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springboot-verification

Spring Boot 프로젝트를 위한 검증 루프: 빌드, 정적 분석, 테스트 및 커버리지, 보안 스캔, PR 또는 릴리스 전 변경 사항 리뷰를 포함합니다.

springboot-verification란 무엇인가요?

이 스킬은 Spring Boot 프로젝트에 대한 포괄적인 검증 파이프라인을 제공합니다. PR 제출 전, 주요 리팩토링 또는 의존성 업그레이드 후, 스테이징/프로덕션 배포 전에 실행하여 코드 품질과 안정성을 보장합니다. 빌드, 정적 분석(SpotBugs, PMD, Checkstyle), 단위/통합/API 테스트 및 JaCoCo 커버리지 보고서, OWASP Dependency-Check를 이용한 보안 스캔, secrets 스캔, 포매팅 검사, diff 리뷰 등 6단계로 구성됩니다. Maven과 Gradle을 모두 지원하며, 각 단계별 구체적인 명령어와 예제 코드(단위 테스트, Testcontainers 통합 테스트, MockMvc API 테스트)를 제공합니다. 최종 출력 템플릿을 통해 전체 검증 상태를 한눈에 파악할 수 있으며, 지속적 모드에서 빠른 피드백 루프를 유지하도록 설계되었습니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/springboot-verification

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

Spring Boot 검증 루프 (Verification Loop)

PR 전, 주요 변경 사항 후, 배포 전에 실행하세요.

활성화 시점

  • Spring Boot 서비스의 풀 리퀘스트를 열기 전
  • 주요 리팩토링 또는 의존성 업그레이드 후
  • 스테이징 또는 프로덕션을 위한 배포 전 검증
  • 전체 빌드 → 린트 → 테스트 → 보안 스캔 파이프라인 실행
  • 테스트 커버리지가 임계값을 충족하는지 검증할 때

1단계: 빌드 (Build)

mvn -T 4 clean verify -DskipTests
# 또는
./gradlew clean assemble -x test

빌드가 실패하면 중단하고 수정하세요.

2단계: 정적 분석 (Static Analysis)

Maven (일반적인 플러그인):

mvn -T 4 spotbugs:check pmd:check checkstyle:check

Gradle (설정된 경우):

./gradlew checkstyleMain pmdMain spotbugsMain

3단계: 테스트 + 커버리지 (Tests + Coverage)

mvn -T 4 test
mvn jacoco:report   # 80% 이상 커버리지 확인
# 또는
./gradlew test jacocoTestReport

보고서:

  • 전체 테스트 수, 통과/실패
  • 커버리지 % (라인/브랜치)

단위 테스트 (Unit Tests)

모의 객체(Mocked Dependencies)로 서비스 로직을 격리하여 테스트:

@ExtendWith(MockitoExtension.class)
class UserServiceTest {

  @Mock private UserRepository userRepository;
  @InjectMocks private UserService userService;

  @Test
  void createUser_validInput_returnsUser() {
    var dto = new CreateUserDto("Alice", "[email protected]");
    var expected = new User(1L, "Alice", "[email protected]");
    when(userRepository.save(any(User.class))).thenReturn(expected);

    var result = userService.create(dto);

    assertThat(result.name()).isEqualTo("Alice");
    verify(userRepository).save(any(User.class));
  }

  @Test
  void createUser_duplicateEmail_throwsException() {
    var dto = new CreateUserDto("Alice", "[email protected]");
    when(userRepository.existsByEmail(dto.email())).thenReturn(true);

    assertThatThrownBy(() -> userService.create(dto))
        .isInstanceOf(DuplicateEmailException.class);
  }
}

Testcontainers를 사용한 통합 테스트 (Integration Tests with Testcontainers)

H2 대신 실제 데이터베이스로 테스트:

@SpringBootTest
@Testcontainers
class UserRepositoryIntegrationTest {

  @Container
  static PostgreSQLContainer<?> postgres = new PostgreSQLContainer<>("postgres:16-alpine")
      .withDatabaseName("testdb");

  @DynamicPropertySource
  static void configureProperties(DynamicPropertyRegistry registry) {
    registry.add("spring.datasource.url", postgres::getJdbcUrl);
    registry.add("spring.datasource.username", postgres::getUsername);
    registry.add("spring.datasource.password", postgres::getPassword);
  }

  @Autowired private UserRepository userRepository;

  @Test
  void findByEmail_existingUser_returnsUser() {
    userRepository.save(new User("Alice", "[email protected]"));

    var found = userRepository.findByEmail("[email protected]");

    assertThat(found).isPresent();
    assertThat(found.get().getName()).isEqualTo("Alice");
  }
}

MockMvc를 사용한 API 테스트 (API Tests with MockMvc)

전체 Spring 컨텍스트로 컨트롤러 레이어 테스트:

@WebMvcTest(UserController.class)
class UserControllerTest {

  @Autowired private MockMvc mockMvc;
  @MockBean private UserService userService;

  @Test
  void createUser_validInput_returns201() throws Exception {
    var user = new UserDto(1L, "Alice", "[email protected]");
    when(userService.create(any())).thenReturn(user);

    mockMvc.perform(post("/api/users")
            .contentType(MediaType.APPLICATION_JSON)
            .content("""
                {"name": "Alice", "email": "[email protected]"}
                """))
        .andExpect(status().isCreated())
        .andExpect(jsonPath("$.name").value("Alice"));
  }

  @Test
  void createUser_invalidEmail_returns400() throws Exception {
    mockMvc.perform(post("/api/users")
            .contentType(MediaType.APPLICATION_JSON)
            .content("""
                {"name": "Alice", "email": "not-an-email"}
                """))
        .andExpect(status().isBadRequest());
  }
}

4단계: 보안 스캔 (Security Scan)

# 의존성 CVE
mvn org.owasp:dependency-check-maven:check
# 또는
./gradlew dependencyCheckAnalyze

# 소스 내 비밀키 검색 (Secrets in source)
grep -rn "password\s*=\s*\"" src/ --include="*.java" --include="*.yml" --include="*.properties"
grep -rn "sk-\|api_key\|secret" src/ --include="*.java" --include="*.yml"

# Git 히스토리에서 비밀키 검색 (Secrets in git history)
git secrets --scan  # 설정된 경우

일반적인 보안 발견 사항 (Common Security Findings)

# System.out.println 확인 (로거를 사용하세요)
grep -rn "System\.out\.print" src/main/ --include="*.java"

# 응답에 원시 예외 메시지가 포함되었는지 확인
grep -rn "e\.getMessage()" src/main/ --include="*.java"

# 와일드카드 CORS 확인
grep -rn "allowedOrigins.*\*" src/main/ --include="*.java"

5단계: 린트/포맷 (Lint/Format) (선택적 게이트)

mvn spotless:apply   # Spotless 플러그인을 사용하는 경우
./gradlew spotlessApply

6단계: Diff 리뷰 (Diff Review)

git diff --stat
git diff

체크리스트:

  • 디버깅 로그가 남아있지 않은지 (System.out, 가드 없는 log.debug)
  • 의미 있는 오류 메시지와 HTTP 상태 코드
  • 트랜잭션 및 검증이 필요한 곳에 적용되었는지
  • 설정 변경 사항이 문서화되었는지

출력 템플릿 (Output Template)

검증 보고서
===================
빌드:     [PASS/FAIL]
정적 분석: [PASS/FAIL] (spotbugs/pmd/checkstyle)
테스트:   [PASS/FAIL] (X/Y 통과, Z% 커버리지)
보안:     [PASS/FAIL] (CVE 발견: N개)
Diff:     [X개 파일 변경]

전체:     [준비됨 / 준비 안됨]

수정해야 할 이슈:
1. ...
2. ...

지속 모드 (Continuous Mode)

  • 큰 변경 사항이 있을 때마다 또는 긴 세션에서 30~60분마다 단계를 재실행
  • 빠른 피드백을 위해 짧은 루프 유지: mvn -T 4 test + spotbugs

기억하세요: 빠른 피드백이 늦은 놀라움보다 낫습니다. 게이트를 엄격하게 유지하세요—프로덕션 시스템에서는 경고를 결함으로 취급하십시오.

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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