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Patrones de Backend

Patrones de arquitectura backend, diseño de API, optimización de bases de datos y mejores prácticas del lado del servidor para Node.js, Express y rutas API de Next.js. Úsalo al construir o revisar rutas API de Node.js, Express o Next.js y su acceso a datos.

¿Qué es Patrones de Backend?

Este skill proporciona una guía completa de patrones de arquitectura backend y mejores prácticas para construir aplicaciones del lado del servidor escalables y mantenibles con Node.js, Express y Next.js. Cubre desde el diseño de APIs RESTful y GraphQL hasta la implementación de capas de repositorio, servicio y controlador, así como la optimización de consultas a bases de datos (prevención de N+1, indexación, pooling de conexiones). También incluye estrategias de caché con Redis, manejo centralizado de errores, autenticación JWT, control de acceso basado en roles, limitación de tasa, trabajos en segundo plano y logging estructurado. Es ideal para desarrolladores que buscan estandarizar y mejorar la calidad de sus aplicaciones backend, ya que proporciona ejemplos de código concretos y explicaciones claras sobre cuándo y cómo aplicar cada patrón.

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

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Documentación

Patrones de Desarrollo Backend

Patrones de arquitectura backend y mejores prácticas para aplicaciones escalables del lado del servidor.

Cuándo activarlo

  • Diseñar endpoints de API REST o GraphQL
  • Implementar capas de repositorio, servicio o controlador
  • Optimizar consultas a bases de datos (N+1, indexación, pooling de conexiones)
  • Agregar caché (Redis, en memoria, cabeceras de caché HTTP)
  • Configurar trabajos en segundo plano o procesamiento asíncrono
  • Estructurar el manejo de errores y la validación para APIs
  • Construir middleware (autenticación, logging, limitación de tasa)

Patrones de Diseño de API

Estructura de API RESTful

// CORRECTO: URLs basadas en recursos
GET    /api/markets                 # Listar recursos
GET    /api/markets/:id             # Obtener un recurso
POST   /api/markets                 # Crear recurso
PUT    /api/markets/:id             # Reemplazar recurso
PATCH  /api/markets/:id             # Actualizar recurso
DELETE /api/markets/:id             # Eliminar recurso

// CORRECTO: Parámetros de consulta para filtrado, ordenamiento, paginación
GET /api/markets?status=active&sort=volume&limit=20&offset=0

Patrón Repositorio

// Abstracción de la lógica de acceso a datos
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
  }

  // Otros métodos...
}

Patrón Capa de Servicio

// Lógica de negocio separada del acceso a datos
class MarketService {
  constructor(private marketRepo: MarketRepository) {}

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

    // Obtener datos completos
    const markets = await this.marketRepo.findByIds(results.map(r => r.id))

    // Ordenar por similitud
    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) {
    // Implementación de búsqueda vectorial
  }
}

Patrón Middleware

// Pipeline de procesamiento de solicitud/respuesta
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: 'No autorizado' })
    }

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

// Uso
export default withAuth(async (req, res) => {
  // El handler tiene acceso a req.user
})

Patrones de Base de Datos

Optimización de Consultas

// CORRECTO: BIEN: Seleccionar solo las columnas necesarias
const { data } = await supabase
  .from('markets')
  .select('id, name, status, volume')
  .eq('status', 'active')
  .order('volume', { ascending: false })
  .limit(10)

// INCORRECTO: MAL: Seleccionar todo
const { data } = await supabase
  .from('markets')
  .select('*')

Prevención de Consultas N+1

// INCORRECTO: MAL: Problema de consulta N+1
const markets = await getMarkets()
for (const market of markets) {
  market.creator = await getUser(market.creator_id)  // N consultas
}

// CORRECTO: BIEN: Obtención por lotes
const markets = await getMarkets()
const creatorIds = markets.map(m => m.creator_id)
const creators = await getUsers(creatorIds)  // 1 consulta
const creatorMap = new Map(creators.map(c => [c.id, c]))

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

Patrón de Transacciones

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

  if (error) throw new Error('Transacción fallida')
  return data
}

// Función SQL en Supabase
CREATE OR REPLACE FUNCTION create_market_with_position(
  market_data jsonb,
  position_data jsonb
)
RETURNS jsonb
LANGUAGE plpgsql
AS $$
BEGIN
  -- Iniciar transacción automáticamente
  INSERT INTO markets VALUES (market_data);
  INSERT INTO positions VALUES (position_data);
  RETURN jsonb_build_object('success', true);
EXCEPTION
  WHEN OTHERS THEN
    -- El rollback ocurre automáticamente
    RETURN jsonb_build_object('success', false, 'error', SQLERRM);
END;
$$;

Estrategias de Caché

Capa de Caché con Redis

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

  async findById(id: string): Promise<Market | null> {
    // Verificar caché primero
    const cached = await this.redis.get(`market:${id}`)

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

    // Fallo de caché - obtener de la base de datos
    const market = await this.baseRepo.findById(id)

    if (market) {
      // Almacenar en caché por 5 minutos
      await this.redis.setex(`market:${id}`, 300, JSON.stringify(market))
    }

    return market
  }

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

Patrón Cache-Aside

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

  // Intentar con caché
  const cached = await redis.get(cacheKey)
  if (cached) return JSON.parse(cached)

  // Fallo de caché - obtener de la BD
  const market = await db.markets.findUnique({ where: { id } })

  if (!market) throw new Error('Mercado no encontrado')

  // Actualizar caché
  await redis.setex(cacheKey, 300, JSON.stringify(market))

  return market
}

Patrones de Manejo de Errores

Manejador de Errores Centralizado

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: 'Validación fallida',
      details: error.issues
    }, { status: 400 })
  }

  // Registrar errores inesperados
  console.error('Error inesperado:', error)

  return NextResponse.json({
    success: false,
    error: 'Error interno del servidor'
  }, { status: 500 })
}

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

Reintento con Backoff Exponencial

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 exponencial: 1s, 2s, 4s
        const delay = Math.pow(2, i) * 1000
        await new Promise(resolve => setTimeout(resolve, delay))
      }
    }
  }

  throw lastError!
}

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

Autenticación y Autorización

Validación de 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, 'Token inválido')
  }
}

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

  if (!token) {
    throw new ApiError(401, 'Token de autorización faltante')
  }

  return verifyToken(token)
}

// Uso en una ruta de API
export async function GET(request: Request) {
  const user = await requireAuth(request)

  const data = await getDataForUser(user.userId)

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

Control de Acceso Basado en Roles

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, 'Permisos insuficientes')
      }

      return handler(request, user)
    }
  }
}

// Uso - HOF envuelve el handler
export const DELETE = requirePermission('delete')(
  async (request: Request, user: User) => {
    // Handler recibe usuario autenticado con permiso verificado
    return new Response('Eliminado', { status: 200 })
  }
)

Limitación de Tasa

La limitación de tasa debe usar un almacén compartido como Redis, una puerta de enlace o el limitador nativo de la plataforma. No uses contadores en memoria por proceso para APIs de producción: se reinician al desplegar, se dividen entre réplicas y fallan en entornos serverless o multi-instancia.

Mantén la capa de backend responsable de elegir el punto de integración y la forma del error; usa api-design para el contrato HTTP y security-review para la revisión de casos de abuso.

Trabajos en Segundo Plano y Colas

Patrón de Cola 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('Trabajo fallido:', error)
      }
    }

    this.processing = false
  }

  private async execute(job: T): Promise<void> {
    // Lógica de ejecución del trabajo
  }
}

// Uso para indexar mercados
interface IndexJob {
  marketId: string
}

const indexQueue = new JobQueue<IndexJob>()

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

  // Agregar a la cola en lugar de bloquear
  await indexQueue.add({ marketId })

  return NextResponse.json({ success: true, message: 'Trabajo en cola' })
}

Logging y Monitoreo

Logging Estructurado

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()

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

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

  try {
    const markets = await fetchMarkets()
    return NextResponse.json({ success: true, data: markets })
  } catch (error) {
    logger.error('Error al obtener mercados', error as Error, { requestId })
    return NextResponse.json({ error: 'Error interno' }, { status: 500 })
  }
}

Recuerda: Los patrones de backend permiten aplicaciones escalables y mantenibles del lado del servidor. Elige los patrones que se ajusten a tu nivel de complejidad.

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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