spring-data-jpa
Persistence layer patterns for Spring Data JPA repositories, entities, queries, and advanced features.
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What it does
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
Installation Guide
How to use spring-data-jpa on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
spring-data-jpa
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches spring-data-jpa from giuseppe-trisciuoglio/developer-kit and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate spring-data-jpa. Access via /spring-data-jpa in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
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:
-
Extend the appropriate repository interface:
@Repository public interface UserRepository extends JpaRepository<User, Long> { // Custom methods defined here } -
Use derived queries for simple conditions:
Optional<User> findByEmail(String email); List<User> findByStatusOrderByCreatedDateDesc(String status); -
Implement custom queries with
@Query:@Query("SELECT u FROM User u WHERE u.status = :status") List<User> findActiveUsers(@Param("status") String status);
Configure Entities
-
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; } -
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.
-
Set up database auditing:
@CreatedDate @Column(nullable = false, updatable = false) private LocalDateTime createdDate;
Apply Query Patterns
- Use derived queries for simple conditions
- Use
@Query for complex queries - Return Optional for single results
- Use Pageable for pagination
- Apply
@Modifying for update/delete operations
Manage Transactions
- Mark read-only operations with
@Transactional(readOnly = true) - Use explicit transaction boundaries for modifying operations
- 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 ANALYZEon queries against large tables - If performance issues detected: add indexes → verify with EXPLAIN → repeat
- Use
@EntityGraphto 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
finalmodifiers - Use Java records (16+) or
@Valuefor DTOs - Always provide proper
@Idand@GeneratedValueannotations - Use explicit
@Tableand@Columnannotations
Performance Optimization
- Use appropriate fetch strategies (LAZY vs EAGER)
- Implement pagination for large datasets
- Use database indexes for frequently queried fields
- Consider using
@EntityGraphto 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
@EntityGraphorJOIN FETCHin queries. - Be cautious with
CascadeType.REMOVEon large collections as it can cause performance issues. - Do not use
EAGERfetch 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.
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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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- DDhruvi Jain★★★★★Dec 28, 2024
spring-data-jpa has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SSakura Chawla★★★★★Dec 28, 2024
spring-data-jpa is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNeel 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.
- AAmelia Patel★★★★★Dec 20, 2024
spring-data-jpa reduced setup friction for our internal harness; good balance of opinion and flexibility.
- NNaina 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.
- NNikhil 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.
- CChen Brown★★★★★Dec 16, 2024
spring-data-jpa has been reliable in day-to-day use. Documentation quality is above average for community skills.
- CCharlotte 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.
- NNeel 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.
- OOshnikdeep★★★★★Nov 19, 2024
spring-data-jpa reduced setup friction for our internal harness; good balance of opinion and flexibility.
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