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Springboot Patterns

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affaan-m
springboot-patterns

Patrones de arquitectura Spring Boot, diseño de API REST, servicios en capas, acceso a datos, caché, procesamiento asíncrono y logging. Usar para trabajo de backend en Java con Spring Boot.

Overview

Publisheraffaan-m
RepositoryECC
Skill namespringboot-patterns
Stars
261.1K
Forks
39.1K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Springboot Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/docs/es/skills/springboot-patterns .claude/skills/springboot-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Springboot Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Springboot Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Springboot Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Patrones de Desarrollo Spring Boot

Patrones de arquitectura y API de Spring Boot para servicios escalables y listos para producción.

Cuándo Activar

  • Construir APIs REST con Spring MVC o WebFlux
  • Estructurar capas controller → service → repository
  • Configurar Spring Data JPA, caché o procesamiento asíncrono
  • Agregar validación, manejo de excepciones o paginación
  • Configurar perfiles para entornos dev/staging/producción
  • Implementar patrones orientados a eventos con Spring Events o Kafka

Estructura de API REST

java
@RestController
@RequestMapping("/api/markets")
@Validated
class MarketController {
  private final MarketService marketService;

  MarketController(MarketService marketService) {
    this.marketService = marketService;
  }

  @GetMapping
  ResponseEntity<Page<MarketResponse>> list(
      @RequestParam(defaultValue = "0") int page,
      @RequestParam(defaultValue = "20") int size) {
    Page<Market> markets = marketService.list(PageRequest.of(page, size));
    return ResponseEntity.ok(markets.map(MarketResponse::from));
  }

  @PostMapping
  ResponseEntity<MarketResponse> create(@Valid @RequestBody CreateMarketRequest request) {
    Market market = marketService.create(request);
    return ResponseEntity.status(HttpStatus.CREATED).body(MarketResponse.from(market));
  }
}

Patrón de Repositorio (Spring Data JPA)

java
public interface MarketRepository extends JpaRepository<MarketEntity, Long> {
  @Query("select m from MarketEntity m where m.status = :status order by m.volume desc")
  List<MarketEntity> findActive(@Param("status") MarketStatus status, Pageable pageable);
}

Capa de Servicio con Transacciones

java
@Service
public class MarketService {
  private final MarketRepository repo;

  public MarketService(MarketRepository repo) {
    this.repo = repo;
  }

  @Transactional
  public Market create(CreateMarketRequest request) {
    MarketEntity entity = MarketEntity.from(request);
    MarketEntity saved = repo.save(entity);
    return Market.from(saved);
  }
}

DTOs y Validación

java
public record CreateMarketRequest(
    @NotBlank @Size(max = 200) String name,
    @NotBlank @Size(max = 2000) String description,
    @NotNull @FutureOrPresent Instant endDate,
    @NotEmpty List<@NotBlank String> categories) {}

public record MarketResponse(Long id, String name, MarketStatus status) {
  static MarketResponse from(Market market) {
    return new MarketResponse(market.id(), market.name(), market.status());
  }
}

Manejo de Excepciones

java
@ControllerAdvice
class GlobalExceptionHandler {
  @ExceptionHandler(MethodArgumentNotValidException.class)
  ResponseEntity<ApiError> handleValidation(MethodArgumentNotValidException ex) {
    String message = ex.getBindingResult().getFieldErrors().stream()
        .map(e -> e.getField() + ": " + e.getDefaultMessage())
        .collect(Collectors.joining(", "));
    return ResponseEntity.badRequest().body(ApiError.validation(message));
  }

  @ExceptionHandler(AccessDeniedException.class)
  ResponseEntity<ApiError> handleAccessDenied() {
    return ResponseEntity.status(HttpStatus.FORBIDDEN).body(ApiError.of("Forbidden"));
  }

  @ExceptionHandler(Exception.class)
  ResponseEntity<ApiError> handleGeneric(Exception ex) {
    // Registrar errores inesperados con stack traces
    return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
        .body(ApiError.of("Internal server error"));
  }
}

Caché

Requiere @EnableCaching en una clase de configuración.

java
@Service
public class MarketCacheService {
  private final MarketRepository repo;

  public MarketCacheService(MarketRepository repo) {
    this.repo = repo;
  }

  @Cacheable(value = "market", key = "#id")
  public Market getById(Long id) {
    return repo.findById(id)
        .map(Market::from)
        .orElseThrow(() -> new EntityNotFoundException("Market not found"));
  }

  @CacheEvict(value = "market", key = "#id")
  public void evict(Long id) {}
}

Procesamiento Asíncrono

Requiere @EnableAsync en una clase de configuración.

java
@Service
public class NotificationService {
  @Async
  public CompletableFuture<Void> sendAsync(Notification notification) {
    // enviar email/SMS
    return CompletableFuture.completedFuture(null);
  }
}

Logging (SLF4J)

java
@Service
public class ReportService {
  private static final Logger log = LoggerFactory.getLogger(ReportService.class);

  public Report generate(Long marketId) {
    log.info("generate_report marketId={}", marketId);
    try {
      // lógica
    } catch (Exception ex) {
      log.error("generate_report_failed marketId={}", marketId, ex);
      throw ex;
    }
    return new Report();
  }
}

Middleware / Filtros

java
@Component
public class RequestLoggingFilter extends OncePerRequestFilter {
  private static final Logger log = LoggerFactory.getLogger(RequestLoggingFilter.class);

  @Override
  protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response,
      FilterChain filterChain) throws ServletException, IOException {
    long start = System.currentTimeMillis();
    try {
      filterChain.doFilter(request, response);
    } finally {
      long duration = System.currentTimeMillis() - start;
      log.info("req method={} uri={} status={} durationMs={}",
          request.getMethod(), request.getRequestURI(), response.getStatus(), duration);
    }
  }
}

Paginación y Ordenamiento

java
PageRequest page = PageRequest.of(pageNumber, pageSize, Sort.by("createdAt").descending());
Page<Market> results = marketService.list(page);

Llamadas Externas Resilientes a Errores

java
public <T> T withRetry(Supplier<T> supplier, int maxRetries) {
  int attempts = 0;
  while (true) {
    try {
      return supplier.get();
    } catch (Exception ex) {
      attempts++;
      if (attempts >= maxRetries) {
        throw ex;
      }
      try {
        Thread.sleep((long) Math.pow(2, attempts) * 100L);
      } catch (InterruptedException ie) {
        Thread.currentThread().interrupt();
        throw ex;
      }
    }
  }
}

Limitación de Velocidad (Filtro + Bucket4j)

Nota de Seguridad: La cabecera X-Forwarded-For no es confiable por defecto porque los clientes pueden falsificarla. Solo usar cabeceras reenviadas cuando:

  1. La aplicación está detrás de un proxy inverso de confianza (nginx, AWS ALB, etc.)
  2. Se ha registrado ForwardedHeaderFilter como un bean
  3. Se ha configurado server.forward-headers-strategy=NATIVE o FRAMEWORK en las propiedades de la aplicación
  4. El proxy está configurado para sobrescribir (no agregar) la cabecera X-Forwarded-For

Cuando ForwardedHeaderFilter está correctamente configurado, request.getRemoteAddr() retornará automáticamente la IP correcta del cliente desde las cabeceras reenviadas. Sin esta configuración, usar request.getRemoteAddr() directamente — retorna la IP de la conexión inmediata, que es el único valor confiable.

java
@Component
public class RateLimitFilter extends OncePerRequestFilter {
  private final Map<String, Bucket> buckets = new ConcurrentHashMap<>();

  /*
   * SEGURIDAD: Este filtro usa request.getRemoteAddr() para identificar clientes en la limitación
   * de velocidad.
   *
   * Si la aplicación está detrás de un proxy inverso (nginx, AWS ALB, etc.), se DEBE configurar
   * Spring para manejar correctamente las cabeceras reenviadas:
   *
   * 1. Establecer server.forward-headers-strategy=NATIVE (para plataformas cloud) o FRAMEWORK
   *    en application.properties/yaml
   * 2. Si se usa la estrategia FRAMEWORK, registrar ForwardedHeaderFilter:
   *
   *    @Bean
   *    ForwardedHeaderFilter forwardedHeaderFilter() {
   *        return new ForwardedHeaderFilter();
   *    }
   *
   * 3. Asegurar que el proxy sobrescriba (no agregue) la cabecera X-Forwarded-For para prevenir
   *    falsificación
   * 4. Configurar server.tomcat.remoteip.trusted-proxies o equivalente para el contenedor
   *
   * Sin esta configuración, request.getRemoteAddr() retorna la IP del proxy, no del cliente.
   * NO leer X-Forwarded-For directamente — es trivialmente falsificable sin manejo de proxy confiable.
   */
  @Override
  protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response,
      FilterChain filterChain) throws ServletException, IOException {
    String clientIp = request.getRemoteAddr();

    Bucket bucket = buckets.computeIfAbsent(clientIp,
        k -> Bucket.builder()
            .addLimit(Bandwidth.classic(100, Refill.greedy(100, Duration.ofMinutes(1))))
            .build());

    if (bucket.tryConsume(1)) {
      filterChain.doFilter(request, response);
    } else {
      response.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());
    }
  }
}

Jobs en Segundo Plano

Usar @Scheduled de Spring o integrar con colas (Kafka, SQS, RabbitMQ). Mantener los handlers idempotentes y observables.

Observabilidad

  • Logging estructurado (JSON) mediante Logback encoder
  • Métricas: Micrometer + Prometheus/OTel
  • Trazado: Micrometer Tracing con backend OpenTelemetry o Brave

Configuraciones para Producción

  • Preferir inyección por constructor, evitar inyección por campo
  • Habilitar spring.mvc.problemdetails.enabled=true para errores RFC 7807 (Spring Boot 3+)
  • Configurar tamaños del pool HikariCP para la carga de trabajo, establecer timeouts
  • Usar @Transactional(readOnly = true) para consultas
  • Reforzar null-safety mediante @NonNull y Optional donde corresponda

Recuerda: Mantener los controllers delgados, los servicios enfocados, los repositorios simples y los errores manejados centralmente. Optimizar para mantenibilidad y testabilidad.

Frequently asked questions

What does the Springboot Patterns AI skill do?

Patrones de arquitectura Spring Boot, diseño de API REST, servicios en capas, acceso a datos, caché, procesamiento asíncrono y logging. Usar para trabajo de backend en Java con Spring Boot.

Why use Springboot Patterns on TypingMind?

Because you install it once and use it with any model. Springboot Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Springboot Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/docs/es/skills/springboot-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Springboot Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Springboot Patterns?

As many as you like. As long as a model supports skills, you can use Springboot Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Springboot Patterns AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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