_position', {
    market_data: marketData,
    position_data: positionData
  })

  if (error) throw new Error('Transaction failed')
  return data
}

// Fonction SQL dans Supabase
CREATE OR REPLACE FUNCTION create_market_with_position(
  market_data jsonb,
  position_data jsonb
)
RETURNS jsonb
LANGUAGE plpgsql
AS $$
BEGIN
  -- La transaction démarre automatiquement
  INSERT INTO markets VALUES (market_data);
  INSERT INTO positions VALUES (position_data);
  RETURN jsonb_build_object('success', true);
EXCEPTION
  WHEN OTHERS THEN
    -- Le rollback se fait automatiquement
    RETURN jsonb_build_object('success', false, 'error', SQLERRM);
END;
$$;

Stratégies de Mise en Cache

Couche de Cache Redis

class CachedMarketRepository implements MarketRepository {
  constructor(
    private baseRepo: MarketRepository,
    private redis: RedisClient
  ) {}

  async findById(id: string): Promise<Market | null> {
    // Vérifier le cache d'abord
    const cached = await this.redis.get(`market:${id}`)

    if (cached) {
      return JSON.parse(cached)
    }

    // Échec du cache - récupérer de la base de données
    const market = await this.baseRepo.findById(id)

    if (market) {
      // Mettre en cache pendant 5 minutes
      await this.redis.setex(`market:${id}`, 300, JSON.stringify(market))
    }

    return market
  }

  async invalidateCache(id: string): Promise<void> {
    await this.redis.del(`market:${id}`)
  }
}

Modèle Cache-Aside

async function getMarketWithCache(id: string): Promise<Market> {
  const cacheKey = `market:${id}`

  // Essayer le cache
  const cached = await redis.get(cacheKey)
  if (cached) return JSON.parse(cached)

  // Échec du cache - récupérer de la base de données
  const market = await db.markets.findUnique({ where: { id } })

  if (!market) throw new Error('Market not found')

  // Mettre à jour le cache
  await redis.setex(cacheKey, 300, JSON.stringify(market))

  return market
}

Modèles de Gestion des Erreurs

Gestionnaire d'Erreurs Centralisé

class ApiError extends Error {
  constructor(
    public statusCode: number,
    public message: string,
    public isOperational = true
  ) {
    super(message)
    Object.setPrototypeOf(this, ApiError.prototype)
  }
}

export function errorHandler(error: unknown, req: Request): Response {
  if (error instanceof ApiError) {
    return NextResponse.json({
      success: false,
      error: error.message
    }, { status: error.statusCode })
  }

  if (error instanceof z.ZodError) {
    return NextResponse.json({
      success: false,
      error: 'Validation failed',
      details: error.issues
    }, { status: 400 })
  }

  // Journaliser les erreurs inattendues
  console.error('Unexpected error:', error)

  return NextResponse.json({
    success: false,
    error: 'Internal server error'
  }, { status: 500 })
}

// Utilisation
export async function GET(request: Request) {
  try {
    const data = await fetchData()
    return NextResponse.json({ success: true, data })
  } catch (error) {
    return errorHandler(error, request)
  }
}

Nouvelle Tentative avec Backoff Exponentiel

async function fetchWithRetry<T>(
  fn: () => Promise<T>,
  maxRetries = 3
): Promise<T> {
  let lastError: Error

  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn()
    } catch (error) {
      lastError = error as Error

      if (i < maxRetries - 1) {
        // Backoff exponentiel : 1s, 2s, 4s
        const delay = Math.pow(2, i) * 1000
        await new Promise(resolve => setTimeout(resolve, delay))
      }
    }
  }

  throw lastError!
}

// Utilisation
const data = await fetchWithRetry(() => fetchFromAPI())

Authentification et Autorisation

Validation du Token JWT

import jwt from 'jsonwebtoken'

interface JWTPayload {
  userId: string
  email: string
  role: 'admin' | 'user'
}

export function verifyToken(token: string): JWTPayload {
  try {
    const payload = jwt.verify(token, process.env.JWT_SECRET!) as JWTPayload
    return payload
  } catch (error) {
    throw new ApiError(401, 'Invalid token')
  }
}

export async function requireAuth(request: Request) {
  const token = request.headers.get('authorization')?.replace('Bearer ', '')

  if (!token) {
    throw new ApiError(401, 'Missing authorization token')
  }

  return verifyToken(token)
}

// Utilisation dans une route API
export async function GET(request: Request) {
  const user = await requireAuth(request)

  const data = await getDataForUser(user.userId)

  return NextResponse.json({ success: true, data })
}

Contrôle d'Accès Basé sur les Rôles

type Permission = 'read' | 'write' | 'delete' | 'admin'

interface User {
  id: string
  role: 'admin' | 'moderator' | 'user'
}

const rolePermissions: Record<User['role'], Permission[]> = {
  admin: ['read', 'write', 'delete', 'admin'],
  moderator: ['read', 'write', 'delete'],
  user: ['read', 'write']
}

export function hasPermission(user: User, permission: Permission): boolean {
  return rolePermissions[user.role].includes(permission)
}

export function requirePermission(permission: Permission) {
  return (handler: (request: Request, user: User) => Promise<Response>) => {
    return async (request: Request) => {
      const user = await requireAuth(request)

      if (!hasPermission(user, permission)) {
        throw new ApiError(403, 'Insufficient permissions')
      }

      return handler(request, user)
    }
  }
}

// Utilisation - HOF enveloppe le handler
export const DELETE = requirePermission('delete')(
  async (request: Request, user: User) => {
    // Le handler reçoit l'utilisateur authentifié avec l'autorisation vérifiée
    return new Response('Deleted', { status: 200 })
  }
)

Limitation de Débit

La limitation de débit doit utiliser un magasin partagé tel que Redis, une passerelle, ou le limiteur natif de la plateforme. N'utilisez pas de compteurs en mémoire par processus pour les API de production : ils sont réinitialisés lors du déploiement, répartis entre les réplicas, et échouent en mode ouvert dans les environnements serverless ou multi-instances.

Gardez la couche backend responsable du choix du point d'intégration et de la forme des erreurs ; utilisez api-design pour le contrat HTTP et security-review pour l'examen des cas d'abus.

Jobs en Arrière-Plan et Files d'Attente

Modèle de File d'Attente Simple

class JobQueue<T> {
  private queue: T[] = []
  private processing = false

  async add(job: T): Promise<void> {
    this.queue.push(job)

    if (!this.processing) {
      this.process()
    }
  }

  private async process(): Promise<void> {
    this.processing = true

    while (this.queue.length > 0) {
      const job = this.queue.shift()!

      try {
        await this.execute(job)
      } catch (error) {
        console.error('Job failed:', error)
      }
    }

    this.processing = false
  }

  private async execute(job: T): Promise<void> {
    // Logique d'exécution du job
  }
}

// Utilisation pour l'indexation des marchés
interface IndexJob {
  marketId: string
}

const indexQueue = new JobQueue<IndexJob>()

export async function POST(request: Request) {
  const { marketId } = await request.json()

  // Ajouter à la file d'attente au lieu de bloquer
  await indexQueue.add({ marketId })

  return NextResponse.json({ success: true, message: 'Job queued' })
}

Journalisation et Surveillance

Journalisation Structurée

interface LogContext {
  userId?: string
  requestId?: string
  method?: string
  path?: string
  [key: string]: unknown
}

class Logger {
  log(level: 'info' | 'warn' | 'error', message: string, context?: LogContext) {
    const entry = {
      timestamp: new Date().toISOString(),
      level,
      message,
      ...context
    }

    console.log(JSON.stringify(entry))
  }

  info(message: string, context?: LogContext) {
    this.log('info', message, context)
  }

  warn(message: string, context?: LogContext) {
    this.log('warn', message, context)
  }

  error(message: string, error: Error, context?: LogContext) {
    this.log('error', message, {
      ...context,
      error: error.message,
      stack: error.stack
    })
  }
}

const logger = new Logger()

// Utilisation
export async function GET(request: Request) {
  const requestId = crypto.randomUUID()

  logger.info('Fetching markets', {
    requestId,
    method: 'GET',
    path: '/api/markets'
  })

  try {
    const markets = await fetchMarkets()
    return NextResponse.json({ success: true, data: markets })
  } catch (error) {
    logger.error('Failed to fetch markets', error as Error, { requestId })
    return NextResponse.json({ error: 'Internal error' }, { status: 500 })
  }
}

Souvenez-vous : Les modèles backend permettent de créer des applications serveur scalables et maintenables. Choisissez des modèles adaptés à votre niveau de complexité.

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.

Skills associés