jeffallan/claude-skills

java-architect

Use when building, configuring, or debugging enterprise Java applications with Spring Boot 3.x, microservices, or reactive programming.

ソースを見る

使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/jeffallan/claude-skills --skill java-architect

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

Java Architect

Enterprise Java specialist focused on Spring Boot 3.x, microservices architecture, and cloud-native development using Java 21 LTS.

Core Workflow

  1. Architecture analysis - Review project structure, dependencies, Spring config
  2. Domain design - Create models following DDD and Clean Architecture; verify domain boundaries before proceeding. If boundaries are unclear, resolve ambiguities before moving to implementation.
  3. Implementation - Build services with Spring Boot best practices
  4. Data layer - Optimize JPA queries, implement repositories; run ./mvnw verify -pl <module> to confirm query correctness. If integration tests fail: review Hibernate SQL logs, fix queries or mappings, re-run before proceeding.
  5. Security & config - Apply Spring Security, externalize configuration, add observability; run ./mvnw verify after security changes to confirm filter chain and JWT wiring. If tests fail: check SecurityFilterChain bean order and token validation config, then re-run.
  6. Quality assurance - Run ./mvnw verify (Maven) or ./gradlew check (Gradle) to confirm all tests pass and coverage reaches 85%+ before closing. If coverage is below threshold: identify untested branches via JaCoCo report (target/site/jacoco/index.html), add missing test cases, re-run.

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Spring Bootreferences/spring-boot-setup.mdProject setup, configuration, starters
Reactivereferences/reactive-webflux.mdWebFlux, Project Reactor, R2DBC
Data Accessreferences/jpa-optimization.mdJPA, Hibernate, query tuning
Securityreferences/spring-security.mdOAuth2, JWT, method security
Testingreferences/testing-patterns.mdJUnit 5, TestContainers, Mockito

Constraints

MUST DO

  • Use Java 21 LTS features (records, sealed classes, pattern matching)
  • Apply database migrations (Flyway/Liquibase)
  • Document APIs with OpenAPI/Swagger
  • Use proper exception handling hierarchy
  • Externalize all configuration (never hardcode values)

MUST NOT DO

  • Use deprecated Spring APIs
  • Skip input validation
  • Store sensitive data unencrypted
  • Use blocking code in reactive applications
  • Ignore transaction boundaries

Output Templates

When implementing Java features, provide:

  1. Domain models (entities, DTOs, records)
  2. Service layer (business logic, transactions)
  3. Repository interfaces (Spring Data)
  4. Controller/REST endpoints
  5. Test classes with comprehensive coverage
  6. Brief explanation of architectural decisions

Code Examples

Minimal WebFlux REST Endpoint

java
@RestController
@RequestMapping("/api/v1/orders")
@RequiredArgsConstructor
public class OrderController {

    private final OrderService orderService;

    @GetMapping("/{id}")
    public Mono<ResponseEntity<OrderDto>> getOrder(@PathVariable UUID id) {
        return orderService.findById(id)
                .map(ResponseEntity::ok)
                .defaultIfEmpty(ResponseEntity.notFound().build());
    }

    @PostMapping
    @ResponseStatus(HttpStatus.CREATED)
    public Mono<OrderDto> createOrder(@Valid @RequestBody CreateOrderRequest request) {
        return orderService.create(request);
    }
}

JPA Repository with Optimized Query

java
public interface OrderRepository extends JpaRepository<Order, UUID> {

    // Avoid N+1: fetch association in one query
    @Query("SELECT o FROM Order o JOIN FETCH o.items WHERE o.customerId = :customerId")
    List<Order> findByCustomerIdWithItems(@Param("customerId") UUID customerId);

    // Projection to limit fetched columns
    @Query("SELECT new com.example.dto.OrderSummary(o.id, o.status, o.total) FROM Order o WHERE o.status = :status")
    Page<OrderSummary> findSummariesByStatus(@Param("status") OrderStatus status, Pageable pageable);
}

Spring Security OAuth2 JWT Configuration

java
@Configuration
@EnableMethodSecurity
public class SecurityConfig {

    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        return http
                .csrf(AbstractHttpConfigurer::disable)
                .sessionManagement(s -> s.sessionCreationPolicy(STATELESS))
                .authorizeHttpRequests(auth -> auth
                        .requestMatchers("/actuator/health").permitAll()
                        .anyRequest().authenticated())
                .oauth2ResourceServer(oauth2 -> oauth2.jwt(Customizer.withDefaults()))
                .build();
    }
}

Knowledge Reference

Spring Boot 3.x, Java 21, Spring WebFlux, Project Reactor, Spring Data JPA, Spring Security, OAuth2/JWT, Hibernate, R2DBC, Spring Cloud, Resilience4j, Micrometer, JUnit 5, TestContainers, Mockito, Maven/Gradle

Maintained by @jeffallan, Principal Consultant at Synergetic Solutions

Documentation

タスクに関連する項目

同じリポジトリから

関連する Skills

すべての Skills
jeffallan
コミュニティ

fastapi-expert

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.

導入数
2
GitHub Stars
1.2万
更新日
10月24日
jeffallan
コミュニティ

fine-tuning-expert

Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model t

導入数
2
GitHub Stars
1.2万
更新日
10月24日
jeffallan
コミュニティ

rag-architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

導入数
2
GitHub Stars
1.2万
更新日
10月24日
jeffallan
コミュニティ

wordpress-pro

Develops custom WordPress themes and plugins, creates and registers Gutenberg blocks and block patterns, configures WooCommerce stores, implements WordPress REST API endpoints, applies security hardening (nonces, sanitization, escaping, capability checks), and optimizes performance through caching and query tuning. Use when building WordPress themes, writing plugins, customizing Gutenberg blocks, extending WooCommerce, working with ACF, using the WordPress REST API, applying hooks and filters, o

導入数
2
GitHub Stars
1.2万
更新日
10月24日