Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-boot-crud-patterns
Provides complete CRUD workflows for Spring Boot 3.5+ services using feature-focused architecture. Creates and validates domain aggregates, JPA repositories, application services, and REST controllers with proper separation of concerns. Defer detailed code listings to reference files for progressive disclosure.
Follow this streamlined workflow to deliver feature-aligned CRUD services with explicit validation gates:
Create feature/<name>/ directories with domain, application, presentation, and infrastructure subpackages.
Validate: Verify directory structure matches the feature boundary before proceeding.
Create entity classes with invariants enforced through factory methods (create, update). Keep domain logic framework-free.
Validate: Assert all invariants are covered by unit tests before advancing.
Declare repository interfaces in domain/repository describing persistence contracts without implementation details.
Validate: Confirm interface signatures match domain operations.
Create JPA entities in infrastructure/persistence that map to domain models. Implement Spring Data repositories.
Validate: Run @DataJpaTest to verify entity mapping and repository integration.
Create @Transactional service classes that orchestrate domain operations and DTO mapping.
Validate: Ensure transaction boundaries are correct and optimistic locking is applied where needed.
Use Java records for API contracts with jakarta.validation annotations. Map REST endpoints with proper status codes.
Validate: Test validation constraints and verify HTTP status codes (201 POST, 200 GET, 204 DELETE).
Run integration tests with Testcontainers. Verify migrations (Liquibase/Flyway) mirror the aggregate schema.
Validate: Execute full test suite before deployment; confirm schema migration scripts are applied.
See references/examples-product-feature.md for complete code aligned with each step.
// feature/product/domain/Product.java
package com.example.product.domain;
import java.math.BigDecimal;
import java.time.Instant;
public record Product(
String id,
String name,
String description,
BigDecimal price,
int stock,
Instant createdAt,
Instant updatedAt
) {
public static Product create(String name, String desc, BigDecimal price, int stock) {
if (name == null || name.isBlank()) throw new IllegalArgumentException("Name required");
if (price == null || price.compareTo(BigDecimal.ZERO) < 0) throw new IllegalArgumentException("Invalid price");
return new Product(null, name.trim(), desc, price, stock, Instant.now(), null);
}
public Product withPrice(BigDecimal newPrice) {
return new Product(id, name, description, newPrice, stock, createdAt, Instant.now());
}
}
// feature/product/domain/repository/ProductRepository.java
package com.example.product.domain.repository;
import com.example.product.domain.Product;
import java.util.Optional;
public interface ProductRepository {
Product save(Product product);
Optional<Product> findById(String id);
void deleteById(String id);
}
// feature/product/infrastructure/persistence/ProductJpaEntity.java
package com.example.product.infrastructure.persistence;
import jakarta.persistence.*;
import java.math.BigDecimal;
import java.time.Instant;
@Entity @Table(name = "products")
public class ProductJpaEntity {
@Id @GeneratedValue(strategy = GenerationType.UUID)
private String id;
private String name;
private String description;
private BigDecimal price;
private int stock;
private Instant createdAt;
private Instant updatedAt;
// getters, setters, constructor from domain (omitted for brevity)
}
// feature/product/infrastructure/persistence/JpaProductRepository.java
package com.example.product.infrastructure.persistence;
import com.example.product.domain.Product;
import com.example.product.domain.repository.ProductRepository;
import org.springframework.stereotype.Repository;
@Repository
public class JpaProductRepository implements ProductRepository {
private final SpringDataProductRepository springData;
public JpaProductRepository(SpringDataProductRepository springData) {
this.springData = springData;
}
@Override
public Product save(Product product) {
ProductJpaEntity entity = toEntity(product);
ProductJpaEntity saved = springData.save(entity);
return toDomain(saved);
}
// findById, deleteById implementations...
}
// feature/product/presentation/rest/ProductController.java
package com.example.product.presentation.rest;
import com.example.product.domain.Product;
import com.example.product.domain.repository.ProductRepository;
import jakarta.validation.Valid;
import jakarta.validation.constraints.*;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
@RestController @RequestMapping("/api/products")
public class ProductController {
private final ProductService service;
public ProductController(ProductService service) { this.service = service; }
@PostMapping
public ResponseEntity<ProductResponse> create(@Valid @RequestBody CreateProductRequest req) {
Product product = service.create(req.toDomain());
return ResponseEntity.status(201).body(ProductResponse.from(product));
}
@GetMapping("/{id}")
public ResponseEntity<ProductResponse> getById(@PathVariable String id) {
return service.findById(id)
.map(p -> ResponseEntity.ok(ProductResponse.from(p)))
.orElse(ResponseEntity.notFound().build());
}
@DeleteMapping("/{id}")
public ResponseEntity<Void> delete(@PathVariable String id) {
service.deleteById(id);
return ResponseEntity.noContent().build();
}
// record DTOs
public record CreateProductRequest(
@NotBlank String name,
String description,
@NotNull @DecimalMin("0.01") java.math.BigDecimal price,
@Min(0) int stock
) {
Product toDomain() { return Product.create(name, description, price, stock); }
}
public record ProductResponse(String id, String name, java.math.BigDecimal price) {
static ProductResponse from(Product p) { return new ProductResponse(p.id(), p.name(), p.price()); }
}
}
Create Request:
{
"name": "Wireless Keyboard",
"description": "Ergonomic keyboard",
"price": 79.99,
"stock": 50
}
Created Response (201):
{
"id": "prod-123",
"name": "Wireless Keyboard",
"price": 79.99,
"_links": { "self": "/api/products/prod-123" }
}
Paginated List Request:
curl "http://localhost:8080/api/products?page=0&size=10&sort=name,asc"
python scripts/generate_crud_boilerplate.py --spec entity.json --package com.example.product --output ./generatedUnified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Take giuseppe-trisciuoglio/spring-boot-crud-patterns from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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