Pipeline run
e1e635ef-e089-4209-a46d-962c62e094e9
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD
sources · ai_index: jd · nature_of_work: no_kras · tech_stack_maturity: jd
Nature of work
no kras
Vague JD — no KRAs present to derive a specific nature of work.
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
MLOps, ML
Evidence — skills matched in JD (0)
Skill cluster (0 dimension groups, role-scoped)
Status:
completed
Created: 2026-05-08T12:59:10.885556Z
Updated: 2026-05-08T13:01:15.668776Z
API 3 duration: 686 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
MLOps Engineer
slug: mlops-engineer · id: 5 · source: db
The dimensions strongly cluster around ML pipeline orchestration, deployment/runtime packaging, and MLOps tooling, with the clearest DB support for MLOps Engineer.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
27
Skipped
Job description
About the job Strong proficiency in Python and FastAPI. Experience with MLOps tools and ML experimentation platforms such as MLflow (Runs & Experiments) Expertise in model versioning and lifecycle management (model training) Knowledge of ML frameworks/libraries (e.g., TensorFlow, PyTorch) and orchestration tools such as MLflow, Ray.io, Kubeflow, and Airflow Familiarity with Docker, Kubernetes, and microservice-based architectures Experience with tools such as MLflow, LakeFS, MinIO, and NATS for asynchronous communication Knowledge of database design and best practices Understanding of RBAC and authentication mechanisms
history_view bundle (older API). Showing raw API payloads below.
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Kubernetes | in_db |
Orchestration Platforms
orchestration-platforms
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Docker | in_db |
Containerization and Image Delivery
containerization-and-image-delivery
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Docker | in_db |
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Analytical Programming Languages
analytical-programming-languages
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Automation Scripting and CLI
automation-scripting-and-cli
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Network Automation and Scripting
network-automation-and-scripting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Backend Systems
programming-languages-for-backend-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Test Automation
programming-languages-for-test-automation
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Security Automation and Scripting
security-automation-and-scripting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Airflow | in_db |
Workflow Orchestration Systems
workflow-orchestration-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| RBAC | in_db |
Authentication and Authorization
authentication-and-authorization
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| RBAC | in_db |
Data Governance and Access Control
data-governance-and-access-control
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| RBAC | in_db |
Secrets and Access Automation
secrets-and-access-automation
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| RBAC | in_db |
Secrets and Key Management
secrets-and-key-management
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| TensorFlow | in_db |
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| PyTorch | in_db |
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| FastAPI | in_db |
Inference Service Frameworks
inference-service-frameworks
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| FastAPI | in_db |
Web Service Frameworks
web-service-frameworks
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| NATS | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Kubeflow | new |
ML Pipeline Orchestration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| LakeFS | new |
Data Versioning and Branching
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| MLflow | new |
ML Experiment Tracking
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| MinIO | new |
Object Storage Systems
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Ray.io | new |
Distributed Workload Orchestration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Kubeflow | type=Framework subtype=ml_workflow_framework nature=FRAMEWORK lifespan=EVERGREEN | |
| canonical_skill_proposed | LakeFS | type=Tool subtype=data_versioning_tool nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | MLOps | type=Methodology subtype=machine_learning_operations nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | MLflow | type=Tool subtype=mlops_tool nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | MinIO | type=Datastore subtype=object_storage_datastore nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | Ray.io | type=Platform subtype=distributed_compute_platform nature=PLATFORM lifespan=EVERGREEN | |
| dimension_proposed | ML Pipeline Orchestration | |
| dimension_skill_link_proposed | Kubeflow ↔ ML Pipeline Orchestration | |
| dimension_proposed | Data Versioning and Branching | |
| dimension_skill_link_proposed | LakeFS ↔ Data Versioning and Branching | |
| dimension_proposed | ML Experiment Tracking | |
| dimension_skill_link_proposed | MLflow ↔ ML Experiment Tracking | |
| dimension_proposed | Object Storage Systems | |
| dimension_skill_link_proposed | MinIO ↔ Object Storage Systems | |
| dimension_proposed | Distributed Workload Orchestration | |
| dimension_skill_link_proposed | Ray.io ↔ Distributed Workload Orchestration |
API 1 — extract-from-jd click to toggle
{
"filtered_unknown_words": [
"Experience",
"Experiments",
"Expertise",
"Familiarity",
"Knowledge",
"Kubeflow",
"LakeFS",
"ML",
"MLOps",
"MLflow",
"MinIO",
"Ray.io",
"Runs",
"Understanding",
"architectures",
"authentication",
"communication",
"database",
"design",
"frameworks",
"job",
"libraries",
"lifecycle",
"management",
"mechanisms",
"microservice",
"model",
"orchestration",
"platforms",
"practices",
"proficiency",
"tools",
"training",
"versioning"
],
"final_non_skills": [
"Experience",
"Experiments",
"Expertise",
"Familiarity",
"Knowledge",
"ML",
"Runs",
"Understanding",
"architectures",
"authentication",
"communication",
"database",
"design",
"frameworks",
"job",
"libraries",
"lifecycle",
"management",
"mechanisms",
"microservice",
"model",
"orchestration",
"platforms",
"practices",
"proficiency",
"tools",
"training",
"versioning"
],
"final_skills": [
"Kubernetes",
"Docker",
"Python",
"Airflow",
"RBAC",
"TensorFlow",
"PyTorch",
"FastAPI",
"NATS",
"Kubeflow",
"LakeFS",
"MLOps",
"MLflow",
"MinIO",
"Ray.io"
],
"initial_skills": [
"Kubernetes",
"Docker",
"Python",
"Airflow",
"RBAC",
"TensorFlow",
"PyTorch",
"FastAPI",
"NATS"
],
"jd_role_hint": {
"display_name": "ML Platform Engineer",
"rationale": "The excerpt emphasizes MLOps tooling, ML frameworks, orchestration, and platform engineering rather than product development or data analysis.",
"role_archetype": "Builds and operates machine learning infrastructure, tooling, and workflows for experimentation, training, and deployment.",
"slug": "ml-platform-engineer"
},
"llm_non_skills": [
"Experience",
"Experiments",
"Expertise",
"Familiarity",
"Knowledge",
"ML",
"Runs",
"Understanding",
"architectures",
"authentication",
"communication",
"database",
"design",
"frameworks",
"job",
"libraries",
"lifecycle",
"management",
"mechanisms",
"microservice",
"model",
"orchestration",
"platforms",
"practices",
"proficiency",
"tools",
"training",
"versioning"
],
"llm_skills": [
"Kubeflow",
"LakeFS",
"MLOps",
"MLflow",
"MinIO",
"Ray.io"
],
"run_id": null,
"unknown_words": [
"Experience",
"Experiments",
"Expertise",
"Familiarity",
"Knowledge",
"Kubeflow",
"LakeFS",
"ML",
"MLOps",
"MLflow",
"MinIO",
"Ray.io",
"Runs",
"Understanding",
"architectures",
"authentication",
"communication",
"database",
"design",
"frameworks",
"job",
"libraries",
"lifecycle",
"management",
"mechanisms",
"microservice",
"model",
"orchestration",
"platforms",
"practices",
"proficiency",
"tools",
"training",
"versioning"
]
}
API 2 — extract-details
{
"alias_matches": [],
"candidate_roles": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
},
{
"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"
},
{
"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": "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": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "MLOps Engineer",
"id": 5,
"rationale": "The dimensions strongly cluster around ML pipeline orchestration, deployment/runtime packaging, and MLOps tooling, with the clearest DB support for MLOps Engineer.",
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Orchestration Platforms",
"id": 25,
"rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
"slug": "orchestration-platforms",
"source": "db"
},
"input_skill": "Kubernetes",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Delivery",
"id": 24,
"rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
"slug": "containerization-and-image-delivery",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Serving Deployment and Runtime Packaging",
"id": 52,
"rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
"slug": "model-serving-deployment-and-runtime-packaging",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "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 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": "Workflow Orchestration Systems",
"id": 64,
"rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
"slug": "workflow-orchestration-systems",
"source": "db"
},
"input_skill": "Airflow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Authentication and Authorization",
"id": 147,
"rationale": "Identity, session, and access-control mechanisms used to protect backend endpoints and service actions. This cluster is coherent because backend engineers often implement the server-side enforcement of who can do what.",
"slug": "authentication-and-authorization",
"source": "db"
},
"input_skill": "RBAC",
"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": "Data Governance and Access Control",
"id": 77,
"rationale": "Controls for sharing data safely across teams and environments. This includes permissions, masking, row-level security, and stewardship practices that keep data usable without exposing sensitive content.",
"slug": "data-governance-and-access-control",
"source": "db"
},
"input_skill": "RBAC",
"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": "Secrets and Access Automation",
"id": 33,
"rationale": "Automates secure handling of credentials, tokens, and access paths used by delivery systems and runtime environments. It is coherent because release tooling frequently needs controlled access to protected resources.",
"slug": "secrets-and-access-automation",
"source": "db"
},
"input_skill": "RBAC",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Secrets and Key Management",
"id": 39,
"rationale": "Manages Azure-native secret, key, and certificate storage used by cloud environments and supporting services. This cluster is distinct because secure credential handling is operationally critical and often integrated with platform access.",
"slug": "secrets-and-key-management",
"source": "db"
},
"input_skill": "RBAC",
"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"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Applied Machine Learning Toolkits",
"id": 94,
"rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
"slug": "applied-machine-learning-toolkits",
"source": "db"
},
"input_skill": "TensorFlow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Applied Machine Learning Toolkits",
"id": 94,
"rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
"slug": "applied-machine-learning-toolkits",
"source": "db"
},
"input_skill": "PyTorch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Inference Service Frameworks",
"id": 114,
"rationale": "Web and service frameworks used to expose model predictions through APIs and application endpoints. This cluster is coherent because MLEs often implement the runtime surface where requests enter and predictions leave the system.",
"slug": "inference-service-frameworks",
"source": "db"
},
"input_skill": "FastAPI",
"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": "Web Service Frameworks",
"id": 141,
"rationale": "Server frameworks used to build HTTP APIs, route requests, validate inputs, and structure backend application code. This cluster is coherent because it defines how backend services expose behavior to clients and other services.",
"slug": "web-service-frameworks",
"source": "db"
},
"input_skill": "FastAPI",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Event Streaming",
"id": 146,
"rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"input_skill": "NATS",
"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": null,
"display_name": "ML Pipeline Orchestration",
"id": null,
"rationale": "Covers building and running machine learning workflows as repeatable pipelines, including training, preprocessing, evaluation, and deployment steps. Kubeflow belongs here because it is primarily used to define and execute Kubernetes-native ML workflows.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Kubeflow",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Data Versioning and Branching",
"id": null,
"rationale": "Covers version control for datasets, branches, commits, merges, and reproducible data snapshots. LakeFS belongs here because it provides Git-like semantics for object storage and data lake assets.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "LakeFS",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "ML Experiment Tracking",
"id": null,
"rationale": "Covers tools and practices for logging experiments, parameters, metrics, artifacts, and model versions during ML development. MLflow belongs here because it is widely used to track runs, compare experiments, and manage model lifecycle metadata.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "MLflow",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Object Storage Systems",
"id": null,
"rationale": "Covers S3-compatible object storage platforms used to store and serve unstructured data, artifacts, and large binary assets. MinIO belongs here because it is an object storage server and client-facing storage layer rather than a database or file system.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "MinIO",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Distributed Workload Orchestration",
"id": null,
"rationale": "Covers platforms that schedule, distribute, and manage execution of containerized or service-based workloads across a cluster. Ray.io fits here because it provides a runtime for coordinating distributed tasks, actors, and jobs on shared infrastructure.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Ray.io",
"llm_role": null,
"roles_from_db": []
}
],
"input_final_skills": [
"Kubernetes",
"Docker",
"Python",
"Airflow",
"RBAC",
"TensorFlow",
"PyTorch",
"FastAPI",
"NATS",
"Kubeflow",
"LakeFS",
"MLOps",
"MLflow",
"MinIO",
"Ray.io"
],
"input_llm_skills": [
"Kubeflow",
"LakeFS",
"MLOps",
"MLflow",
"MinIO",
"Ray.io"
],
"new_aliases_persisted": 0,
"run_id": "e1e635ef-e089-4209-a46d-962c62e094e9",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Kubernetes",
"alias_type": "CANONICAL",
"id": 304,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0",
"alias_type": "VERSION",
"id": 307,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0+",
"alias_type": "VERSION",
"id": 2366,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.1",
"alias_type": "VERSION",
"id": 308,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.10",
"alias_type": "VERSION",
"id": 318,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.11",
"alias_type": "VERSION",
"id": 319,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.12",
"alias_type": "VERSION",
"id": 320,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.13",
"alias_type": "VERSION",
"id": 321,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.14",
"alias_type": "VERSION",
"id": 322,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.15",
"alias_type": "VERSION",
"id": 323,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.16",
"alias_type": "VERSION",
"id": 324,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.17",
"alias_type": "VERSION",
"id": 325,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.18",
"alias_type": "VERSION",
"id": 326,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.19",
"alias_type": "VERSION",
"id": 327,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.2",
"alias_type": "VERSION",
"id": 309,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.20",
"alias_type": "VERSION",
"id": 328,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.21",
"alias_type": "VERSION",
"id": 329,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.22",
"alias_type": "VERSION",
"id": 330,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.23",
"alias_type": "VERSION",
"id": 331,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.24",
"alias_type": "VERSION",
"id": 332,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.25",
"alias_type": "VERSION",
"id": 333,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.26",
"alias_type": "VERSION",
"id": 334,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.27",
"alias_type": "VERSION",
"id": 335,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.28",
"alias_type": "VERSION",
"id": 336,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.29",
"alias_type": "VERSION",
"id": 337,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.3",
"alias_type": "VERSION",
"id": 310,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.30",
"alias_type": "VERSION",
"id": 338,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.4",
"alias_type": "VERSION",
"id": 311,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.5",
"alias_type": "VERSION",
"id": 312,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.6",
"alias_type": "VERSION",
"id": 313,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.7",
"alias_type": "VERSION",
"id": 314,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.8",
"alias_type": "VERSION",
"id": 315,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.9",
"alias_type": "VERSION",
"id": 316,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.x",
"alias_type": "VERSION",
"id": 317,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes v1",
"alias_type": "VERSION",
"id": 306,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "k8s",
"alias_type": "VERSION",
"id": 305,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Kubernetes",
"id": 158,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
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},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Orchestration Platforms",
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"rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
"slug": "orchestration-platforms",
"source": "db"
},
"input_skill": "Kubernetes",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
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]
}
],
"input_skill": "Kubernetes",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Docker",
"alias_type": "CANONICAL",
"id": 299,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
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],
"canonical": {
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"display_name": "Docker",
"id": 153,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "docker",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Delivery",
"id": 24,
"rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
"slug": "containerization-and-image-delivery",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Serving Deployment and Runtime Packaging",
"id": 52,
"rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
"slug": "model-serving-deployment-and-runtime-packaging",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
}
],
"input_skill": "Docker",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"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,
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"alternatives_considered": [
"Framework: ruled out \u2014 although Ray provides libraries for building distributed applications, the named product is fundamentally a platform/ecosystem rather than an application framework.",
"Tool: ruled out \u2014 Ray can be self-hosted, but the skill name refers to the broader Ray.io platform rather than a standalone user-operated tool."
],
"confidence": 0.88,
"name": "Ray.io",
"reasoning": "By the Platform vs Tool rule, Ray.io is best treated as a hosted multi-tenant distributed compute platform with APIs rather than a single software tool you just run locally.",
"skill_id": "ray-io",
"subtype": "distributed_compute_platform",
"type": "Platform"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Kubeflow",
"LakeFS",
"MLOps",
"MLflow",
"MinIO",
"Ray.io"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "MLOps Engineer",
"id": 5,
"rationale": "The dimensions strongly cluster around ML pipeline orchestration, deployment/runtime packaging, and MLOps tooling, with the clearest DB support for MLOps Engineer.",
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
"final_input_skills": [
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Airflow",
"tag": "in_db"
},
{
"skill": "RBAC",
"tag": "in_db"
},
{
"skill": "TensorFlow",
"tag": "in_db"
},
{
"skill": "PyTorch",
"tag": "in_db"
},
{
"skill": "FastAPI",
"tag": "in_db"
},
{
"skill": "NATS",
"tag": "in_db"
},
{
"skill": "Kubeflow",
"tag": "new"
},
{
"skill": "LakeFS",
"tag": "new"
},
{
"skill": "MLOps",
"tag": "new"
},
{
"skill": "MLflow",
"tag": "new"
},
{
"skill": "MinIO",
"tag": "new"
},
{
"skill": "Ray.io",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Orchestration Platforms",
"id": 25,
"rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
"slug": "orchestration-platforms",
"source": "db"
},
"dimension_id": 25,
"input_skill": "Kubernetes",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 158,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Delivery",
"id": 24,
"rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
"slug": "containerization-and-image-delivery",
"source": "db"
},
"dimension_id": 24,
"input_skill": "Docker",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 153,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Serving Deployment and Runtime Packaging",
"id": 52,
"rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
"slug": "model-serving-deployment-and-runtime-packaging",
"source": "db"
},
"dimension_id": 52,
"input_skill": "Docker",
"llm_role": null,
"matched_chosen_role": true,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 153,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration Systems",
"id": 64,
"rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
"slug": "workflow-orchestration-systems",
"source": "db"
},
"dimension_id": 64,
"input_skill": "Airflow",
"llm_role": null,
"matched_chosen_role": true,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 325,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Authentication and Authorization",
"id": 147,
"rationale": "Identity, session, and access-control mechanisms used to protect backend endpoints and service actions. This cluster is coherent because backend engineers often implement the server-side enforcement of who can do what.",
"slug": "authentication-and-authorization",
"source": "db"
},
"dimension_id": 147,
"input_skill": "RBAC",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 202,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Data Governance and Access Control",
"id": 77,
"rationale": "Controls for sharing data safely across teams and environments. This includes permissions, masking, row-level security, and stewardship practices that keep data usable without exposing sensitive content.",
"slug": "data-governance-and-access-control",
"source": "db"
},
"dimension_id": 77,
"input_skill": "RBAC",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 202,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Secrets and Access Automation",
"id": 33,
"rationale": "Automates secure handling of credentials, tokens, and access paths used by delivery systems and runtime environments. It is coherent because release tooling frequently needs controlled access to protected resources.",
"slug": "secrets-and-access-automation",
"source": "db"
},
"dimension_id": 33,
"input_skill": "RBAC",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 202,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Secrets and Key Management",
"id": 39,
"rationale": "Manages Azure-native secret, key, and certificate storage used by cloud environments and supporting services. This cluster is distinct because secure credential handling is operationally critical and often integrated with platform access.",
"slug": "secrets-and-key-management",
"source": "db"
},
"dimension_id": 39,
"input_skill": "RBAC",
"llm_role": null,
"matched_chosen_role": false,
"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"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 202,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 558,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"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,
"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": false,
"skill_id": 557,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Inference Service Frameworks",
"id": 114,
"rationale": "Web and service frameworks used to expose model predictions through APIs and application endpoints. This cluster is coherent because MLEs often implement the runtime surface where requests enter and predictions leave the system.",
"slug": "inference-service-frameworks",
"source": "db"
},
"dimension_id": 114,
"input_skill": "FastAPI",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 682,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Service Frameworks",
"id": 141,
"rationale": "Server frameworks used to build HTTP APIs, route requests, validate inputs, and structure backend application code. This cluster is coherent because it defines how backend services expose behavior to clients and other services.",
"slug": "web-service-frameworks",
"source": "db"
},
"dimension_id": 141,
"input_skill": "FastAPI",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 682,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Messaging and Event Streaming",
"id": 146,
"rationale": "Asynchronous communication patterns and systems for decoupled service interaction and background processing. This is a coherent backend cluster because many server-side workflows depend on queues, topics, and event streams.",
"slug": "messaging-and-event-streaming",
"source": "db"
},
"dimension_id": 146,
"input_skill": "NATS",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 857,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": null,
"display_name": "ML Pipeline Orchestration",
"id": null,
"rationale": "Covers building and running machine learning workflows as repeatable pipelines, including training, preprocessing, evaluation, and deployment steps. Kubeflow belongs here because it is primarily used to define and execute Kubernetes-native ML workflows.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Kubeflow",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": null,
"display_name": "Data Versioning and Branching",
"id": null,
"rationale": "Covers version control for datasets, branches, commits, merges, and reproducible data snapshots. LakeFS belongs here because it provides Git-like semantics for object storage and data lake assets.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "LakeFS",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": null,
"display_name": "ML Experiment Tracking",
"id": null,
"rationale": "Covers tools and practices for logging experiments, parameters, metrics, artifacts, and model versions during ML development. MLflow belongs here because it is widely used to track runs, compare experiments, and manage model lifecycle metadata.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "MLflow",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": null,
"display_name": "Object Storage Systems",
"id": null,
"rationale": "Covers S3-compatible object storage platforms used to store and serve unstructured data, artifacts, and large binary assets. MinIO belongs here because it is an object storage server and client-facing storage layer rather than a database or file system.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "MinIO",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 5,
"dimension": {
"difficulty_hint": null,
"display_name": "Distributed Workload Orchestration",
"id": null,
"rationale": "Covers platforms that schedule, distribute, and manage execution of containerized or service-based workloads across a cluster. Ray.io fits here because it provides a runtime for coordinating distributed tasks, actors, and jobs on shared infrastructure.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Ray.io",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 27
},
"planner_output": null,
"run_id": "e1e635ef-e089-4209-a46d-962c62e094e9"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.
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