Tracktory · jwon0523 · Jun 3, 2026 · Jun 3, 2026 · Jun 3, 2026
diff --git a/src/main/java/com/hansung/tracktory/domain/recommendation/dto/RecommendationResponse.java b/src/main/java/com/hansung/tracktory/domain/recommendation/dto/RecommendationResponse.java
@@ -1,5 +1,6 @@
 package com.hansung.tracktory.domain.recommendation.dto;
 
+import java.math.BigDecimal;
 import java.util.List;
 
 /**
@@ -16,10 +17,16 @@ public record RecommendationResponse(
 
   /**
    * 추천 직무 한 건. {@code score} 는 사용자 체감 척도로 보정한 표시 점수(0~100, 하한 위로 끌어올린 값이며 내부 저장 점수와는 다름), {@code
-   * techStacks} 는 카탈로그가 보유한 직무 요구 기술 스택 이름 목록(없으면 빈 리스트).
+   * techStacks} 는 카탈로그가 보유한 직무 요구 기술 스택 이름 목록(없으면 빈 리스트), {@code competencyTags} 는 AI 가 직무 카드 표시용으로
+   * 돌려준 역량 태그 목록.
    */
   public record JobView(
-      String code, String name, Integer score, String reasoning, List<String> techStacks) {}
+      String code,
+      String name,
+      Integer score,
+      String reasoning,
+      List<String> techStacks,
+      List<String> competencyTags) {}
 
   /** 트랙 추천 — 주 추천 2개 + 보조 추천 다수, 최상위 조합의 시너지 요약을 포함한다. */
   public record TrackRecommendationView(
@@ -54,11 +61,13 @@ public record SemesterView(
 
   /**
    * 학기 내 과목. {@code timing} 은 PAST/CURRENT/FUTURE, {@code completed} 는 이수 여부, {@code score} 는 미래 추천
-   * 과목의 적합도(0~100, 과거 과목은 null).
+   * 과목의 적합도(0~100, 과거 과목은 null), {@code credits}/{@code description} 은 카탈로그 과목 상세 정보.
    */
   public record CourseView(
       String code,
       String name,
+      BigDecimal credits,
+      String description,
       String timing,
       boolean completed,
       Integer score,

diff --git a/src/main/java/com/hansung/tracktory/domain/recommendation/entity/RecommendedJob.java b/src/main/java/com/hansung/tracktory/domain/recommendation/entity/RecommendedJob.java
@@ -2,16 +2,21 @@
 
 import com.hansung.tracktory.domain.catalog.career.entity.Job;
 import com.hansung.tracktory.global.entity.BaseEntity;
+import jakarta.persistence.CollectionTable;
 import jakarta.persistence.Column;
+import jakarta.persistence.ElementCollection;
 import jakarta.persistence.Entity;
 import jakarta.persistence.FetchType;
 import jakarta.persistence.GeneratedValue;
 import jakarta.persistence.GenerationType;
 import jakarta.persistence.Id;
 import jakarta.persistence.JoinColumn;
 import jakarta.persistence.ManyToOne;
+import jakarta.persistence.OrderColumn;
 import jakarta.persistence.Table;
 import jakarta.persistence.UniqueConstraint;
+import java.util.ArrayList;
+import java.util.List;
 import lombok.Builder;
 import lombok.Getter;
 import lombok.NoArgsConstructor;
@@ -35,6 +40,14 @@ public class RecommendedJob extends BaseEntity {
   @Column(columnDefinition = "text")
   private String reasoning;
 
+  @ElementCollection
+  @CollectionTable(
+      name = "recommended_job_competency_tag",
+      joinColumns = @JoinColumn(name = "recommended_job_id"))
+  @OrderColumn(name = "tag_order")
+  @Column(name = "tag", nullable = false)
+  private List<String> competencyTags = new ArrayList<>();
+
   @ManyToOne(fetch = FetchType.LAZY)
   @JoinColumn(name = "recommendation_id", nullable = false)
   private Recommendation recommendation;
@@ -44,9 +57,11 @@ public class RecommendedJob extends BaseEntity {
   private Job job;
 
   @Builder
-  public RecommendedJob(Integer score, String reasoning, Job job) {
+  public RecommendedJob(Integer score, String reasoning, List<String> competencyTags, Job job) {
     this.score = score;
     this.reasoning = reasoning;
+    this.competencyTags =
+        competencyTags == null ? new ArrayList<>() : new ArrayList<>(competencyTags);
     this.job = job;
   }
 

diff --git a/...ain/java/com/hansung/tracktory/domain/recommendation/service/RecommendationAssembler.java b/...ain/java/com/hansung/tracktory/domain/recommendation/service/RecommendationAssembler.java
@@ -52,6 +52,9 @@ public class RecommendationAssembler {
   /** 트랙별 주요 과목으로 노출할 전공필수 과목 수. */
   private static final int MAIN_SUBJECT_LIMIT = 3;
 
+  /** 직무 카드가 과도하게 커지지 않도록 AI 가 정렬해 준 역량 태그 중 상위 N 개만 노출한다. */
+  private static final int COMPETENCY_TAG_LIMIT = 10;
+
   private final SubjectRepository subjectRepository;
   private final SubjectPrerequisiteRepository subjectPrerequisiteRepository;
   private final TrackSubjectRepository trackSubjectRepository;
@@ -78,10 +81,15 @@ private List<JobView> jobs(Recommendation recommendation) {
                     // 저장된 내부 점수는 그대로 두고 응답 노출값만 체감 척도로 보정한다.
                     JobScoreCalibrator.toDisplayScore(j.getScore()),
                     j.getReasoning(),
-                    techStacks.getOrDefault(j.getJob().getId(), List.of())))
+                    techStacks.getOrDefault(j.getJob().getId(), List.of()),
+                    topCompetencyTags(j.getCompetencyTags())))
         .toList();
   }
 
+  private List<String> topCompetencyTags(List<String> competencyTags) {
+    return competencyTags.stream().limit(COMPETENCY_TAG_LIMIT).toList();
+  }
+
   private Map<Long, List<String>> techStackIndex(List<Job> jobs) {
     Map<Long, List<String>> index = new LinkedHashMap<>();
     if (jobs.isEmpty()) {
@@ -198,6 +206,8 @@ private SemesterView pastSemester(
                     new CourseView(
                         s.getCode(),
                         s.getName(),
+                        s.getCredit(),
+                        s.getDescription(),
                         timing,
                         true,
                         null,
@@ -219,6 +229,8 @@ private SemesterView futureSemester(
                     new CourseView(
                         item.getSubject().getCode(),
                         item.getSubject().getName(),
+                        item.getSubject().getCredit(),
+                        item.getSubject().getDescription(),
                         timing,
                         false,
                         item.getScore(),

diff --git a/src/main/java/com/hansung/tracktory/domain/recommendation/service/RecommendationService.java b/src/main/java/com/hansung/tracktory/domain/recommendation/service/RecommendationService.java
@@ -147,6 +147,7 @@ private void addJobs(Recommendation recommendation, AiRecommendResponse ai) {
                       RecommendedJob.builder()
                           .score(percent(job.matchScore()))
                           .reasoning(reasoning)
+                          .competencyTags(nullSafe(job.competencyTags()))
                           .job(catalogJob)
                           .build()));
     }

diff --git a/src/test/java/com/hansung/tracktory/domain/recommendation/ai/AiEnvelopeContractTest.java b/src/test/java/com/hansung/tracktory/domain/recommendation/ai/AiEnvelopeContractTest.java
@@ -76,6 +76,7 @@ void deserializesRealFastApiEnvelopeWithoutFieldLoss() {
 
     JobCandidate job = data.jobs().get(0);
     assertThat(job.jobId()).isEqualTo("ml_engineer");
+    assertThat(job.competencyTags()).containsExactly("문제해결");
     assertThat(job.matchScore()).isEqualTo(0.91);
     assertThat(job.fallbackUsed()).isFalse();
 

diff --git a/...java/com/hansung/tracktory/domain/recommendation/service/RecommendationAssemblerTest.java b/...java/com/hansung/tracktory/domain/recommendation/service/RecommendationAssemblerTest.java
@@ -33,6 +33,7 @@
 import com.hansung.tracktory.domain.recommendation.entity.RoadmapSemester;
 import com.hansung.tracktory.domain.recommendation.onboarding.OnboardingProfileSnapshot;
 import com.hansung.tracktory.domain.recommendation.onboarding.OnboardingProfileSnapshot.CompletedCourse;
+import java.math.BigDecimal;
 import java.util.List;
 import java.util.Optional;
 import org.junit.jupiter.api.Test;
@@ -99,6 +100,8 @@ void assemble_prependsReconstructedPastSemestersBeforeFutureWithTiming() {
     assertThat(first.timing()).isEqualTo("PAST");
     assertThat(first.courses()).hasSize(1);
     assertThat(first.courses().get(0).code()).isEqualTo("W1");
+    assertThat(first.courses().get(0).credits()).isEqualByComparingTo("3.0");
+    assertThat(first.courses().get(0).description()).isEqualTo("자료구조 설명");
     assertThat(first.courses().get(0).completed()).isTrue();
     assertThat(first.courses().get(0).score()).isNull();
 
@@ -114,21 +117,42 @@ void assemble_prependsReconstructedPastSemestersBeforeFutureWithTiming() {
     assertThat(third.stage()).isEqualTo(SubjectStage.APPLIED.name());
     assertThat(third.timing()).isEqualTo("FUTURE");
     assertThat(third.courses().get(0).code()).isEqualTo("W3");
+    assertThat(third.courses().get(0).credits()).isEqualByComparingTo("3.0");
+    assertThat(third.courses().get(0).description()).isEqualTo("캡스톤 설명");
     assertThat(third.courses().get(0).completed()).isFalse();
     assertThat(third.courses().get(0).score()).isEqualTo(2);
   }
 
   @Test
-  void assemble_populatesJobTechStacksFromCatalogSortedByName() {
+  void assemble_populatesJobTechStacksFromCatalogSortedByNameAndLimitsCompetencyTags() {
     Job job = mock(Job.class);
     given(job.getId()).willReturn(10L);
     given(job.getCode()).willReturn("DE");
     given(job.getName()).willReturn("데이터 엔지니어");
+    List<String> competencyTags =
+        List.of(
+            "Docker",
+            "S3",
+            "Grafana",
+            "Flink",
+            "Prometheus",
+            "dbt",
+            "BigQuery",
+            "PostgreSQL",
+            "Vue.js",
+            "Apache",
+            "CI/CD",
+            "Hadoop");
 
     Recommendation recommendation =
         Recommendation.builder().status(RecommendationStatus.ACTIVE).build();
     recommendation.addRecommendedJob(
-        RecommendedJob.builder().score(90).reasoning("이유").job(job).build());
+        RecommendedJob.builder()
+            .score(90)
+            .reasoning("이유")
+            .competencyTags(competencyTags)
+            .job(job)
+            .build());
 
     JobTechStack spark = jobTechStack(job, "Spark");
     JobTechStack airflow = jobTechStack(job, "Airflow");
@@ -159,6 +183,18 @@ void assemble_populatesJobTechStacksFromCatalogSortedByName() {
     assertThat(view.score()).isEqualTo(96);
     assertThat(view.reasoning()).isEqualTo("이유");
     assertThat(view.techStacks()).containsExactly("Airflow", "Spark");
+    assertThat(view.competencyTags())
+        .containsExactly(
+            "Docker",
+            "S3",
+            "Grafana",
+            "Flink",
+            "Prometheus",
+            "dbt",
+            "BigQuery",
+            "PostgreSQL",
+            "Vue.js",
+            "Apache");
   }
 
   @Test
@@ -284,6 +320,8 @@ private static Subject subject(long id, String code, String name) {
     given(subject.getId()).willReturn(id);
     given(subject.getCode()).willReturn(code);
     given(subject.getName()).willReturn(name);
+    given(subject.getCredit()).willReturn(new BigDecimal("3.0"));
+    given(subject.getDescription()).willReturn(name + " 설명");
     return subject;
   }
 

diff --git a/...t/java/com/hansung/tracktory/domain/recommendation/service/RecommendationServiceTest.java b/...t/java/com/hansung/tracktory/domain/recommendation/service/RecommendationServiceTest.java
@@ -146,6 +146,8 @@ void generate_freshWhenNoActive_callsAiSupersedesSavesAndMaps() {
     assertThat(saved.getRecommendedJobs().get(0).getScore()).isEqualTo(90);
     assertThat(saved.getRecommendedJobs().get(0).getJob().getCode()).isEqualTo("be_dev");
     assertThat(saved.getRecommendedJobs().get(0).getReasoning()).isEqualTo("직무 설명");
+    assertThat(saved.getRecommendedJobs().get(0).getCompetencyTags())
+        .containsExactly("API 설계", "트랜잭션");
 
     assertThat(saved.getRecommendedTracks()).hasSize(4);
     List<RecommendedTrack> primaries =
@@ -219,8 +221,9 @@ void generate_jobsFoldingToSameCatalogCode_dedupedToSingleRecommendedJob() {
     AiRecommendResponse ai =
         new AiRecommendResponse(
             List.of(
-                new JobCandidate("DE", "데이터 엔지니어", List.of(), List.of(), 0.9, 0.8, false),
-                new JobCandidate("DE", "데이터 분석가", List.of(), List.of(), 0.5, 0.4, false)),
+                new JobCandidate(
+                    "DE", "데이터 엔지니어", List.of(), List.of("데이터 파이프라인"), 0.9, 0.8, false),
+                new JobCandidate("DE", "데이터 분석가", List.of(), List.of("분석 모델링"), 0.5, 0.4, false)),
             List.of(),
             List.of(),
             null,
@@ -251,6 +254,7 @@ void generate_jobsFoldingToSameCatalogCode_dedupedToSingleRecommendedJob() {
     assertThat(saved.getRecommendedJobs()).hasSize(1);
     assertThat(saved.getRecommendedJobs().get(0).getJob().getCode()).isEqualTo("DE");
     assertThat(saved.getRecommendedJobs().get(0).getScore()).isEqualTo(90);
+    assertThat(saved.getRecommendedJobs().get(0).getCompetencyTags()).containsExactly("데이터 파이프라인");
   }
 
   @Test
@@ -397,7 +401,9 @@ private static AiRecommendResponse sampleAiResponse() {
             List.of());
 
     return new AiRecommendResponse(
-        List.of(new JobCandidate("be_dev", "백엔드 개발자", List.of(), List.of(), 0.9, 0.8, false)),
+        List.of(
+            new JobCandidate(
+                "be_dev", "백엔드 개발자", List.of(), List.of("API 설계", "트랜잭션"), 0.9, 0.8, false)),
         List.of(primary),
         List.of(secondaryCross, secondaryMmr),
         roadmap,