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      "roles_from_db": [
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          "display_name": "Backend Engineer",
          "id": 14,
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        "id": 148,
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        "slug": "service-architecture-and-integration",
        "source": "db"
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      "input_skill": "Microservices",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
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          "role_archetype": null,
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    },
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        "display_name": "Version Control Systems",
        "id": 365,
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        "slug": "d_init_01",
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      "llm_role": null,
      "roles_from_db": []
    },
    {
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        "id": 74,
        "rationale": "Techniques for making analytical data fast and reliable to query. This includes partitioning, clustering, indexing choices, file layout, and access-path tuning for downstream consumers.",
        "slug": "data-access-and-query-optimization",
        "source": "db"
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      "input_skill": "MySQL",
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      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
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      ]
    },
    {
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        "id": 166,
        "rationale": "Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.",
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        "source": "db"
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      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MySQL DBA",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "mysql-dba",
          "source": "db"
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        "display_name": "NoSQL and Cache Stores",
        "id": 145,
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        "source": "db"
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      "input_skill": "MongoDB",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
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      ]
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        "slug": "nosql-and-data-lake-storage",
        "source": "db"
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      "input_skill": "MongoDB",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
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        "slug": "nosql-and-cache-stores",
        "source": "db"
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      "input_skill": "Redis",
      "llm_role": null,
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          "display_name": "Backend Engineer",
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        "id": 146,
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        "slug": "messaging-and-event-streaming",
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        "display_name": "Containerization and Image Delivery",
        "id": 24,
        "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
        "slug": "containerization-and-image-delivery",
        "source": "db"
      },
      "input_skill": "Docker",
      "llm_role": null,
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        "source": "db"
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      "input_skill": "Docker",
      "llm_role": null,
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          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
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        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
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          "rationale": null,
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      "input_skill": "Azure",
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        "slug": "observability-and-alerting",
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        "display_name": "Version Control Systems",
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        "slug": "d_init_01",
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        "display_name": "Version Control Systems",
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        "rationale": "Client-side integration with backend services, including request handling, response parsing, and contract alignment. This cluster is coherent because iOS features frequently depend on stable data exchange with server APIs.",
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              "rationale": null,
              "role_archetype": null,
              "slug": "mlops-engineer",
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            {
              "display_name": "Machine Learning Engineer",
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              "rationale": null,
              "role_archetype": null,
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        {
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        {
          "alias_text": "Kubernetes v1",
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          "alias_text": "k8s",
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    {
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      ],
      "canonical": {
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Test Automation",
          "id": 193,
          "rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
          "slug": "programming-languages-for-test-automation",
          "source": "db"
        },
        "dimension_id": 193,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Automation Tester",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "automation-tester",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Security Automation and Scripting",
          "id": 258,
          "rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
          "slug": "security-automation-and-scripting",
          "source": "db"
        },
        "dimension_id": 258,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Backend Systems",
          "id": 140,
          "rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
          "slug": "programming-languages-for-backend-systems",
          "source": "db"
        },
        "dimension_id": 140,
        "input_skill": "Go",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 679,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 113,
          "rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 113,
        "input_skill": "Go",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 679,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Security Work",
          "id": 328,
          "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
          "slug": "programming-languages-for-security-work",
          "source": "db"
        },
        "dimension_id": 328,
        "input_skill": "Go",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 679,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Express.js",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2668,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Inference Service Frameworks",
          "id": 114,
          "rationale": "Web and service frameworks used to expose model predictions through APIs and application endpoints. This cluster is coherent because MLEs often implement the runtime surface where requests enter and predictions leave the system.",
          "slug": "inference-service-frameworks",
          "source": "db"
        },
        "dimension_id": 114,
        "input_skill": "Spring Boot",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 684,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Service Frameworks",
          "id": 141,
          "rationale": "Server frameworks used to build HTTP APIs, route requests, validate inputs, and structure backend application code. This cluster is coherent because it defines how backend services expose behavior to clients and other services.",
          "slug": "web-service-frameworks",
          "source": "db"
        },
        "dimension_id": 141,
        "input_skill": "Spring Boot",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 684,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Inference Service Frameworks",
          "id": 114,
          "rationale": "Web and service frameworks used to expose model predictions through APIs and application endpoints. This cluster is coherent because MLEs often implement the runtime surface where requests enter and predictions leave the system.",
          "slug": "inference-service-frameworks",
          "source": "db"
        },
        "dimension_id": 114,
        "input_skill": "FastAPI",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 682,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Web Service Frameworks",
          "id": 141,
          "rationale": "Server frameworks used to build HTTP APIs, route requests, validate inputs, and structure backend application code. This cluster is coherent because it defines how backend services expose behavior to clients and other services.",
          "slug": "web-service-frameworks",
          "source": "db"
        },
        "dimension_id": 141,
        "input_skill": "FastAPI",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 682,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Integration",
          "id": 148,
          "rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
          "slug": "service-architecture-and-integration",
          "source": "db"
        },
        "dimension_id": 148,
        "input_skill": "Microservices",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 864,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "PostgreSQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2669,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Access and Query Optimization",
          "id": 74,
          "rationale": "Techniques for making analytical data fast and reliable to query. This includes partitioning, clustering, indexing choices, file layout, and access-path tuning for downstream consumers.",
          "slug": "data-access-and-query-optimization",
          "source": "db"
        },
        "dimension_id": 74,
        "input_skill": "MySQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2670,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
          "id": 166,
          "rationale": "Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.",
          "slug": "mysql-operational-monitoring-logging-and-diagnostics",
          "source": "db"
        },
        "dimension_id": 166,
        "input_skill": "MySQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "MySQL DBA",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "mysql-dba",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2670,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Cache Stores",
          "id": 145,
          "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
          "slug": "nosql-and-cache-stores",
          "source": "db"
        },
        "dimension_id": 145,
        "input_skill": "MongoDB",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 432,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Data Lake Storage",
          "id": 73,
          "rationale": "Non-relational stores and lake storage used for semi-structured, large-scale, or raw data retention. This cluster is coherent because many pipelines land and serve data outside classic relational warehouses.",
          "slug": "nosql-and-data-lake-storage",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "MongoDB",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 432,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Cache Stores",
          "id": 145,
          "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
          "slug": "nosql-and-cache-stores",
          "source": "db"
        },
        "dimension_id": 145,
        "input_skill": "Redis",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 846,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Event Streaming",
          "id": 146,
          "rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
          "slug": "messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 146,
        "input_skill": "Kafka",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 852,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Messaging and Event Streaming",
          "id": 146,
          "rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
          "slug": "messaging-and-event-streaming",
          "source": "db"
        },
        "dimension_id": 146,
        "input_skill": "RabbitMQ",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 853,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Containerization and Image Delivery",
          "id": 24,
          "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
          "slug": "containerization-and-image-delivery",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Docker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model Serving Deployment and Runtime Packaging",
          "id": 52,
          "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
          "slug": "model-serving-deployment-and-runtime-packaging",
          "source": "db"
        },
        "dimension_id": 52,
        "input_skill": "Docker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "MLOps Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "mlops-engineer",
            "source": "db"
          },
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Orchestration Platforms",
          "id": 25,
          "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
          "slug": "orchestration-platforms",
          "source": "db"
        },
        "dimension_id": 25,
        "input_skill": "Kubernetes",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Engineer",
            "id": 18,
            "rationale": null,
            "role_archetype": null,
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