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      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Network Engineer",
          "id": 21,
          "rationale": null,
          "role_archetype": null,
          "slug": "network-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for AI Workflows",
        "id": 261,
        "rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
        "slug": "programming-languages-for-ai-workflows",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AI Engineer",
          "id": 12,
          "rationale": null,
          "role_archetype": null,
          "slug": "ai-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 67,
        "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",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Automation Tester",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "automation-tester",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
      },
      "input_skill": "TensorFlow",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
      },
      "input_skill": "PyTorch",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
      },
      "input_skill": "Scikit-learn",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Continuous Integration Test Integration",
        "id": 207,
        "rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
        "slug": "continuous-integration-test-integration",
        "source": "db"
      },
      "input_skill": "GitHub Actions",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Automation Tester",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "automation-tester",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Workflow Orchestration Systems",
        "id": 64,
        "rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
        "slug": "workflow-orchestration-systems",
        "source": "db"
      },
      "input_skill": "Airflow",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        },
        {
          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure Provisioning Templates",
        "id": 291,
        "rationale": "Declarative templates and modules used to create repeatable cloud resources and environments. This cluster covers the infrastructure definitions the role applies, reviews, and updates to keep environments consistent.",
        "slug": "infrastructure-provisioning-templates",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Engineer",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure as Code",
        "id": 22,
        "rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
        "slug": "infrastructure-as-code",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Infrastructure as Code and Declarative Provisioning",
        "id": 36,
        "rationale": "Defines cloud and platform infrastructure declaratively through versioned code so environments are repeatable, reviewable, and automatable. This includes authoring and maintaining IaC templates/modules, managing parameters and state, and using plan/apply workflows to provision and update resources across Azure and other cloud platforms.",
        "slug": "infrastructure-as-code-and-declarative-provisioning",
        "source": "db"
      },
      "input_skill": "Terraform",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Azure Cloud Engineer",
          "id": 4,
          "rationale": null,
          "role_archetype": null,
          "slug": "azure-cloud-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Docker",
      "llm_role": null,
      "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"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Docker",
      "llm_role": null,
      "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"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Kubernetes",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Engineer",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "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"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Applied Machine Learning Tooling and Frameworks",
        "id": null,
        "rationale": "Libraries and frameworks used to prototype, build, train, tune, and compare machine learning models in practice. Includes hands-on AI/ML tooling such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, along with model training, feature engineering, hyperparameter tuning, and evaluation workflows. Excludes model serving, deployment packaging, online inference, registry/version promotion, distributed stream processing, and cloud infrastructure provisioning.",
        "slug": "d_merge_01",
        "source": "llm"
      },
      "input_skill": "AI/ML",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "AI Service Integration and Orchestration Patterns",
        "id": null,
        "rationale": "Patterns for structuring and embedding AI/ML capabilities within products and services, including deciding where AI logic lives and how it interacts with application flows. Covers model-backed APIs, retrieval-augmented generation, agent and workflow orchestration, batch scoring services, online inference integration, feature pipelines, and placement across handlers, workers, gateways, and service boundaries.",
        "slug": "d_merge_02",
        "source": "llm"
      },
      "input_skill": "AI/ML",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "AI Inference Cost, Latency, and Throughput Optimization",
        "id": 260,
        "rationale": "Improving the speed, throughput, and cost efficiency of AI and ML-powered product features without sacrificing correctness or user experience. Includes token budgeting, prompt compression, batching, caching, model selection, quantization, pruning, async inference, warm starts, streaming UX, timeout tuning, concurrency control, and profiling. Excludes infrastructure autoscaling, model serving capacity planning, generic backend performance tuning, and unrelated data/warehouse optimization.",
        "slug": "ai-inference-cost-latency-and-throughput-optimization",
        "source": "db"
      },
      "input_skill": "AI/ML",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AI Engineer",
          "id": 12,
          "rationale": null,
          "role_archetype": null,
          "slug": "ai-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Managed ML Platform Workflows",
        "id": null,
        "rationale": "Cloud ML platforms for building and operating models end-to-end, including notebooks, experiments, managed training jobs, and pipeline/studio workflows. Examples: SageMaker Studio, SageMaker notebooks, SageMaker Pipelines, managed training jobs.",
        "slug": "d_split_01_01",
        "source": "llm"
      },
      "input_skill": "Amazon SageMaker",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Managed Model Hosting and Endpoints",
        "id": null,
        "rationale": "Cloud-managed services for deploying trained models as online or batch inference endpoints, including endpoint provisioning, batch transform, and rollout coordination. Examples: SageMaker endpoints, SageMaker batch transform.",
        "slug": "d_split_01_02",
        "source": "llm"
      },
      "input_skill": "Amazon SageMaker",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Model Serving Runtime Packaging",
        "id": null,
        "rationale": "Packaging trained models and model servers for deployment, including containers, base images, runtime dependencies, entrypoints, GPU images, and image scanning. Examples: Docker images for model services.",
        "slug": "d_split_01_03",
        "source": "llm"
      },
      "input_skill": "Amazon SageMaker",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Model Serving Frameworks and Platforms",
        "id": null,
        "rationale": "Serving frameworks and platforms used to run deployed models in online, batch, or streaming environments. Examples: TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, Seldon Core.",
        "slug": "d_split_01_04",
        "source": "llm"
      },
      "input_skill": "Amazon SageMaker",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Model Runtime Services",
        "id": 121,
        "rationale": "Consumer-level use of cloud services that host or support model inference applications. This cluster is coherent because MLEs often deploy and tune services on managed cloud compute, networking, and storage primitives.",
        "slug": "cloud-model-runtime-services",
        "source": "db"
      },
      "input_skill": "Amazon Bedrock",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Model Runtime Services",
        "id": 121,
        "rationale": "Consumer-level use of cloud services that host or support model inference applications. This cluster is coherent because MLEs often deploy and tune services on managed cloud compute, networking, and storage primitives.",
        "slug": "cloud-model-runtime-services",
        "source": "db"
      },
      "input_skill": "Amazon Bedrock",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Managed Cloud Data Platform Services",
        "id": null,
        "rationale": "Managed cloud services used to run data engineering and related application workloads, including serverless compute, workflow orchestration, storage, event triggers, and adjacent security/platform primitives. This covers services such as AWS Lambda, AWS Step Functions, AWS Glue, Amazon S3 event triggers, and other managed services used to build and operate pipelines and data platforms.",
        "slug": "d_merge_01",
        "source": "llm"
      },
      "input_skill": "AWS Lambda",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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"
      },
      "input_skill": "ECS",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Cloud Analytics Query Services",
        "id": null,
        "rationale": "Managed cloud services for querying and processing analytical data, including serverless SQL analytics, data lake querying, and federated query services such as Athena, Glue, Redshift Spectrum, and EMR-based analytics.",
        "slug": "d_split_01_01",
        "source": "llm"
      },
      "input_skill": "Amazon Athena",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Cloud Data Pipeline Runtime",
        "id": null,
        "rationale": "Managed cloud compute and orchestration services used to run data engineering jobs, transformations, and workflows.",
        "slug": "d_split_01_02",
        "source": "llm"
      },
      "input_skill": "Amazon Athena",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Cloud Data Platform Storage",
        "id": null,
        "rationale": "Cloud storage and data access services used as the persistence layer for data engineering platforms and pipelines.",
        "slug": "d_split_01_03",
        "source": "llm"
      },
      "input_skill": "Amazon Athena",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": null,
        "display_name": "Cloud Data Platform Security and Networking",
        "id": null,
        "rationale": "Identity, access, secrets, and networking primitives used to support cloud data platforms and pipelines.",
        "slug": "d_split_01_04",
        "source": "llm"
      },
      "input_skill": "Amazon Athena",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "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"
      },
      "input_skill": "AWS Data Pipeline",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "AWS Data Pipeline",
      "llm_role": null,
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          "reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "d_init_01"
          ],
          "skill_id": "faiss"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "mfa",
            "amm",
            "spl",
            "iscsi",
            "owasp-top-10",
            "foundry-fuzzing",
            "hsm",
            "bls",
            "dex",
            "aks"
          ],
          "requires": [],
          "skill_id": "faiss",
          "suppress_on_match": []
        },
        "skill_id": "faiss",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.93,
          "name": "FAISS",
          "reasoning": "FAISS is fundamentally a code package imported by applications for similarity search, so under the Tool vs Framework rule it fits Library rather than a user-operated tool or hosted platform.",
          "skill_id": "faiss",
          "subtype": "vector_search_library",
          "type": "Library"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "AI/ML",
    "Amazon SageMaker",
    "Amazon Bedrock",
    "AWS Lambda",
    "ECS",
    "Amazon Athena",
    "AWS Data Pipeline",
    "S3",
    "Kinesis",
    "Amazon API Gateway",
    "Pinecone",
    "OpenSearch",
    "FAISS"
  ]
}

{
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    "id": 10,
    "rationale": "The primary skills include a strong focus on AWS and AI/ML technologies, which aligns well with the role of a Machine Learning Engineer.",
    "role_archetype": null,
    "slug": "machine-learning-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "AI/ML",
      "tag": "new"
    },
    {
      "skill": "Amazon SageMaker",
      "tag": "new"
    },
    {
      "skill": "Amazon Bedrock",
      "tag": "new"
    },
    {
      "skill": "AWS Lambda",
      "tag": "new"
    },
    {
      "skill": "ECS",
      "tag": "new"
    },
    {
      "skill": "EKS",
      "tag": "in_db"
    },
    {
      "skill": "EC2",
      "tag": "in_db"
    },
    {
      "skill": "AWS Glue",
      "tag": "in_db"
    },
    {
      "skill": "Amazon Athena",
      "tag": "new"
    },
    {
      "skill": "Redshift",
      "tag": "in_db"
    },
    {
      "skill": "AWS Data Pipeline",
      "tag": "new"
    },
    {
      "skill": "S3",
      "tag": "new"
    },
    {
      "skill": "Kinesis",
      "tag": "new"
    },
    {
      "skill": "Amazon API Gateway",
      "tag": "new"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "TensorFlow",
      "tag": "in_db"
    },
    {
      "skill": "PyTorch",
      "tag": "in_db"
    },
    {
      "skill": "Scikit-learn",
      "tag": "in_db"
    },
    {
      "skill": "GitHub Actions",
      "tag": "in_db"
    },
    {
      "skill": "Airflow",
      "tag": "in_db"
    },
    {
      "skill": "Terraform",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "Pinecone",
      "tag": "new"
    },
    {
      "skill": "OpenSearch",
      "tag": "new"
    },
    {
      "skill": "FAISS",
      "tag": "new"
    }
  ],
  "persistence": {
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        "dimension": {
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          "display_name": "Cloud Platform Operations",
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          "rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
          "slug": "cloud-platform-operations",
          "source": "db"
        },
        "dimension_id": 26,
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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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            "slug": "devops-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 163,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
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        "dimension": {
          "difficulty_hint": "well_known",
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          "slug": "cloud-security-platforms",
          "source": "db"
        },
        "dimension_id": 332,
        "input_skill": "AWS",
        "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": 163,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Model Runtime Services",
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          "rationale": "Consumer-level use of cloud services that host or support model inference applications. This cluster is coherent because MLEs often deploy and tune services on managed cloud compute, networking, and storage primitives.",
          "slug": "cloud-model-runtime-services",
          "source": "db"
        },
        "dimension_id": 121,
        "input_skill": "EKS",
        "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": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 725,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Orchestration Platforms",
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          "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": "EKS",
        "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",
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            "slug": "cloud-engineer",
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          },
          {
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            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 725,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Provider Core Services",
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          "slug": "cloud-provider-core-services",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "EC2",
        "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,
            "slug": "cloud-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1773,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Platform Services",
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          "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": "AWS Glue",
        "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": 466,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Warehousing Platforms",
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          "rationale": "Cloud and on-prem analytical storage systems used to persist curated datasets and serve downstream consumers. This cluster is about the warehouse/lakehouse layer where transformed data is organized for access.",
          "slug": "data-warehousing-platforms",
          "source": "db"
        },
        "dimension_id": 72,
        "input_skill": "Redshift",
        "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": 2570,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Analytical Programming Languages",
          "id": 82,
          "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",
        "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 Analyst",
            "id": 20,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-analyst",
            "source": "db"
          },
          {
            "display_name": "Data Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Automation Scripting and CLI",
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          "rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
          "slug": "automation-scripting-and-cli",
          "source": "db"
        },
        "dimension_id": 48,
        "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": "Azure Cloud Engineer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "azure-cloud-engineer",
            "source": "db"
          },
          {
            "display_name": "Cloud Engineer",
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            "rationale": null,
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            "slug": "cloud-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
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          "display_name": "Automation and Scripting for Operations",
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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",
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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": "Virtualization Engineer",
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            "rationale": null,
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            "slug": "virtualization-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
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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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        "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": "Network Engineer",
            "id": 21,
            "rationale": null,
            "role_archetype": null,
            "slug": "network-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for AI Workflows",
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          "rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
          "slug": "programming-languages-for-ai-workflows",
          "source": "db"
        },
        "dimension_id": 261,
        "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": "AI Engineer",
            "id": 12,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
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          "display_name": "Programming Languages for Backend Systems",
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          "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": "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": "Backend Engineer",
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            "rationale": null,
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            "slug": "backend-engineer",
            "source": "db"
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
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          "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",
          "source": "db"
        },
        "dimension_id": 67,
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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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            "rationale": null,
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            "slug": "data-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
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          "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
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            "rationale": null,
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            "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": 10,
        "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": "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": [
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            "display_name": "Cybersecurity Engineer",
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            "slug": "cybersecurity-engineer",
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        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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": 10,
        "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": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "TensorFlow",
        "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 Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 558,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "PyTorch",
        "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 Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 557,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "Scikit-learn",
        "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 Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 554,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Continuous Integration Test Integration",
          "id": 207,
          "rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
          "slug": "continuous-integration-test-integration",
          "source": "db"
        },
        "dimension_id": 207,
        "input_skill": "GitHub Actions",
        "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": 1250,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Workflow Orchestration Systems",
          "id": 64,
          "rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
          "slug": "workflow-orchestration-systems",
          "source": "db"
        },
        "dimension_id": 64,
        "input_skill": "Airflow",
        "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"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "mlops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 325,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure Provisioning Templates",
          "id": 291,
          "rationale": "Declarative templates and modules used to create repeatable cloud resources and environments. This cluster covers the infrastructure definitions the role applies, reviews, and updates to keep environments consistent.",
          "slug": "infrastructure-provisioning-templates",
          "source": "db"
        },
        "dimension_id": 291,
        "input_skill": "Terraform",
        "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,
            "slug": "cloud-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 144,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure as Code",
          "id": 22,
          "rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
          "slug": "infrastructure-as-code",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Terraform",
        "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": 144,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure as Code and Declarative Provisioning",
          "id": 36,
          "rationale": "Defines cloud and platform infrastructure declaratively through versioned code so environments are repeatable, reviewable, and automatable. This includes authoring and maintaining IaC templates/modules, managing parameters and state, and using plan/apply workflows to provision and update resources across Azure and other cloud platforms.",
          "slug": "infrastructure-as-code-and-declarative-provisioning",
          "source": "db"
        },
        "dimension_id": 36,
        "input_skill": "Terraform",
        "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": "Azure Cloud Engineer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "azure-cloud-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 144,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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": 10,
        "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 10,
        "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,
            "slug": "cloud-engineer",
            "source": "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": 158,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Applied Machine Learning Tooling and Frameworks",
          "id": null,
          "rationale": "Libraries and frameworks used to prototype, build, train, tune, and compare machine learning models in practice. Includes hands-on AI/ML tooling such as scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch, along with model training, feature engineering, hyperparameter tuning, and evaluation workflows. Excludes model serving, deployment packaging, online inference, registry/version promotion, distributed stream processing, and cloud infrastructure provisioning.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 94,
        "input_skill": "AI/ML",
        "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": 2611,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "AI Service Integration and Orchestration Patterns",
          "id": null,
          "rationale": "Patterns for structuring and embedding AI/ML capabilities within products and services, including deciding where AI logic lives and how it interacts with application flows. Covers model-backed APIs, retrieval-augmented generation, agent and workflow orchestration, batch scoring services, online inference integration, feature pipelines, and placement across handlers, workers, gateways, and service boundaries.",
          "slug": "d_merge_02",
          "source": "llm"
        },
        "dimension_id": 270,
        "input_skill": "AI/ML",
        "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": 2611,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "AI Inference Cost, Latency, and Throughput Optimization",
          "id": 260,
          "rationale": "Improving the speed, throughput, and cost efficiency of AI and ML-powered product features without sacrificing correctness or user experience. Includes token budgeting, prompt compression, batching, caching, model selection, quantization, pruning, async inference, warm starts, streaming UX, timeout tuning, concurrency control, and profiling. Excludes infrastructure autoscaling, model serving capacity planning, generic backend performance tuning, and unrelated data/warehouse optimization.",
          "slug": "ai-inference-cost-latency-and-throughput-optimization",
          "source": "db"
        },
        "dimension_id": 260,
        "input_skill": "AI/ML",
        "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": "AI Engineer",
            "id": 12,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2611,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Managed ML Platform Workflows",
          "id": null,
          "rationale": "Cloud ML platforms for building and operating models end-to-end, including notebooks, experiments, managed training jobs, and pipeline/studio workflows. Examples: SageMaker Studio, SageMaker notebooks, SageMaker Pipelines, managed training jobs.",
          "slug": "d_split_01_01",
          "source": "llm"
        },
        "dimension_id": 367,
        "input_skill": "Amazon SageMaker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 New dimension saved (reconciliation separate) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2612,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Managed Model Hosting and Endpoints",
          "id": null,
          "rationale": "Cloud-managed services for deploying trained models as online or batch inference endpoints, including endpoint provisioning, batch transform, and rollout coordination. Examples: SageMaker endpoints, SageMaker batch transform.",
          "slug": "d_split_01_02",
          "source": "llm"
        },
        "dimension_id": 368,
        "input_skill": "Amazon SageMaker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 New dimension saved (reconciliation separate) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2612,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Model Serving Runtime Packaging",
          "id": null,
          "rationale": "Packaging trained models and model servers for deployment, including containers, base images, runtime dependencies, entrypoints, GPU images, and image scanning. Examples: Docker images for model services.",
          "slug": "d_split_01_03",
          "source": "llm"
        },
        "dimension_id": 52,
        "input_skill": "Amazon SageMaker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (embedding dedup) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2612,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Model Serving Frameworks and Platforms",
          "id": null,
          "rationale": "Serving frameworks and platforms used to run deployed models in online, batch, or streaming environments. Examples: TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, Seldon Core.",
          "slug": "d_split_01_04",
          "source": "llm"
        },
        "dimension_id": 52,
        "input_skill": "Amazon SageMaker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (embedding dedup) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2612,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Model Runtime Services",
          "id": 121,
          "rationale": "Consumer-level use of cloud services that host or support model inference applications. This cluster is coherent because MLEs often deploy and tune services on managed cloud compute, networking, and storage primitives.",
          "slug": "cloud-model-runtime-services",
          "source": "db"
        },
        "dimension_id": 121,
        "input_skill": "Amazon Bedrock",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 2613,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Managed Cloud Data Platform Services",
          "id": null,
          "rationale": "Managed cloud services used to run data engineering and related application workloads, including serverless compute, workflow orchestration, storage, event triggers, and adjacent security/platform primitives. This covers services such as AWS Lambda, AWS Step Functions, AWS Glue, Amazon S3 event triggers, and other managed services used to build and operate pipelines and data platforms.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 81,
        "input_skill": "AWS Lambda",
        "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": 2614,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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": "ECS",
        "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": 2615,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Cloud Analytics Query Services",
          "id": null,
          "rationale": "Managed cloud services for querying and processing analytical data, including serverless SQL analytics, data lake querying, and federated query services such as Athena, Glue, Redshift Spectrum, and EMR-based analytics.",
          "slug": "d_split_01_01",
          "source": "llm"
        },
        "dimension_id": 367,
        "input_skill": "Amazon Athena",
        "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": 2616,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Cloud Data Pipeline Runtime",
          "id": null,
          "rationale": "Managed cloud compute and orchestration services used to run data engineering jobs, transformations, and workflows.",
          "slug": "d_split_01_02",
          "source": "llm"
        },
        "dimension_id": 81,
        "input_skill": "Amazon Athena",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (embedding dedup) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2616,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
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          "display_name": "Cloud Data Platform Storage",
          "id": null,
          "rationale": "Cloud storage and data access services used as the persistence layer for data engineering platforms and pipelines.",
          "slug": "d_split_01_03",
          "source": "llm"
        },
        "dimension_id": 81,
        "input_skill": "Amazon Athena",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (embedding dedup) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2616,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Cloud Data Platform Security and Networking",
          "id": null,
          "rationale": "Identity, access, secrets, and networking primitives used to support cloud data platforms and pipelines.",
          "slug": "d_split_01_04",
          "source": "llm"
        },
        "dimension_id": 369,
        "input_skill": "Amazon Athena",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 New dimension saved (reconciliation separate) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2616,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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": "AWS Data Pipeline",
        "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": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2617,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Storage Provisioning and Automation",
          "id": 311,
          "rationale": "Covers the scripts, APIs, and operational workflows used to create, resize, map, and retire storage resources. This cluster is coherent because storage engineers often automate repetitive provisioning and maintenance tasks.",
          "slug": "storage-provisioning-and-automation",
          "source": "db"
        },
        "dimension_id": 311,
        "input_skill": "S3",
        "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": "Storage Engineer",
            "id": 22,
            "rationale": null,
            "role_archetype": null,
            "slug": "storage-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2618,
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      },
      {
        "chosen_role_id": 10,
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          "difficulty_hint": "well_known",
          "display_name": "Streaming Data Processing",
          "id": 69,
          "rationale": "Tools and patterns for ingesting and transforming event streams with low latency. This cluster covers continuous processing, windowing, and stateful stream jobs used to keep data fresh.",
          "slug": "streaming-data-processing",
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        },
        "dimension_id": 69,
        "input_skill": "Kinesis",
        "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": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2619,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
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        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "HTTP API Frameworks and Gateway Layers",
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          "rationale": "Server-side frameworks and gateway layers used to build, route, validate, and manage HTTP APIs. Includes backend web service frameworks and API gateway configuration for endpoints, request/response mapping, routing, input validation, auth integration, throttling, usage plans, stages, custom domains, and backend integration.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 141,
        "input_skill": "Amazon API Gateway",
        "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": 2620,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Model Runtime Services",
          "id": 121,
          "rationale": "Consumer-level use of cloud services that host or support model inference applications. This cluster is coherent because MLEs often deploy and tune services on managed cloud compute, networking, and storage primitives.",
          "slug": "cloud-model-runtime-services",
          "source": "db"
        },
        "dimension_id": 121,
        "input_skill": "Pinecone",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 2621,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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": "OpenSearch",
        "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": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2622,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "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",
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        "dimension_id": 365,
        "input_skill": "OpenSearch",
        "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": 2622,
        "skill_tag": "in_db",
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      },
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        "chosen_role_id": 10,
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          "display_name": "Applied Machine Learning Toolkits and Frameworks",
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          "rationale": "Libraries and frameworks used to prototype, compare, index, and evaluate machine learning solutions quickly. Includes applied-ML toolkits such as scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, NumPy, pandas, FAISS, vector search libraries, and embedding utilities. Excludes serving runtimes, deployment containers, online inference infrastructure, streaming systems, and database/storage engines.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 94,
        "input_skill": "FAISS",
        "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": 2623,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
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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.",
          "slug": "d_init_01",
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        "dimension_id": 365,
        "input_skill": "FAISS",
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        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2623,
        "skill_tag": "in_db",
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      },
      {
        "chosen_role_id": 10,
        "dimension": {
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          "display_name": "AI Inference Cost, Latency, and Throughput Optimization",
          "id": null,
          "rationale": "Improving the runtime efficiency of AI/ML-powered features by reducing inference cost and latency while increasing throughput and preserving user experience. Includes token budgeting, prompt compression, batching, caching, quantization, pruning, model selection, async inference, warm starts, streaming UX, timeout tuning, concurrency control, GPU utilization, and profiling. Excludes model training, feature engineering, registry/versioning, infrastructure autoscaling, serving capacity planning, generic backend performance tuning, and unrelated data/warehouse optimization.",
          "slug": "d_merge_03",
          "source": "llm"
        },
        "dimension_id": 260,
        "input_skill": "AI/ML",
        "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": 2611,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Streaming Data Processing",
          "id": null,
          "rationale": "Tools and patterns for ingesting, buffering, and transforming event streams with low latency. This includes continuous processing, windowing, stateful stream jobs, checkpointing, shard scaling, stream partitioning, and managed streaming services such as Kinesis, Amazon Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 69,
        "input_skill": "Kinesis",
        "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": 2619,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 13,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 21,
    "skipped": 0
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  "planner_output": null,
  "run_id": "20755499-04f6-440f-80a9-bb023fddc1ff"
}