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          "role_archetype": null,
          "slug": "data-analyst",
          "source": "db"
        },
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Automation Scripting and CLI",
        "id": 48,
        "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "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",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Automation and Scripting for Operations",
        "id": 361,
        "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"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Virtualization Engineer",
          "id": 26,
          "rationale": null,
          "role_archetype": null,
          "slug": "virtualization-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Network Automation and Scripting",
        "id": 285,
        "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"
      },
      "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": "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"
      },
      "input_skill": "PySpark",
      "llm_role": null,
      "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"
        }
      ]
    },
    {
      "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": "PySpark",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Relational Data Modeling",
        "id": 71,
        "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
        "slug": "relational-data-modeling",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "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": "SQL",
      "llm_role": null,
      "roles_from_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": "MLflow",
      "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": "Project Delivery and Coordination",
        "id": 366,
        "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
        "slug": "d_init_02",
        "source": "db"
      },
      "input_skill": "MLflow",
      "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": "MLflow",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud ML Platform Operations",
        "id": 65,
        "rationale": "Consumer-level operation of managed ML services and cloud resources used to train and serve models. This covers the cloud platform surface that MLOps engineers use without owning the underlying cloud platform itself.",
        "slug": "cloud-ml-platform-operations",
        "source": "db"
      },
      "input_skill": "Azure Machine Learning",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-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": "Azure Data Factory",
      "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 ML Platform Operations",
        "id": 65,
        "rationale": "Consumer-level operation of managed ML services and cloud resources used to train and serve models. This covers the cloud platform surface that MLOps engineers use without owning the underlying cloud platform itself.",
        "slug": "cloud-ml-platform-operations",
        "source": "db"
      },
      "input_skill": "Databricks",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "Notebooks",
      "llm_role": null,
      "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"
        }
      ]
    },
    {
      "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": "CI/CD",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
        "id": 146,
        "rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Big Data",
      "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": "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": "Big Data",
      "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": "Data Modeling",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Inference Data Pipelines",
        "id": 59,
        "rationale": "Operational data movement for batch scoring, feature refresh, and inference-time data preparation. This is separate from model training because it focuses on getting the right data to the serving path reliably.",
        "slug": "inference-data-pipelines",
        "source": "db"
      },
      "input_skill": "Data Pipelines",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
          "source": "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": "Data Pipelines",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Messaging and Event Streaming",
        "id": 146,
        "rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Stream Processing",
      "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": "Messaging and Event Streaming",
        "id": 146,
        "rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
        "slug": "messaging-and-event-streaming",
        "source": "db"
      },
      "input_skill": "Queueing",
      "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": "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": "Data Stores",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "API Integration and Data Fetching",
        "id": 9,
        "rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
        "slug": "api-integration-and-data-fetching",
        "source": "db"
      },
      "input_skill": "APIs",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Frontend Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
          "slug": "frontend-engineer",
          "source": "db"
        },
        {
          "display_name": "Full Stack Developer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "full-stack-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Service Integration Patterns",
        "id": 188,
        "rationale": "Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.",
        "slug": "cloud-service-integration-patterns",
        "source": "db"
      },
      "input_skill": "APIs",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Architect",
          "id": 11,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-architect",
          "source": "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": "APIs",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Testing and Validation Practices",
        "id": 221,
        "rationale": "Validating platform changes before release, including functional checks and regression verification. This cluster is coherent because ServiceNow developers must confirm workflows, scripts, and integrations behave as intended.",
        "slug": "testing-and-validation-practices",
        "source": "db"
      },
      "input_skill": "Testability",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ServiceNOW Developer",
          "id": 24,
          "rationale": null,
          "role_archetype": null,
          "slug": "servicenow-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "input_skill": "MLflow",
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          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
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          "source": "db"
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          "slug": "cloud-ml-platform-operations",
          "source": "db"
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        "dimension_id": 65,
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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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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "slug": "data-analyst",
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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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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [],
        "skill_dimension_saved": true,
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        "skill_tag": "in_db",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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          "slug": "d_init_01",
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          "slug": "d_init_01",
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          "rationale": "Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.",
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        "input_skill": "Testability",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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            "display_name": "ServiceNOW Developer",
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