Pipeline run

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "PyTorch"
    },
    {
      "is_primary": true,
      "skill_name": "TensorFlow"
    },
    {
      "is_primary": true,
      "skill_name": "pandas"
    },
    {
      "is_primary": true,
      "skill_name": "NumPy"
    },
    {
      "is_primary": true,
      "skill_name": "OCR"
    },
    {
      "is_primary": true,
      "skill_name": "A/B Testing"
    },
    {
      "is_primary": true,
      "skill_name": "Feature Engineering"
    },
    {
      "is_primary": true,
      "skill_name": "Model Serving"
    },
    {
      "is_primary": true,
      "skill_name": "Monitoring"
    },
    {
      "is_primary": true,
      "skill_name": "Alerting"
    },
    {
      "is_primary": false,
      "skill_name": "Reinforcement Learning"
    },
    {
      "is_primary": false,
      "skill_name": "Graph Neural Networks"
    },
    {
      "is_primary": false,
      "skill_name": "RAG"
    },
    {
      "is_primary": false,
      "skill_name": "Fine-tuning"
    },
    {
      "is_primary": false,
      "skill_name": "Prompt Engineering"
    }
  ],
  "jd_role": {
    "display_name": "Machine Learning Engineer",
    "rationale": null,
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "SabPaisa (SRS Live Technologies) is",
        "last_5_words": "to the client\u2019s unique needs."
      },
      "text": "SabPaisa (SRS Live Technologies) is an RBI Authorised Payment Aggregator.\n\nFounded in 2016 with headquarters in New Delhi, a corporate office in Kolkata, and regional offices across the country, it is a rapidly advancing fintech company. SabPaisa is dedicated to providing simplified payment solutions, offering customizable options tailored to the client\u2019s unique needs.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "SabPaisa (SRS Live Technologies)",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "FinTech",
          "Payments"
        ],
        "domain": "Financial Services"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": 5,
      "min": 3,
      "raw": "3-5 years of applied ML with models deployed to production (not just notebooks/Kaggle)"
    },
    "job_locations": [
      {
        "aliases": [
          "Delhi"
        ],
        "city": "New Delhi",
        "country": "India",
        "state": "Delhi",
        "work_mode": "null"
      },
      {
        "aliases": [
          "Calcutta"
        ],
        "city": "Kolkata",
        "country": "India",
        "state": "West Bengal",
        "work_mode": "null"
      }
    ],
    "role": "Machine Learning Engineer",
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 8,
        "heading": "Roles \u0026 Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Build smart payment routing models",
          "last_5_words": "training procedures, and performance metrics"
        },
        "text": "Build smart payment routing models to optimize transaction success rates across UPI, cards, and net banking rails \nDesign and deploy real-time fraud detection systems with sub-100ms latency\u2014device fingerprinting, behavioral analysis, velocity checks \nDevelop merchant risk scoring models for automated underwriting using documents, transaction patterns, and external signals \nBuild document intelligence pipelines\u2014OCR, classification, and data extraction for KYC documents (PAN, Aadhaar, GST, bank statements) \nSet up ML infrastructure\u2014feature stores, model serving, A/B testing frameworks, monitoring and alerting \nCollaborate with product and engineering teams to integrate ML models into production systems \nMonitor model performance, detect drift, and implement retraining pipelines \nDocument model architecture, training procedures, and performance metrics",
        "word_count": 139
      },
      {
        "bullet_count": 5,
        "heading": "Requirements",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "3-5 years of applied ML",
          "last_5_words": "feature stores, model serving"
        },
        "text": "3-5 years of applied ML with models deployed to production (not just notebooks/Kaggle) \nStrong Python with PyTorch or TensorFlow, plus pandas/numpy for data manipulation \nEnd-to-end ML skills: feature engineering, training, evaluation, deployment, monitoring \nExperience with tabular/transactional data and classification/ranking problems \nUnderstanding of real-time inference\u2014latency budgets, feature stores, model serving",
        "word_count": 66
      },
      {
        "bullet_count": 4,
        "heading": "Good to Have",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Fraud detection or risk modeling",
          "last_5_words": "fine-tuning, prompt engineering"
        },
        "text": "Fraud detection or risk modeling experience in fintech/payments \nMulti-armed bandits or reinforcement learning for optimization \nGraph neural networks for network-based detection \nLLM experience: RAG pipelines, fine-tuning, prompt engineering",
        "word_count": 40
      }
    ],
    "urls": [
      {
        "type": "other",
        "url": "https://www.youtube.com/watch?v=K7Z7A059faE"
      }
    ]
  },
  "run_id": null
}

{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3647,
      "existing_alias_text": "Machine Learning",
      "input_term": "Machine Learning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 2672,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 2146,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 608,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 5,
        "display_name": "Python",
        "id": 393,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 54,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 861,
      "existing_alias_text": "PyTorch",
      "input_term": "PyTorch",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "PyTorch",
        "id": 557,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "pytorch",
        "sub_category_id": 456,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 862,
      "existing_alias_text": "TensorFlow",
      "input_term": "TensorFlow",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "TensorFlow",
        "id": 558,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "tensorflow",
        "sub_category_id": 456,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 766,
      "existing_alias_text": "pandas",
      "input_term": "pandas",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "pandas",
        "id": 474,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "pandas",
        "sub_category_id": 454,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 767,
      "existing_alias_text": "NumPy",
      "input_term": "NumPy",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "NumPy",
        "id": 475,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "numpy",
        "sub_category_id": 459,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3610,
      "existing_alias_text": "OCR",
      "input_term": "OCR",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "OCR",
        "id": 2653,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "ocr",
        "sub_category_id": 2166,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 798,
      "existing_alias_text": "A/B testing",
      "input_term": "A/B Testing",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "A/B testing",
        "id": 506,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "a-b-testing",
        "sub_category_id": 469,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 811,
      "existing_alias_text": "feature engineering",
      "input_term": "Feature Engineering",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "feature engineering",
        "id": 519,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "feature-engineering",
        "sub_category_id": 471,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3676,
      "existing_alias_text": "Model Serving",
      "input_term": "Model Serving",
      "matched_canonical": {
        "category_id": 1,
        "display_name": "Model Serving",
        "id": 2701,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "model-serving",
        "sub_category_id": 541,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 975,
      "existing_alias_text": "alerting",
      "input_term": "Alerting",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "alerting",
        "id": 665,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "alerting",
        "sub_category_id": 356,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3677,
      "existing_alias_text": "Reinforcement Learning",
      "input_term": "Reinforcement Learning",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Reinforcement Learning",
        "id": 2702,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "reinforcement-learning",
        "sub_category_id": 2207,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3678,
      "existing_alias_text": "Graph Neural Networks",
      "input_term": "Graph Neural Networks",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "Graph Neural Networks",
        "id": 2703,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "graph-neural-networks",
        "sub_category_id": 2161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3616,
      "existing_alias_text": "RAG",
      "input_term": "RAG",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "RAG",
        "id": 2659,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rag",
        "sub_category_id": 2172,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3679,
      "existing_alias_text": "Fine-tuning",
      "input_term": "Fine-tuning",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "Fine-tuning",
        "id": 2704,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "fine-tuning",
        "sub_category_id": 2208,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3658,
      "existing_alias_text": "Prompt Engineering",
      "input_term": "Prompt Engineering",
      "matched_canonical": {
        "category_id": 7,
        "display_name": "Prompt Engineering",
        "id": 2683,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "prompt-engineering",
        "sub_category_id": 2191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "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"
    },
    {
      "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"
    },
    {
      "display_name": "Virtualization Engineer",
      "id": 26,
      "rationale": null,
      "role_archetype": null,
      "slug": "virtualization-engineer",
      "source": "db"
    },
    {
      "display_name": "Network Engineer",
      "id": 21,
      "rationale": null,
      "role_archetype": null,
      "slug": "network-engineer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 12,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "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"
    },
    {
      "display_name": "Machine Learning Engineer",
      "id": 10,
      "rationale": null,
      "role_archetype": null,
      "slug": "machine-learning-engineer",
      "source": "db"
    },
    {
      "display_name": "Cybersecurity Engineer",
      "id": 9,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "Automation Tester",
      "id": 16,
      "rationale": null,
      "role_archetype": null,
      "slug": "automation-tester",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "mlops-engineer",
      "source": "db"
    },
    {
      "display_name": "Cloud Architect",
      "id": 11,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-architect",
      "source": "db"
    },
    {
      "display_name": "MySQL DBA",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "mysql-dba",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Machine Learning Engineer",
    "id": 10,
    "rationale": "The primary skills indicate a focus on machine learning and related tools, aligning perfectly with the Machine Learning Engineer role.",
    "role_archetype": null,
    "slug": "machine-learning-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "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": "Machine Learning",
      "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": "Machine Learning",
      "llm_role": null,
      "roles_from_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": "Python",
      "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": "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": "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": "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": "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": "pandas",
      "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": "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": "NumPy",
      "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": "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": "OCR",
      "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": "OCR",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Experiment Design and Analysis",
        "id": 87,
        "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
        "slug": "experiment-design-and-analysis",
        "source": "db"
      },
      "input_skill": "A/B Testing",
      "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": "Feature Engineering and Selection",
        "id": 89,
        "rationale": "Creating, transforming, and selecting variables that improve model performance and interpretability. This is a coherent cluster because it bridges raw data understanding and model building.",
        "slug": "feature-engineering-and-selection",
        "source": "db"
      },
      "input_skill": "Feature Engineering",
      "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": "Model Serving Architecture",
        "id": 115,
        "rationale": "Patterns for hosting, routing, and scaling model inference in online or batch-serving applications. This covers how model calls are embedded into services, sidecars, gateways, or dedicated serving layers.",
        "slug": "model-serving-architecture",
        "source": "db"
      },
      "input_skill": "Model Serving",
      "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": "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": "Model Serving",
      "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": "Observability and Incident Triage",
        "id": 61,
        "rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
        "slug": "observability-and-incident-triage",
        "source": "db"
      },
      "input_skill": "Alerting",
      "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": "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": "Reinforcement Learning",
      "llm_role": null,
      "roles_from_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": "Graph Neural Networks",
      "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": "Graph Neural Networks",
      "llm_role": null,
      "roles_from_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": "RAG",
      "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": "Context Management and Retrieval",
        "id": 264,
        "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
        "slug": "context-management-and-retrieval",
        "source": "db"
      },
      "input_skill": "RAG",
      "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": "Experiment Design and Analysis",
        "id": 87,
        "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
        "slug": "experiment-design-and-analysis",
        "source": "db"
      },
      "input_skill": "Fine-tuning",
      "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": "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": "Fine-tuning",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Context Management and Retrieval",
        "id": 264,
        "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
        "slug": "context-management-and-retrieval",
        "source": "db"
      },
      "input_skill": "Prompt Engineering",
      "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": "MySQL Operational Monitoring, Logging, and Diagnostics",
        "id": 166,
        "rationale": "Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.",
        "slug": "mysql-operational-monitoring-logging-and-diagnostics",
        "source": "db"
      },
      "input_skill": "Monitoring",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MySQL DBA",
          "id": 23,
          "rationale": null,
          "role_archetype": null,
          "slug": "mysql-dba",
          "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": "Monitoring",
      "llm_role": null,
      "roles_from_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": "Monitoring",
      "llm_role": null,
      "roles_from_db": []
    }
  ],
  "input_final_skills": [
    "Machine Learning",
    "Python",
    "PyTorch",
    "TensorFlow",
    "pandas",
    "NumPy",
    "OCR",
    "A/B Testing",
    "Feature Engineering",
    "Model Serving",
    "Monitoring",
    "Alerting",
    "Reinforcement Learning",
    "Graph Neural Networks",
    "RAG",
    "Fine-tuning",
    "Prompt Engineering"
  ],
  "input_llm_skills": [
    "Machine Learning",
    "Python",
    "PyTorch",
    "TensorFlow",
    "pandas",
    "NumPy",
    "OCR",
    "A/B Testing",
    "Feature Engineering",
    "Model Serving",
    "Monitoring",
    "Alerting",
    "Reinforcement Learning",
    "Graph Neural Networks",
    "RAG",
    "Fine-tuning",
    "Prompt Engineering"
  ],
  "new_aliases_persisted": 0,
  "run_id": "0ecfddb7-617a-4645-868f-9266ac0de542",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 3647,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 2672,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 2146,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "Machine Learning",
          "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": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Python",
          "alias_type": "CANONICAL",
          "id": 608,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2",
          "alias_type": "VERSION",
          "id": 611,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2.x",
          "alias_type": "VERSION",
          "id": 613,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3",
          "alias_type": "VERSION",
          "id": 612,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.10",
          "alias_type": "VERSION",
          "id": 2330,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.11",
          "alias_type": "VERSION",
          "id": 2331,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.12",
          "alias_type": "VERSION",
          "id": 2332,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 3.x",
          "alias_type": "VERSION",
          "id": 614,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py2",
          "alias_type": "VERSION",
          "id": 609,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "py3",
          "alias_type": "VERSION",
          "id": 610,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 2",
          "alias_type": "VERSION",
          "id": 2152,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 2.x",
          "alias_type": "VERSION",
          "id": 2154,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3",
          "alias_type": "VERSION",
          "id": 990,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.10",
          "alias_type": "VERSION",
          "id": 992,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.11",
          "alias_type": "VERSION",
          "id": 993,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.12",
          "alias_type": "VERSION",
          "id": 994,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python 3.x",
          "alias_type": "VERSION",
          "id": 991,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python2",
          "alias_type": "VERSION",
          "id": 2150,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "python3",
          "alias_type": "VERSION",
          "id": 989,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "Python",
        "id": 393,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 54,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "Python",
          "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": "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"
            }
          ]
        }
      ],
      "input_skill": "Python",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "PyTorch",
          "alias_type": "CANONICAL",
          "id": 861,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "PyTorch",
        "id": 557,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "pytorch",
        "sub_category_id": 456,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
            }
          ]
        }
      ],
      "input_skill": "PyTorch",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "TensorFlow",
          "alias_type": "CANONICAL",
          "id": 862,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TF1",
          "alias_type": "VERSION",
          "id": 863,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TF2",
          "alias_type": "VERSION",
          "id": 864,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TensorFlow 1",
          "alias_type": "VERSION",
          "id": 865,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TensorFlow 1.x",
          "alias_type": "VERSION",
          "id": 867,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TensorFlow 2",
          "alias_type": "VERSION",
          "id": 866,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "TensorFlow 2.x",
          "alias_type": "VERSION",
          "id": 868,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "TensorFlow",
        "id": 558,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "tensorflow",
        "sub_category_id": 456,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
            }
          ]
        }
      ],
      "input_skill": "TensorFlow",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "pandas",
          "alias_type": "CANONICAL",
          "id": 766,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "pandas 1.x",
          "alias_type": "VERSION",
          "id": 2529,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "pandas 2.x",
          "alias_type": "VERSION",
          "id": 2530,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "pandas1",
          "alias_type": "VERSION",
          "id": 2527,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "pandas2",
          "alias_type": "VERSION",
          "id": 2528,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "pandas",
        "id": 474,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "pandas",
        "sub_category_id": 454,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "pandas",
          "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"
            }
          ]
        }
      ],
      "input_skill": "pandas",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "NumPy",
          "alias_type": "CANONICAL",
          "id": 767,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "NumPy",
        "id": 475,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "numpy",
        "sub_category_id": 459,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "NumPy",
          "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"
            }
          ]
        }
      ],
      "input_skill": "NumPy",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "OCR",
          "alias_type": "CANONICAL",
          "id": 3610,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "OCR",
        "id": 2653,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "ocr",
        "sub_category_id": 2166,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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": "OCR",
          "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": "OCR",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "OCR",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "A/B testing",
          "alias_type": "CANONICAL",
          "id": 798,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "A/B testing",
        "id": 506,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "a-b-testing",
        "sub_category_id": 469,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Experiment Design and Analysis",
            "id": 87,
            "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
            "slug": "experiment-design-and-analysis",
            "source": "db"
          },
          "input_skill": "A/B Testing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Scientist",
              "id": 7,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-scientist",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "A/B Testing",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "feature engineering",
          "alias_type": "CANONICAL",
          "id": 811,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "feature engineering",
        "id": 519,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "feature-engineering",
        "sub_category_id": 471,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Feature Engineering and Selection",
            "id": 89,
            "rationale": "Creating, transforming, and selecting variables that improve model performance and interpretability. This is a coherent cluster because it bridges raw data understanding and model building.",
            "slug": "feature-engineering-and-selection",
            "source": "db"
          },
          "input_skill": "Feature Engineering",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Scientist",
              "id": 7,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-scientist",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Feature Engineering",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Model Serving",
          "alias_type": "CANONICAL",
          "id": 3676,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "Model Serving",
        "id": 2701,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "model-serving",
        "sub_category_id": 541,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Model Serving Architecture",
            "id": 115,
            "rationale": "Patterns for hosting, routing, and scaling model inference in online or batch-serving applications. This covers how model calls are embedded into services, sidecars, gateways, or dedicated serving layers.",
            "slug": "model-serving-architecture",
            "source": "db"
          },
          "input_skill": "Model Serving",
          "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": "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": "Model Serving",
          "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"
            }
          ]
        }
      ],
      "input_skill": "Model Serving",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
            "id": 166,
            "rationale": "Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.",
            "slug": "mysql-operational-monitoring-logging-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Monitoring",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "MySQL DBA",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "mysql-dba",
              "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": "Monitoring",
          "llm_role": null,
          "roles_from_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": "Monitoring",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Monitoring",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "observability_monitoring",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": true,
            "confused_with": [
              "observability",
              "logging",
              "alerting"
            ],
            "reasoning": "\"Monitoring\" is broad in JDs and can overlap with observability, logging, or alerting work. A parser could reasonably map it to those adjacent monitoring-stack concepts rather than a distinct skill."
          },
          "context_keywords": {
            "context_keywords": [
              "alerting",
              "dashboards",
              "metrics",
              "logs",
              "traces",
              "Prometheus",
              "Grafana",
              "Datadog",
              "New Relic",
              "Splunk",
              "SLA",
              "SLO",
              "incident response",
              "on-call",
              "observability"
            ]
          },
          "maturity": {
            "confidence": 0.96,
            "maturity": "well_known",
            "reasoning": "Monitoring is a standard requirement in most SRE/DevOps job descriptions and is bundled with major observability stacks like Prometheus/Grafana and Datadog, indicating broad hiring-market adoption."
          },
          "skill_id": "monitoring",
          "vendor_license": {
            "confidence": 0.99,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Monitoring MySQL production health and using MySQL-native logs, counters, and diagnostic views/queries to detect, investigate, explain, and respond to incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, lock and saturation checks, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, information_schema, status variables, and related diagnostic queries.",
            "exemplar_skills": [
              "MySQL Operational Monitoring, Logging, and Diagnostics"
            ],
            "in_scope": "Skills, tools, and practices that belong under MySQL Operational Monitoring, Logging, and Diagnostics for the target role, including items implied by the dimension rationale.",
            "name": "MySQL Operational Monitoring, Logging, and Diagnostics",
            "out_of_scope": "Adjacent clusters explicitly not owned by MySQL Operational Monitoring, Logging, and Diagnostics, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "d_merge_01"
          },
          {
            "description": "Monitoring across services, hosts, and applications using metrics, logs, traces, and alerts. This fits the target skill when it refers to general production visibility rather than a single product or database.",
            "exemplar_skills": [
              "Monitoring",
              "observability",
              "alerting",
              "metrics dashboards",
              "log monitoring",
              "distributed tracing"
            ],
            "in_scope": "Monitoring, metrics collection, dashboards, alerting rules, log aggregation, tracing, SLOs, health checks, anomaly detection, incident signals, observability tooling",
            "name": "System Observability and Alerting",
            "out_of_scope": "Incident triage and containment, capacity forecasting, performance tuning, deployment automation, these are owned by incident response or optimization dimensions",
            "overlap_flags": [
              {
                "reason": "Monitoring provides the signals used in incidents, but response and containment are separate actions after detection.",
                "with_dim_id": "incident-response-and-containment",
                "with_dim_name": null,
                "with_role": "Cybersecurity Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          },
          {
            "description": "Monitoring machine learning models and data pipelines for drift, freshness, quality, and service health. This is relevant to the role hint because ML engineers often monitor inference and pipeline behavior in production.",
            "exemplar_skills": [
              "Monitoring",
              "model drift monitoring",
              "data drift monitoring",
              "feature freshness checks",
              "inference monitoring",
              "pipeline health monitoring"
            ],
            "in_scope": "Monitoring, model drift detection, data drift, feature freshness, inference latency, prediction quality, pipeline health, job failures, service uptime, alerting for ML systems",
            "name": "Model and Pipeline Monitoring",
            "out_of_scope": "Model training, experiment design, feature engineering, serving architecture, these belong to training or deployment dimensions rather than ongoing observation",
            "overlap_flags": [
              {
                "reason": "Serving infrastructure may expose the metrics, but this dimension focuses on observing model behavior after deployment.",
                "with_dim_id": "model-serving-deployment-and-runtime-packaging",
                "with_dim_name": null,
                "with_role": "MLOps Engineer, Machine Learning Engineer"
              },
              {
                "reason": "Pipeline execution is monitored here, but the mechanics of building the pipelines belong to the data-pipeline dimension.",
                "with_dim_id": "inference-data-pipelines",
                "with_dim_name": null,
                "with_role": "MLOps Engineer"
              }
            ],
            "tentative_id": "d_init_02"
          }
        ],
        "merge_log": [
          {
            "a_dim_id": "mysql-operational-monitoring-logging-and-diagnostics",
            "a_name": "MySQL Operational Monitoring and Diagnostics",
            "a_role": "__skill_focal__",
            "b_dim_id": "mysql-operational-monitoring-logging-and-diagnostics",
            "b_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
            "b_role": "MySQL DBA",
            "into": "d_merge_01",
            "into_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
            "merged_from": [
              "mysql-operational-monitoring-logging-and-diagnostics",
              "mysql-operational-monitoring-logging-and-diagnostics"
            ],
            "pair_kind": "cross_role",
            "reasoning": "Both dims describe the same DBA operational skill cluster: monitoring MySQL production health and using native logs/views to diagnose incidents. Dim A covers performance counters, error logs, slow query log, performance_schema, information_schema diagnostics, replication lag, connection health, lock waits, and alert thresholds. Dim B says the same thing and adds SHOW PROCESSLIST, status variables, and diagnostic queries. The exemplar skills in A (slow query log analysis, performance_schema, replication lag monitoring, database health checks) all fit B exactly. The cross-role label does not indicate a distinct cluster here.",
            "similarity": 0.8949410804136524
          }
        ],
        "placed": {
          "name": "Monitoring",
          "placement_confidence": 0.92,
          "primary_dimension": "d_merge_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 3 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "d_init_01",
            "d_init_02"
          ],
          "skill_id": "monitoring"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "observability",
            "security",
            "devops",
            "event-logs",
            "capacity-alerts",
            "cloudwatch",
            "failure-analysis",
            "restore-testing",
            "snapshot"
          ],
          "requires": [],
          "skill_id": "monitoring",
          "suppress_on_match": []
        },
        "skill_id": "monitoring",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.9,
          "name": "Monitoring",
          "reasoning": "Monitoring is fundamentally a knowledge unit about observing system health and behavior, so it fits the Concept type rather than a Tool or Platform.",
          "skill_id": "monitoring",
          "subtype": "observability_monitoring",
          "type": "Concept"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:42-\u003e3"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "alerting",
          "alias_type": "CANONICAL",
          "id": 975,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "alerting",
        "id": 665,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "alerting",
        "sub_category_id": 356,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Triage",
            "id": 61,
            "rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
            "slug": "observability-and-incident-triage",
            "source": "db"
          },
          "input_skill": "Alerting",
          "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"
            }
          ]
        }
      ],
      "input_skill": "Alerting",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Reinforcement Learning",
          "alias_type": "CANONICAL",
          "id": 3677,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Reinforcement Learning",
        "id": 2702,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "reinforcement-learning",
        "sub_category_id": 2207,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "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": "Reinforcement Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Reinforcement Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Graph Neural Networks",
          "alias_type": "CANONICAL",
          "id": 3678,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Graph Neural Networks",
        "id": 2703,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "graph-neural-networks",
        "sub_category_id": 2161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "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": "Graph Neural Networks",
          "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": "Graph Neural Networks",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Graph Neural Networks",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "RAG",
          "alias_type": "CANONICAL",
          "id": 3616,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "RAG",
        "id": 2659,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "rag",
        "sub_category_id": 2172,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "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": "RAG",
          "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": "Context Management and Retrieval",
            "id": 264,
            "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
            "slug": "context-management-and-retrieval",
            "source": "db"
          },
          "input_skill": "RAG",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 12,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "RAG",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Fine-tuning",
          "alias_type": "CANONICAL",
          "id": 3679,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "Fine-tuning",
        "id": 2704,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "fine-tuning",
        "sub_category_id": 2208,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Experiment Design and Analysis",
            "id": 87,
            "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
            "slug": "experiment-design-and-analysis",
            "source": "db"
          },
          "input_skill": "Fine-tuning",
          "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": "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": "Fine-tuning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Fine-tuning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Prompt Engineering",
          "alias_type": "CANONICAL",
          "id": 3658,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "Prompt Engineering",
        "id": 2683,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "prompt-engineering",
        "sub_category_id": 2191,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Context Management and Retrieval",
            "id": 264,
            "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
            "slug": "context-management-and-retrieval",
            "source": "db"
          },
          "input_skill": "Prompt Engineering",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 12,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Prompt Engineering",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Monitoring"
  ]
}

{
  "chosen_role": {
    "display_name": "Machine Learning Engineer",
    "id": 10,
    "rationale": "The primary skills indicate a focus on machine learning and related tools, aligning perfectly with the Machine Learning Engineer role.",
    "role_archetype": null,
    "slug": "machine-learning-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "PyTorch",
      "tag": "in_db"
    },
    {
      "skill": "TensorFlow",
      "tag": "in_db"
    },
    {
      "skill": "pandas",
      "tag": "in_db"
    },
    {
      "skill": "NumPy",
      "tag": "in_db"
    },
    {
      "skill": "OCR",
      "tag": "in_db"
    },
    {
      "skill": "A/B Testing",
      "tag": "in_db"
    },
    {
      "skill": "Feature Engineering",
      "tag": "in_db"
    },
    {
      "skill": "Model Serving",
      "tag": "in_db"
    },
    {
      "skill": "Monitoring",
      "tag": "new"
    },
    {
      "skill": "Alerting",
      "tag": "in_db"
    },
    {
      "skill": "Reinforcement Learning",
      "tag": "in_db"
    },
    {
      "skill": "Graph Neural Networks",
      "tag": "in_db"
    },
    {
      "skill": "RAG",
      "tag": "in_db"
    },
    {
      "skill": "Fine-tuning",
      "tag": "in_db"
    },
    {
      "skill": "Prompt Engineering",
      "tag": "in_db"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "Machine Learning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2672,
        "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": "Machine Learning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2672,
        "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",
          "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"
        },
        "dimension_id": 48,
        "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": "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"
          }
        ],
        "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 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"
        },
        "dimension_id": 361,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Virtualization Engineer",
            "id": 26,
            "rationale": null,
            "role_archetype": null,
            "slug": "virtualization-engineer",
            "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": "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"
        },
        "dimension_id": 285,
        "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": "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",
          "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"
        },
        "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"
          }
        ],
        "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 Backend Systems",
          "id": 140,
          "rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
          "slug": "programming-languages-for-backend-systems",
          "source": "db"
        },
        "dimension_id": 140,
        "input_skill": "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": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-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 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"
        },
        "dimension_id": 67,
        "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 Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-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 ML Systems",
          "id": 113,
          "rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 113,
        "input_skill": "Python",
        "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": 393,
        "skill_tag": "in_db",
        "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",
        "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": "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": "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": "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": "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": "pandas",
        "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": 474,
        "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": "NumPy",
        "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": 475,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "OCR",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2653,
        "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": "OCR",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2653,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Experiment Design and Analysis",
          "id": 87,
          "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
          "slug": "experiment-design-and-analysis",
          "source": "db"
        },
        "dimension_id": 87,
        "input_skill": "A/B Testing",
        "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": 506,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Feature Engineering and Selection",
          "id": 89,
          "rationale": "Creating, transforming, and selecting variables that improve model performance and interpretability. This is a coherent cluster because it bridges raw data understanding and model building.",
          "slug": "feature-engineering-and-selection",
          "source": "db"
        },
        "dimension_id": 89,
        "input_skill": "Feature Engineering",
        "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": 519,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model Serving Architecture",
          "id": 115,
          "rationale": "Patterns for hosting, routing, and scaling model inference in online or batch-serving applications. This covers how model calls are embedded into services, sidecars, gateways, or dedicated serving layers.",
          "slug": "model-serving-architecture",
          "source": "db"
        },
        "dimension_id": 115,
        "input_skill": "Model Serving",
        "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": 2701,
        "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": "Model Serving",
        "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": 2701,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Observability and Incident Triage",
          "id": 61,
          "rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
          "slug": "observability-and-incident-triage",
          "source": "db"
        },
        "dimension_id": 61,
        "input_skill": "Alerting",
        "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": 665,
        "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": "Reinforcement Learning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2702,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "Graph Neural Networks",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2703,
        "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": "Graph Neural Networks",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2703,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Service Integration Patterns",
          "id": 188,
          "rationale": "Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.",
          "slug": "cloud-service-integration-patterns",
          "source": "db"
        },
        "dimension_id": 188,
        "input_skill": "RAG",
        "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 Architect",
            "id": 11,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2659,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Context Management and Retrieval",
          "id": 264,
          "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
          "slug": "context-management-and-retrieval",
          "source": "db"
        },
        "dimension_id": 264,
        "input_skill": "RAG",
        "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2659,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Experiment Design and Analysis",
          "id": 87,
          "rationale": "Designing controlled experiments and analyzing outcomes to estimate causal impact. This cluster is central when the role needs to compare interventions, treatments, or product changes with rigor.",
          "slug": "experiment-design-and-analysis",
          "source": "db"
        },
        "dimension_id": 87,
        "input_skill": "Fine-tuning",
        "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": 2704,
        "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": "Fine-tuning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2704,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Context Management and Retrieval",
          "id": 264,
          "rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
          "slug": "context-management-and-retrieval",
          "source": "db"
        },
        "dimension_id": 264,
        "input_skill": "Prompt Engineering",
        "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"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2683,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
          "id": 166,
          "rationale": "Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.",
          "slug": "mysql-operational-monitoring-logging-and-diagnostics",
          "source": "db"
        },
        "dimension_id": 166,
        "input_skill": "Monitoring",
        "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": "MySQL DBA",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "mysql-dba",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2709,
        "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": "Monitoring",
        "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": 2709,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "Monitoring",
        "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": 2709,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 10,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
          "id": null,
          "rationale": "Monitoring MySQL production health and using MySQL-native logs, counters, and diagnostic views/queries to detect, investigate, explain, and respond to incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, lock and saturation checks, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, information_schema, status variables, and related diagnostic queries.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 166,
        "input_skill": "Monitoring",
        "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": 2709,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 1,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 3,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "0ecfddb7-617a-4645-868f-9266ac0de542"
}