spring-data-jpa

giuseppe-trisciuoglio/developer-kit · updated May 22, 2026

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$npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-data-jpa
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summary

Persistence layer patterns for Spring Data JPA repositories, entities, queries, and advanced features.

  • Create repository interfaces extending JpaRepository with derived queries, custom @Query methods, and automatic CRUD operations
  • Configure entity relationships (one-to-one, one-to-many, many-to-many) with appropriate cascade types and fetch strategies
  • Implement pagination, sorting, database auditing with timestamps and user tracking, and transaction management
  • Optimize performance
skill.md

Spring Data JPA

Overview

Provides patterns for Spring Data JPA repositories, entity relationships, queries, pagination, auditing, and transactions.

When to Use

Creating repositories with CRUD operations, entity relationships, @Query annotations, pagination, auditing, or UUID primary keys.

Instructions

Create Repository Interfaces

To implement a repository interface:

  1. Extend the appropriate repository interface:

    @Repository
    public interface UserRepository extends JpaRepository<User, Long> {
        // Custom methods defined here
    }
    
  2. Use derived queries for simple conditions:

    Optional<User> findByEmail(String email);
    List<User> findByStatusOrderByCreatedDateDesc(String status);
    
  3. Implement custom queries with @Query:

    @Query("SELECT u FROM User u WHERE u.status = :status")
    List<User> findActiveUsers(@Param("status") String status);
    

Configure Entities

  1. Define entities with proper annotations:

    @Entity
    @Table(name = "users")
    public class User {
        @Id
        @GeneratedValue(strategy = GenerationType.IDENTITY)
        private Long id;
    
        @Column(nullable = false, length = 100)
        private String email;
    }
    
  2. Configure relationships using appropriate cascade types:

    @OneToMany(mappedBy = "user", cascade = CascadeType.ALL, orphanRemoval = true)
    private List<Order> orders = new ArrayList<>();
    

    Validation: Test cascade behavior with a small dataset before applying to production data. Verify delete operations don't cascade unexpectedly.

  3. Set up database auditing:

    @CreatedDate
    @Column(nullable = false, updatable = false)
    private LocalDateTime createdDate;
    

Apply Query Patterns

  1. Use derived queries for simple conditions
  2. Use @Query for complex queries
  3. Return Optional for single results
  4. Use Pageable for pagination
  5. Apply @Modifying for update/delete operations

Manage Transactions

  1. Mark read-only operations with @Transactional(readOnly = true)
  2. Use explicit transaction boundaries for modifying operations
  3. Specify rollback conditions when needed

Validate and Optimize

1. Verify entity configuration:

  • Test cascade behavior in a transaction before production deployment
  • Validate bidirectional relationships sync correctly

2. Optimize query performance:

  • Run EXPLAIN ANALYZE on queries against large tables
  • If performance issues detected: add indexes → verify with EXPLAIN → repeat
  • Use @EntityGraph to prevent N+1 queries

3. Validate pagination:

  • Ensure indexed columns support pagination queries
  • Test with large datasets to verify cursor stability

Examples

Basic CRUD Repository

@Repository
public interface ProductRepository extends JpaRepository<Product, Long> {
    // Derived query
    List<Product> findByCategory(String category);

    // Custom query
    @Query("SELECT p FROM Product p WHERE p.price > :minPrice")
    List<Product> findExpensiveProducts(@Param("minPrice") BigDecimal minPrice);
}

Pagination Implementation

@Service
public class ProductService {
    private final ProductRepository repository;

    public Page<Product> getProducts(int page, int size) {
        Pageable pageable = PageRequest.of(page, size, Sort.by("name").ascending());
        return repository.findAll(pageable);
    }
}

Entity with Auditing

@Entity
@EntityListeners(AuditingEntityListener.class)
public class Order {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;

    @CreatedDate
    @Column(nullable = false, updatable = false)
    private LocalDateTime createdDate;

    @LastModifiedDate
    private LocalDateTime lastModifiedDate;

    @CreatedBy
    @Column(nullable = false, updatable = false)
    private String createdBy;
}

Best Practices

Entity Design

  • Use constructor injection exclusively (never field injection)
  • Prefer immutable fields with final modifiers
  • Use Java records (16+) or @Value for DTOs
  • Always provide proper @Id and @GeneratedValue annotations
  • Use explicit @Table and @Column annotations

Performance Optimization

  • Use appropriate fetch strategies (LAZY vs EAGER)
  • Implement pagination for large datasets
  • Use database indexes for frequently queried fields
  • Consider using @EntityGraph to avoid N+1 query problems

Reference Documentation

For comprehensive examples, detailed patterns, and advanced configurations, see:

  • Examples - Complete code examples for common scenarios
  • Reference - Detailed patterns and advanced configurations

Constraints and Warnings

  • Never expose JPA entities directly in REST APIs; always use DTOs to prevent lazy loading issues.
  • Avoid N+1 query problems by using @EntityGraph or JOIN FETCH in queries.
  • Be cautious with CascadeType.REMOVE on large collections as it can cause performance issues.
  • Do not use EAGER fetch type for collections; it can cause excessive database queries.
  • Avoid long-running transactions as they can cause database lock contention.
  • Use @Transactional(readOnly = true) for read operations to enable optimizations.
  • Be aware of the first-level cache; entities may not reflect database changes within the same transaction.
  • UUID primary keys can cause index fragmentation; consider using sequential UUIDs or Long IDs.
  • Pagination on large datasets requires proper indexing to avoid full table scans.
how to use spring-data-jpa

How to use spring-data-jpa on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add spring-data-jpa
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill spring-data-jpa

The skills CLI fetches spring-data-jpa from GitHub repository giuseppe-trisciuoglio/developer-kit and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/spring-data-jpa

Reload or restart Cursor to activate spring-data-jpa. Access the skill through slash commands (e.g., /spring-data-jpa) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.860 reviews
  • Dhruvi Jain· Dec 28, 2024

    spring-data-jpa has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Sakura Chawla· Dec 28, 2024

    spring-data-jpa is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Neel Khanna· Dec 28, 2024

    Keeps context tight: spring-data-jpa is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Amelia Patel· Dec 20, 2024

    spring-data-jpa reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Naina Perez· Dec 20, 2024

    Useful defaults in spring-data-jpa — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nikhil Robinson· Dec 16, 2024

    Solid pick for teams standardizing on skills: spring-data-jpa is focused, and the summary matches what you get after install.

  • Chen Brown· Dec 16, 2024

    spring-data-jpa has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Charlotte Ghosh· Dec 12, 2024

    Solid pick for teams standardizing on skills: spring-data-jpa is focused, and the summary matches what you get after install.

  • Neel Ndlovu· Nov 27, 2024

    Solid pick for teams standardizing on skills: spring-data-jpa is focused, and the summary matches what you get after install.

  • Oshnikdeep· Nov 19, 2024

    spring-data-jpa reduced setup friction for our internal harness; good balance of opinion and flexibility.

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