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        "source": "db"
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        "source": "db"
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        "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.",
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      "skill": "Azure Machine Learning",
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    {
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        "dimension_id": 332,
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        "roles_from_db": [
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            "slug": "cybersecurity-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 164,
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      {
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        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Analytical Programming Languages",
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          "rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
          "slug": "analytical-programming-languages",
          "source": "db"
        },
        "dimension_id": 82,
        "input_skill": "Python",
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        "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 Analyst",
            "id": 20,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-analyst",
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          },
          {
            "display_name": "Data Scientist",
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            "role_archetype": null,
            "slug": "data-scientist",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
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        },
        "dimension_id": 48,
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        "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": "Azure Cloud Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "azure-cloud-engineer",
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          },
          {
            "display_name": "Cloud Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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          "rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
          "slug": "automation-and-scripting-for-operations",
          "source": "db"
        },
        "dimension_id": 361,
        "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": "Virtualization Engineer",
            "id": 26,
            "rationale": null,
            "role_archetype": null,
            "slug": "virtualization-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
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        "chosen_role_id": 6,
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          "display_name": "Network Automation and Scripting",
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          "rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
          "slug": "network-automation-and-scripting",
          "source": "db"
        },
        "dimension_id": 285,
        "input_skill": "Python",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Network Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "network-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        },
        "dimension_id": 261,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
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            "display_name": "AI Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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          "display_name": "Programming Languages for Backend Systems",
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          "slug": "programming-languages-for-backend-systems",
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        },
        "dimension_id": 140,
        "input_skill": "Python",
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        "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": [
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            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
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      {
        "chosen_role_id": 6,
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
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          "rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
          "slug": "programming-languages-for-data-work",
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        },
        "dimension_id": 67,
        "input_skill": "Python",
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        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
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      {
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          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
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          "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": "Python",
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        "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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"
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        "dimension_id": 328,
        "input_skill": "Python",
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        "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
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      {
        "chosen_role_id": 6,
        "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": [
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            "display_name": "Cybersecurity Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Data Modeling",
          "id": 71,
          "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
          "slug": "relational-data-modeling",
          "source": "db"
        },
        "dimension_id": 71,
        "input_skill": "SQL",
        "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",
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            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
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          },
          {
            "display_name": "Data Engineer",
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            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "SQL",
        "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": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model Serving Deployment and Runtime Packaging",
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          "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": "MLflow",
        "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": 2640,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "MLflow",
        "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": 2640,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "MLflow",
        "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": 2640,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud ML Platform Operations",
          "id": 65,
          "rationale": "Consumer-level operation of managed ML services and cloud resources used to train and serve models. This covers the cloud platform surface that MLOps engineers use without owning the underlying cloud platform itself.",
          "slug": "cloud-ml-platform-operations",
          "source": "db"
        },
        "dimension_id": 65,
        "input_skill": "Azure Machine Learning",
        "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",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 385,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Platform Services",
          "id": 81,
          "rationale": "Consumer-level use of cloud services that support data engineering workloads. This includes managed compute, storage, networking-adjacent services, and security primitives used to run pipelines and data platforms.",
          "slug": "cloud-data-platform-services",
          "source": "db"
        },
        "dimension_id": 81,
        "input_skill": "Azure Data Factory",
        "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": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 467,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud ML Platform Operations",
          "id": 65,
          "rationale": "Consumer-level operation of managed ML services and cloud resources used to train and serve models. This covers the cloud platform surface that MLOps engineers use without owning the underlying cloud platform itself.",
          "slug": "cloud-ml-platform-operations",
          "source": "db"
        },
        "dimension_id": 65,
        "input_skill": "Databricks",
        "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",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 386,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "CI/CD",
        "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": 2579,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Inference Data Pipelines",
          "id": 59,
          "rationale": "Operational data movement for batch scoring, feature refresh, and inference-time data preparation. This is separate from model training because it focuses on getting the right data to the serving path reliably.",
          "slug": "inference-data-pipelines",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "MLOps",
        "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",
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          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2643,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "MLOps",
        "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": 2643,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Analytical Programming and Notebook Languages",
          "id": null,
          "rationale": "Languages and notebook/script-based coding used to clean, transform, analyze, and prototype data workflows and models. Includes Python, pandas, SQL, PySpark, notebook scripting, dataframe manipulation, exploratory analysis, ETL/data transformation logic, and other reproducible analytical code.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 82,
        "input_skill": "PySpark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2684,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "PySpark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2684,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Analytical Programming and Notebook-Based Data Analysis",
          "id": null,
          "rationale": "Languages and notebook-friendly coding used to clean, transform, analyze, and prototype data and model workflows. This includes Python, R, SQL, and Scala used in notebooks or scripts for data wrangling, exploratory data analysis, statistical logic, feature engineering, and reproducible prototyping. It excludes production orchestration and scheduling, dashboard/report authoring, model deployment packaging, database administration, and UI development.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 82,
        "input_skill": "Notebooks",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2685,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Big Data",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2686,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Big Data",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "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": 2686,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2687,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Inference Data Pipelines for Serving and Batch Scoring",
          "id": null,
          "rationale": "Operational data movement that prepares and delivers timely, reliable data to production inference systems. Includes batch scoring inputs, feature refresh jobs, inference-time preprocessing, scheduled extracts, data validation for serving, and online/offline feature synchronization. Excludes training dataset curation, model training workflows, experimentation-focused feature engineering, model evaluation, and serving infrastructure/routing.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 59,
        "input_skill": "Data Pipelines",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2688,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Data Pipelines",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2688,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Asynchronous Messaging and Event Streaming",
          "id": null,
          "rationale": "Covers asynchronous communication and data movement through queues, topics, streams, event buses, and pub/sub systems for decoupled processing, background jobs, and event-driven integration. Includes continuous or event-driven data ingestion and change data capture pipelines, but excludes batch ETL orchestration, warehouse modeling, query optimization, model training data prep, and direct application API calls.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 146,
        "input_skill": "Data Ingestion",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2689,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Data Ingestion",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2689,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "Stream Processing",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "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": 2690,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Messaging, Queueing, and Event Streaming",
          "id": null,
          "rationale": "Asynchronous communication patterns and systems that decouple producers and consumers, buffer and route work items, and support background processing and service-to-service integration. Includes queueing, message queues, pub/sub, brokers, topics, consumer groups, producers/consumers, dead-letter queues, retry handling, backpressure, and event streaming platforms such as Kafka, RabbitMQ, SQS, and Azure Service Bus.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 146,
        "input_skill": "Queueing",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2691,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "API Integration, Request Orchestration, and Data Fetching",
          "id": null,
          "rationale": "Connecting applications to internal or external services through request/response APIs. This includes consuming REST and GraphQL endpoints, orchestrating requests, handling payloads and response parsing, pagination, retries, error handling, and shaping remote data for downstream or UI consumption.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 9,
        "input_skill": "APIs",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2692,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Service Integration Patterns",
          "id": 188,
          "rationale": "Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.",
          "slug": "cloud-service-integration-patterns",
          "source": "db"
        },
        "dimension_id": 188,
        "input_skill": "APIs",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Architect",
            "id": 11,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2692,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "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": "APIs",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2692,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 6,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Testing and Validation Practices",
          "id": 221,
          "rationale": "Validating platform changes before release, including functional checks and regression verification. This cluster is coherent because ServiceNow developers must confirm workflows, scripts, and integrations behave as intended.",
          "slug": "testing-and-validation-practices",
          "source": "db"
        },
        "dimension_id": 221,
        "input_skill": "Testability",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ServiceNOW Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "servicenow-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2693,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 10,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 16,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "265899c9-6b42-43cb-a0f8-64ac64ac5a98"
}