JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, and performance tuning in Spring Boot.
Works with
Covers entity mapping with auditing, soft deletes, indexing, and enumerated types; includes transaction management with read-only optimization and propagation strategies
Provides N+1 prevention techniques using lazy loading, JOIN FETCH queries, and DTO projections for lightweight reads
Demonstrates pagination with Pageable, custom repository methods, and c
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionjpa-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches jpa-patterns from affaan-m/everything-claude-code and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate jpa-patterns. Access via /jpa-patterns in your agent's command palette.
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.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Use for data modeling, repositories, and performance tuning in Spring Boot.
@Entity
@Table(name = "markets", indexes = {
@Index(name = "idx_markets_slug", columnList = "slug", unique = true)
})
@EntityListeners(AuditingEntityListener.class)
public class MarketEntity {
@Id @GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
@Column(nullable = false, length = 200)
private String name;
@Column(nullable = false, unique = true, length = 120)
private String slug;
@Enumerated(EnumType.STRING)
private MarketStatus status = MarketStatus.ACTIVE;
@CreatedDate private Instant createdAt;
@LastModifiedDate private Instant updatedAt;
}
Enable auditing:
@Configuration
@EnableJpaAuditing
class JpaConfig {}
@OneToMany(mappedBy = "market", cascade = CascadeType.ALL, orphanRemoval = true)
private List<PositionEntity> positions = new ArrayList<>();
JOIN FETCH in queries when neededEAGER on collections; use DTO projections for read paths@Query("select m from MarketEntity m left join fetch m.positions where m.id = :id")
Optional<MarketEntity> findWithPositions(@Param("id") Long id);
public interface MarketRepository extends JpaRepository<MarketEntity, Long> {
Optional<MarketEntity> findBySlug(String slug);
@Query("select m from MarketEntity m where m.status = :status")
Page<MarketEntity> findByStatus(@Param("status") MarketStatus status, Pageable pageable);
}
public interface MarketSummary {
Long getId();
String getName();
MarketStatus getStatus();
}
Page<MarketSummary> findAllBy(Pageable pageable);
@Transactional@Transactional(readOnly = true) for read paths to optimize@Transactional
public Market updateStatus(Long id, MarketStatus status) {
MarketEntity entity = repo.findById(id)
.orElseThrow(() -> new EntityNotFoundException("Market"));
entity.setStatus(status);
return Market.from(entity);
}
PageRequest page = PageRequest.of(pageNumber, pageSize, Sort.by("createdAt").descending());
Page<MarketEntity> markets = repo.findByStatus(MarketStatus.ACTIVE, page);
For cursor-like pagination, include id > :lastId in JPQL with ordering.
status, slug, foreign keys)status, created_at)select *; project only needed columnssaveAll and hibernate.jdbc.batch_sizeRecommended properties:
spring.datasource.hikari.maximum-pool-size=20
spring.datasource.hikari.minimum-idle=5
spring.datasource.hikari.connection-timeout=30000
spring.datasource.hikari.validation-timeout=5000
For PostgreSQL LOB handling, add:
spring.jpa.properties.hibernate.jdbc.lob.non_contextual_creation=true
@DataJpaTest with Testcontainers to mirror productionlogging.level.org.hibernate.SQL=DEBUG and logging.level.org.hibernate.orm.jdbc.bind=TRACE for parameter valuesRemember: Keep entities lean, queries intentional, and transactions short. Prevent N+1 with fetch strategies and projections, and index for your read/write paths.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in jpa-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added jpa-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added jpa-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in jpa-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for jpa-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
jpa-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend jpa-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
jpa-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: jpa-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
jpa-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
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