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springboot-tdd

Desarrollo guiado por pruebas para Spring Boot con JUnit 5, Mockito, MockMvc, Testcontainers y JaCoCo. Úsalo al agregar funcionalidades, corregir errores o refactorizar.

¿Qué es springboot-tdd?

Este skill define un flujo de trabajo de Desarrollo Guiado por Pruebas (TDD) para servicios Spring Boot, con el objetivo de alcanzar una cobertura de código superior al 80% combinando pruebas unitarias y de integración. Está diseñado para ayudar a desarrolladores y agentes de IA a escribir primero las pruebas, implementar el código mínimo necesario para que pasen y luego refactorizar de forma segura. Utiliza herramientas clave del ecosistema Spring: JUnit 5 y Mockito para pruebas unitarias, MockMvc para pruebas de capa web, `@SpringBootTest` para integración, `@DataJpaTest` para persistencia y Testcontainers para replicar entornos de producción con contenedores de Postgres/Redis. Además, integra JaCoCo para medir y hacer cumplir la cobertura de código, fomentando el uso de AssertJ para aserciones legibles y constructores de datos de prueba para mantener las pruebas limpias y mantenibles. El skill incluye ejemplos de código concretos para cada tipo de prueba, comandos de CI (Maven y Gradle) y patrones como Arrange-Act-Assert, evitando mocks parciales y priorizando el stubbing explícito.

Compatible con~Claude Code~Codex CLI~Cursor
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Documentación

Flujo de trabajo TDD para Spring Boot

Guía de TDD para servicios Spring Boot con cobertura superior al 80% (unidad + integración).

Cuándo usarlo

  • Nuevas funcionalidades o endpoints
  • Corrección de errores o refactorizaciones
  • Adición de lógica de acceso a datos o reglas de seguridad

Flujo de trabajo

  1. Escribir las pruebas primero (deben fallar)
  2. Implementar el código mínimo necesario para que pasen
  3. Refactorizar con las pruebas en verde
  4. Hacer cumplir la cobertura (JaCoCo)

Pruebas unitarias (JUnit 5 + Mockito)

@ExtendWith(MockitoExtension.class)
class MarketServiceTest {
  @Mock MarketRepository repo;
  @InjectMocks MarketService service;

  @Test
  void createsMarket() {
    CreateMarketRequest req = new CreateMarketRequest("name", "desc", Instant.now(), List.of("cat"));
    when(repo.save(any())).thenAnswer(inv -> inv.getArgument(0));

    Market result = service.create(req);

    assertThat(result.name()).isEqualTo("name");
    verify(repo).save(any());
  }
}

Patrones:

  • Arrange-Act-Assert
  • Evitar mocks parciales; preferir stubbing explícito
  • Usar @ParameterizedTest para variantes

Pruebas de capa web (MockMvc)

@WebMvcTest(MarketController.class)
class MarketControllerTest {
  @Autowired MockMvc mockMvc;
  @MockBean MarketService marketService;

  @Test
  void returnsMarkets() throws Exception {
    when(marketService.list(any())).thenReturn(Page.empty());

    mockMvc.perform(get("/api/markets"))
        .andExpect(status().isOk())
        .andExpect(jsonPath("$.content").isArray());
  }
}

Pruebas de integración (SpringBootTest)

@SpringBootTest
@AutoConfigureMockMvc
@ActiveProfiles("test")
class MarketIntegrationTest {
  @Autowired MockMvc mockMvc;

  @Test
  void createsMarket() throws Exception {
    mockMvc.perform(post("/api/markets")
        .contentType(MediaType.APPLICATION_JSON)
        .content("""
          {"name":"Test","description":"Desc","endDate":"2030-01-01T00:00:00Z","categories":["general"]}
        """))
      .andExpect(status().isCreated());
  }
}

Pruebas de persistencia (DataJpaTest)

@DataJpaTest
@AutoConfigureTestDatabase(replace = AutoConfigureTestDatabase.Replace.NONE)
@Import(TestContainersConfig.class)
class MarketRepositoryTest {
  @Autowired MarketRepository repo;

  @Test
  void savesAndFinds() {
    MarketEntity entity = new MarketEntity();
    entity.setName("Test");
    repo.save(entity);

    Optional<MarketEntity> found = repo.findByName("Test");
    assertThat(found).isPresent();
  }
}

Testcontainers

  • Usar contenedores reutilizables para Postgres/Redis que reflejen el entorno de producción
  • Conectarlos mediante @DynamicPropertySource para inyectar las URLs JDBC en el contexto de Spring

Cobertura (JaCoCo)

Fragmento de Maven:

<plugin>
  <groupId>org.jacoco</groupId>
  <artifactId>jacoco-maven-plugin</artifactId>
  <version>0.8.14</version>
  <executions>
    <execution>
      <goals><goal>prepare-agent</goal></goals>
    </execution>
    <execution>
      <id>report</id>
      <phase>verify</phase>
      <goals><goal>report</goal></goals>
    </execution>
  </executions>
</plugin>

Aserciones

  • Preferir AssertJ (assertThat) por legibilidad
  • Para respuestas JSON, usar jsonPath
  • Para excepciones: assertThatThrownBy(...)

Constructores de datos de prueba

class MarketBuilder {
  private String name = "Test";
  MarketBuilder withName(String name) { this.name = name; return this; }
  Market build() { return new Market(null, name, MarketStatus.ACTIVE); }
}

Comandos de CI

  • Maven: mvn -T 4 test o mvn verify
  • Gradle: ./gradlew test jacocoTestReport

Recuerda: Mantén las pruebas rápidas, aisladas y deterministas. Prueba el comportamiento, no los detalles de implementación.

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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