Pipeline run
3ae1d371-ca75-4dda-b3b6-5db504aeed29
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded
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)
0.00 / 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):
—
Evidence — skills matched in JD (0)
Skill cluster (0 dimension groups, role-scoped)
Status:
completed
Created: 2026-05-08T09:03:04.904533Z
Updated: 2026-05-08T09:04:57.066296Z
API 3 duration: 1218 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
DevOps Engineer
slug: devops-engineer · id: 1 · source: db
DevOps Engineer is the dominant role across the most dimensions, including IaC, observability, containerization, configuration management, orchestration, and cloud platform operations.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
47
Skipped
Job description
Job Description – DevOps Engineer Job Title: DevOps Engineer Experience: 3–6 Years Location: Hyderabad / Remote / Hybrid About the Role We are looking for a skilled DevOps Engineer to build, automate, and maintain scalable infrastructure and deployment pipelines. The ideal candidate should have strong experience in CI/CD, cloud platforms, containerization, and monitoring systems to support high-availability applications. Key Responsibilities Design and maintain CI/CD pipelines for automated build, test, and deployment processes Manage cloud infrastructure across AWS, Azure, or GCP environments Deploy and maintain containerized applications using Docker and Kubernetes Automate infrastructure provisioning using Infrastructure as Code tools Monitor system performance, uptime, and reliability using observability tools Collaborate with development and QA teams to improve deployment efficiency Implement security best practices and access control mechanisms Troubleshoot production issues and optimize system performance Maintain backup, disaster recovery, and scaling strategies Support release management and environment configuration activities Required Skills Strong experience with Linux system administration Hands-on experience with Docker and Kubernetes Experience with CI/CD tools like Jenkins, GitHub Actions, or GitLab CI Knowledge of cloud platforms such as AWS, Azure, or GCP Experience with Terraform, Ansible, or CloudFormation Understanding of networking, DNS, load balancing, and security concepts Familiarity with monitoring tools like Prometheus, Grafana, ELK, or Datadog Scripting knowledge in Bash, Python, or Shell scripting Preferred Qualifications Bachelor’s degree in Computer Science, IT, or related field Experience working in microservices-based architectures Knowledge of container orchestration and scaling strategies Cloud or Kubernetes certifications are a plus Nice to Have Exposure to SRE practices and incident management Experience with Kafka, Redis, or message queue systems Knowledge of security scanning and DevSecOps practices Benefits Flexible working hours Health and wellness benefits Learning and certification support Internet/work-from-home allowance Paid time off and holidays Collaborative engineering culture
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 |
|---|---|---|---|---|---|---|
| CloudFormation | in_db |
Infrastructure as Code
infrastructure-as-code
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Grafana | in_db |
Observability and Alerting
observability-and-alerting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Grafana | in_db |
Observability and Diagnostics
observability-and-diagnostics
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Microservices | in_db |
Service Architecture and Integration
service-architecture-and-integration
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| GitLab CI | in_db |
Continuous Integration Test Integration
continuous-integration-test-integration
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Jenkins | in_db |
Continuous Integration Test Integration
continuous-integration-test-integration
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Disaster Recovery | in_db |
Backup and Disaster Recovery
backup-and-disaster-recovery
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Bash | in_db |
Build and Execution Tooling
build-and-execution-tooling
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Prometheus | in_db |
Observability and Alerting
observability-and-alerting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Prometheus | in_db |
Observability and Diagnostics
observability-and-diagnostics
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Datadog | in_db |
Observability and Alerting
observability-and-alerting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| shell scripting | in_db |
Programming for Data Automation
programming-for-data-automation
|
— | — | — | 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) |
| Load Balancing | in_db |
Scaling and Resilience Engineering
scaling-and-resilience-engineering
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Redis | in_db |
NoSQL and Cache Stores
nosql-and-cache-stores
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Ansible | in_db |
Configuration Management
configuration-management
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Ansible | in_db |
Network Automation and Scripting
network-automation-and-scripting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Kubernetes | in_db |
Orchestration Platforms
orchestration-platforms
|
— | — | — | 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) |
| Terraform | in_db |
Infrastructure Provisioning Templates
infrastructure-provisioning-templates
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Terraform | in_db |
Infrastructure as Code
infrastructure-as-code
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Terraform | in_db |
Infrastructure as Code and Declarative Provisioning
infrastructure-as-code-and-declarative-provisioning
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Azure | in_db |
Cloud Platform Operations
cloud-platform-operations
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| GitHub Actions | in_db |
Continuous Integration Test Integration
continuous-integration-test-integration
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Kafka | in_db |
Messaging and Event Streaming
messaging-and-event-streaming
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| AWS | in_db |
Cloud Platform Operations
cloud-platform-operations
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| CI | new |
Continuous Integration Practices
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Cloud | new |
Cloud Platforms and Services
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| ELK | new |
Log Search and Analytics
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Exposure | new |
Exposure Measurement
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Hands | new |
Manual Dexterity and Hand Skills
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Infrastructure | new |
Infrastructure
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Knowledge | new |
Knowledge Representation
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Linux | new |
Linux System Administration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Monitor | new |
Operational Monitoring and Triage
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| SRE | new |
Site Reliability Engineering
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Scripting | new |
General Scripting
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Understanding | new |
Conceptual Understanding
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | CI | type=Methodology subtype=continuous_integration_methodology nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Cloud | type=Domain subtype=cloud_computing nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | ELK | type=Tool subtype=log_analysis_stack nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | Exposure | type=Concept subtype=security_exposure nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Hands | type=SoftSkill subtype=manual_dexterity nature=PRACTICE lifespan=EVERGREEN | |
| canonical_skill_proposed | Infrastructure | type=Domain subtype=infrastructure nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Knowledge | type=Concept subtype=general_knowledge nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Linux | type=Concept subtype=operating_system_concept nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Monitor | type=Tool subtype=monitoring_tool nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | QA | type=Methodology subtype=quality_assurance nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | SRE | type=Methodology subtype=site_reliability_engineering nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Scripting | type=Language subtype=scripting_language nature=LANGUAGE lifespan=EVERGREEN | |
| canonical_skill_proposed | Understanding | type=Concept subtype=comprehension nature=CONCEPT lifespan=EVERGREEN | |
| dimension_proposed | Continuous Integration Practices | |
| dimension_skill_link_proposed | CI ↔ Continuous Integration Practices | |
| dimension_proposed | Cloud Platforms and Services | |
| dimension_skill_link_proposed | Cloud ↔ Cloud Platforms and Services | |
| dimension_proposed | Log Search and Analytics | |
| dimension_skill_link_proposed | ELK ↔ Log Search and Analytics | |
| dimension_proposed | Exposure Measurement | |
| dimension_skill_link_proposed | Exposure ↔ Exposure Measurement | |
| dimension_proposed | Manual Dexterity and Hand Skills | |
| dimension_skill_link_proposed | Hands ↔ Manual Dexterity and Hand Skills | |
| dimension_proposed | Infrastructure | |
| dimension_skill_link_proposed | Infrastructure ↔ Infrastructure | |
| dimension_proposed | Knowledge Representation | |
| dimension_skill_link_proposed | Knowledge ↔ Knowledge Representation | |
| dimension_proposed | Linux System Administration | |
| dimension_skill_link_proposed | Linux ↔ Linux System Administration | |
| dimension_proposed | Operational Monitoring and Triage | |
| dimension_skill_link_proposed | Monitor ↔ Operational Monitoring and Triage | |
| dimension_proposed | Site Reliability Engineering | |
| dimension_skill_link_proposed | SRE ↔ Site Reliability Engineering | |
| dimension_proposed | General Scripting | |
| dimension_skill_link_proposed | Scripting ↔ General Scripting | |
| dimension_proposed | Conceptual Understanding | |
| dimension_skill_link_proposed | Understanding ↔ Conceptual Understanding |
API 1 — extract-from-jd click to toggle
{
"filtered_unknown_words": [
"3\u20136",
"Actions",
"Automate",
"Bachelor",
"Benefits",
"CD",
"CI",
"Cloud",
"Code",
"Computer",
"Description",
"Design",
"ELK",
"Engineer",
"Experience",
"Exposure",
"Familiarity",
"Hands",
"Health",
"Hybrid",
"Hyderabad",
"Infrastructure",
"Internet",
"Job",
"Key",
"Knowledge",
"Learning",
"Linux",
"Location",
"Monitor",
"Preferred",
"QA",
"Qualifications",
"Remote",
"Required",
"Responsibilities",
"Role",
"SRE",
"Science",
"Scripting",
"Skills",
"Support",
"Title",
"Understanding",
"Years",
"access",
"administration",
"allowance",
"applications",
"architectures",
"availability",
"balancing",
"benefits",
"build",
"candidate",
"certification",
"certifications",
"cloud",
"concepts",
"configuration",
"container",
"control",
"culture",
"degree",
"deployment",
"development",
"disaster",
"efficiency",
"engineering",
"environment",
"environments",
"experience",
"field",
"holidays",
"home",
"hours",
"incident",
"infrastructure",
"issues",
"knowledge",
"load",
"management",
"mechanisms",
"message",
"monitoring",
"networking",
"observability",
"orchestration",
"performance",
"pipelines",
"platforms",
"practices",
"processes",
"production",
"provisioning",
"queue",
"recovery",
"release",
"scaling",
"scanning",
"scripting",
"security",
"strategies",
"support",
"system",
"systems",
"teams",
"test",
"time",
"tools",
"wellness",
"work"
],
"final_non_skills": [
"3\u20136",
"Actions",
"Automate",
"Bachelor",
"Benefits",
"CD",
"Code",
"Computer",
"Description",
"Engineer",
"Experience",
"Familiarity",
"Health",
"Hybrid",
"Hyderabad",
"Internet",
"Job",
"Key",
"Learning",
"Location",
"Preferred",
"Qualifications",
"Remote",
"Required",
"Responsibilities",
"Role",
"Science",
"Skills",
"Support",
"Title",
"Years",
"allowance",
"candidate",
"certification",
"certifications",
"concepts",
"culture",
"degree",
"efficiency",
"engineering",
"field",
"holidays",
"home",
"hours",
"time",
"wellness",
"work"
],
"final_skills": [
"CloudFormation",
"Grafana",
"Microservices",
"GitLab CI",
"Jenkins",
"Disaster Recovery",
"Bash",
"Prometheus",
"Datadog",
"shell scripting",
"Docker",
"Load Balancing",
"Redis",
"Ansible",
"Kubernetes",
"Python",
"Terraform",
"Azure",
"GitHub Actions",
"Kafka",
"AWS",
"CI",
"Cloud",
"ELK",
"Exposure",
"Hands",
"Infrastructure",
"Knowledge",
"Linux",
"Monitor",
"QA",
"SRE",
"Scripting",
"Understanding",
"access",
"administration",
"applications",
"architectures",
"balancing",
"build",
"configuration",
"container",
"control",
"deployment",
"development",
"disaster",
"environment",
"environments",
"incident",
"issues",
"load",
"management",
"mechanisms",
"message",
"monitoring",
"networking",
"observability",
"orchestration",
"performance",
"pipelines",
"platforms",
"practices",
"processes",
"production",
"provisioning",
"queue",
"recovery",
"release",
"scaling",
"scanning",
"security",
"strategies",
"system",
"systems",
"test",
"tools"
],
"initial_skills": [
"CloudFormation",
"Grafana",
"Microservices",
"GitLab CI",
"Jenkins",
"Disaster Recovery",
"Bash",
"Prometheus",
"Datadog",
"shell scripting",
"Docker",
"Load Balancing",
"Redis",
"Ansible",
"Kubernetes",
"Python",
"Terraform",
"Azure",
"GitHub Actions",
"Kafka",
"AWS"
],
"jd_role_hint": null,
"llm_non_skills": [
"3\u20136",
"Actions",
"Automate",
"Bachelor",
"Benefits",
"CD",
"Code",
"Computer",
"Description",
"Engineer",
"Experience",
"Familiarity",
"Health",
"Hybrid",
"Hyderabad",
"Internet",
"Job",
"Key",
"Learning",
"Location",
"Preferred",
"Qualifications",
"Remote",
"Required",
"Responsibilities",
"Role",
"Science",
"Skills",
"Support",
"Title",
"Years",
"allowance",
"candidate",
"certification",
"certifications",
"concepts",
"culture",
"degree",
"efficiency",
"engineering",
"field",
"holidays",
"home",
"hours",
"time",
"wellness",
"work"
],
"llm_skills": [
"CI",
"Cloud",
"ELK",
"Exposure",
"Hands",
"Infrastructure",
"Knowledge",
"Linux",
"Monitor",
"QA",
"SRE",
"Scripting",
"Understanding",
"access",
"administration",
"applications",
"architectures",
"balancing",
"build",
"cloud",
"configuration",
"container",
"control",
"deployment",
"development",
"disaster",
"environment",
"environments",
"incident",
"infrastructure",
"issues",
"knowledge",
"load",
"management",
"mechanisms",
"message",
"monitoring",
"networking",
"observability",
"orchestration",
"performance",
"pipelines",
"platforms",
"practices",
"processes",
"production",
"provisioning",
"queue",
"recovery",
"release",
"scaling",
"scanning",
"scripting",
"security",
"strategies",
"system",
"systems",
"test",
"tools"
],
"run_id": null,
"unknown_words": [
"3\u20136",
"Actions",
"Automate",
"Bachelor",
"Benefits",
"CD",
"CI",
"Cloud",
"Code",
"Computer",
"Description",
"Design",
"ELK",
"Engineer",
"Experience",
"Exposure",
"Familiarity",
"Hands",
"Health",
"Hybrid",
"Hyderabad",
"Infrastructure",
"Internet",
"Job",
"Key",
"Knowledge",
"Learning",
"Linux",
"Location",
"Monitor",
"Preferred",
"QA",
"Qualifications",
"Remote",
"Required",
"Responsibilities",
"Role",
"SRE",
"Science",
"Scripting",
"Skills",
"Support",
"Title",
"Understanding",
"Years",
"access",
"administration",
"allowance",
"applications",
"architectures",
"availability",
"balancing",
"benefits",
"build",
"candidate",
"certification",
"certifications",
"cloud",
"concepts",
"configuration",
"container",
"control",
"culture",
"degree",
"deployment",
"development",
"disaster",
"efficiency",
"engineering",
"environment",
"environments",
"experience",
"field",
"holidays",
"home",
"hours",
"incident",
"infrastructure",
"issues",
"knowledge",
"load",
"management",
"mechanisms",
"message",
"monitoring",
"networking",
"observability",
"orchestration",
"performance",
"pipelines",
"platforms",
"practices",
"processes",
"production",
"provisioning",
"queue",
"recovery",
"release",
"scaling",
"scanning",
"scripting",
"security",
"strategies",
"support",
"system",
"systems",
"teams",
"test",
"time",
"tools",
"wellness",
"work"
]
}
API 2 — extract-details
{
"alias_matches": [],
"candidate_roles": [
{
"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": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
},
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"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": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "DevOps Engineer",
"id": 1,
"rationale": "DevOps Engineer is the dominant role across the most dimensions, including IaC, observability, containerization, configuration management, orchestration, and cloud platform operations.",
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 22,
"rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
"slug": "infrastructure-as-code",
"source": "db"
},
"input_skill": "CloudFormation",
"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": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"input_skill": "Grafana",
"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": "Observability and Diagnostics",
"id": 151,
"rationale": "Logging, metrics, tracing, dashboards, and debugging practices used to understand backend behavior in production. This cluster is coherent because backend engineers must detect failures, performance issues, and unexpected behavior.",
"slug": "observability-and-diagnostics",
"source": "db"
},
"input_skill": "Grafana",
"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": "Service Architecture and Integration",
"id": 148,
"rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
"slug": "service-architecture-and-integration",
"source": "db"
},
"input_skill": "Microservices",
"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": "Continuous Integration Test Integration",
"id": 207,
"rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
"slug": "continuous-integration-test-integration",
"source": "db"
},
"input_skill": "GitLab CI",
"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": "Continuous Integration Test Integration",
"id": 207,
"rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
"slug": "continuous-integration-test-integration",
"source": "db"
},
"input_skill": "Jenkins",
"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": "Backup and Disaster Recovery",
"id": 51,
"rationale": "Plans and operates backup, restore, and disaster recovery capabilities for Azure environments. This is a distinct cluster because recovery objectives and restore mechanics are specialized operational skills.",
"slug": "backup-and-disaster-recovery",
"source": "db"
},
"input_skill": "Disaster Recovery",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Build and Execution Tooling",
"id": 206,
"rationale": "Command-line and build tools used to run, package, and integrate automated tests into developer workflows. This cluster is coherent because automation testers often need to wire tests into local and shared execution paths.",
"slug": "build-and-execution-tooling",
"source": "db"
},
"input_skill": "Bash",
"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": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"input_skill": "Prometheus",
"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": "Observability and Diagnostics",
"id": 151,
"rationale": "Logging, metrics, tracing, dashboards, and debugging practices used to understand backend behavior in production. This cluster is coherent because backend engineers must detect failures, performance issues, and unexpected behavior.",
"slug": "observability-and-diagnostics",
"source": "db"
},
"input_skill": "Prometheus",
"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": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"input_skill": "Datadog",
"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": "Programming for Data Automation",
"id": 93,
"rationale": "Lightweight scripting used to automate repetitive analysis tasks, data preparation, and report generation. This is a useful split because data scientists often need practical automation without owning full pipelines.",
"slug": "programming-for-data-automation",
"source": "db"
},
"input_skill": "shell scripting",
"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": "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": "Scaling and Resilience Engineering",
"id": 45,
"rationale": "Implements Azure-side patterns that improve availability, capacity, and fault tolerance. This is a coherent cluster because the role must keep environments ready for production load and failure scenarios.",
"slug": "scaling-and-resilience-engineering",
"source": "db"
},
"input_skill": "Load Balancing",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "NoSQL and Cache Stores",
"id": 145,
"rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
"slug": "nosql-and-cache-stores",
"source": "db"
},
"input_skill": "Redis",
"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": "Configuration Management",
"id": 23,
"rationale": "Manages environment-specific settings, secrets wiring, and standardized runtime configuration across services. It is distinct from infrastructure provisioning because it focuses on how software is parameterized at deploy time.",
"slug": "configuration-management",
"source": "db"
},
"input_skill": "Ansible",
"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": "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": "Ansible",
"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": "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": "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": "Infrastructure Provisioning Templates",
"id": 291,
"rationale": "Declarative templates and modules used to create repeatable cloud resources and environments. This cluster covers the infrastructure definitions the role applies, reviews, and updates to keep environments consistent.",
"slug": "infrastructure-provisioning-templates",
"source": "db"
},
"input_skill": "Terraform",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 22,
"rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
"slug": "infrastructure-as-code",
"source": "db"
},
"input_skill": "Terraform",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code and Declarative Provisioning",
"id": 36,
"rationale": "Defines cloud and platform infrastructure declaratively through versioned code so environments are repeatable, reviewable, and automatable. This includes authoring and maintaining IaC templates/modules, managing parameters and state, and using plan/apply workflows to provision and update resources across Azure and other cloud platforms.",
"slug": "infrastructure-as-code-and-declarative-provisioning",
"source": "db"
},
"input_skill": "Terraform",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
"id": 26,
"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
"source": "db"
},
"input_skill": "Azure",
"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": "Continuous Integration Test Integration",
"id": 207,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"skill_id": "cloud",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers core cloud computing concepts, managed services, and platform primitives used to build and run workloads in public or private cloud environments. The skill Cloud belongs here because it is the umbrella concept for selecting, using, and operating cloud capabilities.",
"exemplar_skills": [
"Cloud",
"AWS",
"Azure",
"Google Cloud Platform",
"cloud computing",
"cloud services",
"serverless computing",
"cloud regions",
"availability zones"
],
"in_scope": "Cloud, AWS, Azure, Google Cloud Platform, compute, storage, networking, managed databases, serverless, containers, IAM basics, cloud regions and availability zones",
"name": "Cloud Platforms and Services",
"out_of_scope": "Cloud service selection and tradeoff analysis, which belongs to cloud-platform-service-selection; security controls and secret storage, which belong to security-focused dimensions; migration execution and data movement, which belong to infrastructure-provisioning-for-migration or data-transfer-and-synchronization",
"overlap_flags": [
{
"reason": "Cloud often includes choosing services, but that dimension is specifically about selection and fit-for-purpose tradeoffs.",
"with_dim_id": "cloud-platform-service-selection",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Cloud work frequently touches security design, but that dimension owns security-specific review and hardening.",
"with_dim_id": "security-architecture-review",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Cloud",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "cloud"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"cloud-run",
"cloud-reference-architecture",
"kubernetes"
],
"requires": [],
"skill_id": "cloud",
"suppress_on_match": []
},
"skill_id": "cloud",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.93,
"name": "Cloud",
"reasoning": "Cloud is best treated as a Domain because it names a broad technology/problem space rather than a specific hosted environment, software product, or architecture.",
"skill_id": "cloud",
"subtype": "cloud_computing",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Log Search and Analytics",
"id": null,
"rationale": "Covers indexing, searching, visualizing, and alerting on machine-generated logs and related operational telemetry. ELK belongs here because it is commonly used as a log analytics stack for observability and troubleshooting.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "ELK",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "ELK",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Tool",
"skill_nature": "TOOL",
"sub_category": "log_analysis_stack",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"elastic_stack",
"elk_stack"
],
"reasoning": "ELK is a common shorthand for the Elastic Stack, but in JDs it can also be read as the elk animal or other ELK acronyms. A reasonable extractor could confuse it with the broader Elastic Stack skill."
},
"context_keywords": {
"context_keywords": [
"Elasticsearch",
"Logstash",
"Kibana",
"Beats",
"Filebeat",
"Metricbeat",
"Apm Server",
"index patterns",
"dashboards",
"ingest pipelines",
"Grok",
"Lucene",
"X-Pack",
"watcher",
"cluster"
]
},
"maturity": {
"confidence": 0.92,
"maturity": "well_known",
"reasoning": "ELK (Elasticsearch, Logstash, Kibana) appears frequently in SRE/observability job descriptions and is a standard log-analysis stack across many enterprises."
},
"skill_id": "elk",
"vendor_license": {
"confidence": 0.92,
"license": "apache_2",
"vendor": "Elastic",
"year_introduced": 2010
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers indexing, searching, visualizing, and alerting on machine-generated logs and related operational telemetry. ELK belongs here because it is commonly used as a log analytics stack for observability and troubleshooting.",
"exemplar_skills": [
"ELK",
"Elasticsearch",
"Kibana",
"Logstash",
"Beats",
"log aggregation",
"log search",
"dashboarding"
],
"in_scope": "ELK, Elasticsearch indexing and search, Kibana dashboards and visualizations, Logstash pipelines, Beats shippers, log aggregation, query filters, alerting on log patterns, operational telemetry exploration",
"name": "Log Search and Analytics",
"out_of_scope": "Distributed tracing, metrics-only monitoring, SIEM threat hunting, application code instrumentation, database performance tuning",
"overlap_flags": [
{
"reason": "ELK is often used during incident triage and remediation, but this dimension owns the operational response workflow rather than the logging platform itself.",
"with_dim_id": "incident-response-and-remediation",
"with_dim_name": null,
"with_role": null
},
{
"reason": "ELK can support security log analysis and automation, but the core skill here is log indexing and search infrastructure.",
"with_dim_id": "security-automation-and-scripting",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "ELK",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "elk"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"dashboards",
"json"
],
"requires": [],
"skill_id": "elk",
"suppress_on_match": []
},
"skill_id": "elk",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "ELK",
"reasoning": "ELK refers to the Elasticsearch-Logstash-Kibana stack as software you operate, so by the Tool vs Framework rule it is best treated as a tool stack rather than a framework or platform.",
"skill_id": "elk",
"subtype": "log_analysis_stack",
"type": "Tool"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Exposure Measurement",
"id": null,
"rationale": "Covers how much a person, system, or dataset is exposed to a condition, risk, stimulus, or signal, and how that exposure is quantified or tracked. The skill belongs here when it refers to measuring or managing exposure as an analytical or operational concept rather than access control or security posture.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Exposure",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Exposure",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "security_exposure",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "In JDs, \"exposure\" as a security concept is usually context-specific and not a common standalone skill name; it\u2019s unlikely to be mistaken for another catalog skill."
},
"context_keywords": {
"context_keywords": [
"attack surface",
"vulnerability",
"threat modeling",
"risk assessment",
"misconfiguration",
"data exposure",
"information disclosure",
"privilege escalation",
"CVE",
"OWASP",
"security controls",
"least privilege",
"sensitive data",
"breach",
"hardening"
]
},
"maturity": {
"confidence": 0.78,
"maturity": "niche",
"reasoning": "\u201cExposure\u201d as a security concept appears in some JDs and risk frameworks, but it is not a standard standalone hiring skill; market demand is usually for specific controls like IAM, vuln mgmt, or attack surface management."
},
"skill_id": "exposure",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers how much a person, system, or dataset is exposed to a condition, risk, stimulus, or signal, and how that exposure is quantified or tracked. The skill belongs here when it refers to measuring or managing exposure as an analytical or operational concept rather than access control or security posture.",
"exemplar_skills": [
"Exposure",
"Exposure assessment",
"Exposure measurement",
"Risk exposure",
"Cumulative exposure",
"Environmental exposure"
],
"in_scope": "Exposure, exposure measurement, exposure assessment, exposure levels, exposure metrics, environmental exposure, risk exposure, dose exposure, signal exposure, cumulative exposure",
"name": "Exposure Measurement",
"out_of_scope": "Authentication, authorization, and access boundaries, data masking and permissions, network attack surface management, financial market exposure hedging, image or camera exposure settings",
"overlap_flags": [
{
"reason": "Exposure can be a data attribute being measured, but this dimension is about the exposure concept itself rather than dataset fitness or bias checking.",
"with_dim_id": "data-quality-assessment",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Security reviews may discuss exposure of assets or attack surface, but that is a different security-design concern than exposure measurement.",
"with_dim_id": "security-architecture-review",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Exposure",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "exposure"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"risk-based-testing",
"threat-modeling",
"fuzzing",
"sampling-bias",
"change-impact-analysis"
],
"requires": [],
"skill_id": "exposure",
"suppress_on_match": []
},
"skill_id": "exposure",
"split_log": [],
"typed": {
"alternatives_considered": [
"Domain: ruled out \u2014 it is not a vertical or problem-space body of knowledge.",
"Methodology: ruled out \u2014 it does not describe a way of working or process."
],
"confidence": 0.78,
"name": "Exposure",
"reasoning": "Exposure is best treated as a Concept because it names a knowledge unit about risk or vulnerability rather than a tool, format, or process.",
"skill_id": "exposure",
"subtype": "security_exposure",
"type": "Concept"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Manual Dexterity and Hand Skills",
"id": null,
"rationale": "Covers skills that rely on direct hand use, fine motor control, and manual manipulation of objects or tools. The target skill belongs here because it refers to the physical use of hands rather than a software, data, or systems concept.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Hands",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Hands",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "SoftSkill",
"skill_nature": "PRACTICE",
"sub_category": "manual_dexterity",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"handwriting",
"hand_tool_use"
],
"reasoning": "\u201cHands\u201d is a generic body-part term and in JDs could be mistaken for manual dexterity, handwriting, or hands-on/tool use rather than a distinct skill."
},
"context_keywords": {
"context_keywords": [
"fine motor skills",
"dexterity",
"hand-eye coordination",
"manual handling",
"assembly",
"precision work",
"tool use",
"repetitive tasks",
"grip strength",
"finger dexterity",
"steady hands",
"craftsmanship",
"machine operation",
"soldering",
"inspection"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "niche",
"reasoning": "Manual dexterity appears in few engineering JDs and is usually implicit in hardware/lab roles rather than a standalone hiring keyword; market demand is narrow compared with common software skills."
},
"skill_id": "hands",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers skills that rely on direct hand use, fine motor control, and manual manipulation of objects or tools. The target skill belongs here because it refers to the physical use of hands rather than a software, data, or systems concept.",
"exemplar_skills": [
"Hands",
"manual dexterity",
"fine motor control",
"hand-eye coordination",
"grip strength"
],
"in_scope": "Hands, fine motor control, manual dexterity, hand-eye coordination, gripping, pinching, manipulating small objects, repetitive hand motions",
"name": "Manual Dexterity and Hand Skills",
"out_of_scope": "Typing and keyboard shortcuts, mouse navigation, touch gestures, tool-specific operation, ergonomic workplace design, these belong to other interaction or equipment dimensions",
"overlap_flags": [],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Hands",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "hands"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "hands",
"suppress_on_match": []
},
"skill_id": "hands",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.97,
"name": "Hands",
"reasoning": "Hands is a non-technical physical capability rather than software, so it best fits the SoftSkill category.",
"skill_id": "hands",
"subtype": "manual_dexterity",
"type": "SoftSkill"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure",
"id": null,
"rationale": "Core compute, network, storage, and platform foundations that applications run on. This skill belongs here when it refers to the underlying environments, provisioning, connectivity, and operational building blocks rather than a specific app or tool layer.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Infrastructure",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Infrastructure",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "infrastructure",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cInfrastructure\u201d is a broad domain term, but in JDs it usually refers to the intended IT/ops domain rather than a distinct catalog skill with a competing meaning."
},
"context_keywords": {
"context_keywords": [
"Terraform",
"Ansible",
"Kubernetes",
"Docker",
"AWS",
"Azure",
"GCP",
"CI/CD",
"IaC",
"Linux",
"VMware",
"networking",
"load balancer",
"subnet",
"DNS"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Infrastructure is a core hiring-pipeline domain: job postings routinely ask for cloud, networking, CI/CD, and IaC skills, and major vendors (AWS/Azure/GCP) center it in their certification and product ecosystems."
},
"skill_id": "infrastructure",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Core compute, network, storage, and platform foundations that applications run on. This skill belongs here when it refers to the underlying environments, provisioning, connectivity, and operational building blocks rather than a specific app or tool layer.",
"exemplar_skills": [
"Infrastructure",
"Cloud infrastructure",
"Server provisioning",
"Network setup",
"Environment provisioning",
"Capacity planning"
],
"in_scope": "Infrastructure, servers, virtual machines, bare metal, cloud infrastructure, networking basics, storage, load balancers, DNS, firewalls, operating environments, environment setup, capacity planning",
"name": "Infrastructure",
"out_of_scope": "Application feature code, UI implementation, database schema design, security policy tuning, CI/CD pipeline configuration, observability dashboards",
"overlap_flags": [
{
"reason": "Infrastructure work often includes choosing cloud primitives, but that catalog dimension is specifically about selecting managed services rather than the broader platform foundation.",
"with_dim_id": "cloud-platform-service-selection",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Infrastructure can include storage and connectivity, but that dimension is narrower and focused on host-to-storage networking specifics.",
"with_dim_id": "storage-networking-and-connectivity",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Infrastructure may support service deployments, but service architecture is about how backend services are structured and connected logically.",
"with_dim_id": "service-architecture-and-integration",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Infrastructure",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "infrastructure"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"capacity-planning",
"capacity-forecasting",
"dependency-mapping",
"terraform",
"pulumi",
"thin-provisioning",
"acls",
"expressroute",
"endpoint-hardening",
"policy-enforcement",
"feature-flags",
"http",
"notifications",
"sqlite",
"protobuf",
"gradle",
"junit",
"livedata",
"navcontroller",
"hilt"
],
"requires": [],
"skill_id": "infrastructure",
"suppress_on_match": []
},
"skill_id": "infrastructure",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Infrastructure",
"reasoning": "Infrastructure is best treated as a Domain because it names a broad technical problem-space/body of knowledge rather than a specific system shape, tool, or methodology.",
"skill_id": "infrastructure",
"subtype": "infrastructure",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Knowledge Representation",
"id": null,
"rationale": "Represents structured knowledge used by AI systems to store, retrieve, and reason over facts, entities, and relationships. The skill \"Knowledge\" fits here when it refers to building or using knowledge bases, ontologies, or retrieval-augmented context.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Knowledge",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Knowledge",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "general_knowledge",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"knowledge_management",
"knowledge_graph",
"knowledge_base"
],
"reasoning": "\"Knowledge\" is a very broad concept and in JDs could easily refer to knowledge management, knowledge graphs, or a knowledge base rather than the generic concept."
},
"context_keywords": {
"context_keywords": [
"domain expertise",
"subject matter expert",
"SME",
"institutional knowledge",
"knowledge base",
"knowledge management",
"knowledge transfer",
"lessons learned",
"best practices",
"documentation",
"taxonomy",
"ontology",
"information architecture",
"expertise",
"know-how"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "niche",
"reasoning": "\u201cKnowledge\u201d is a generic concept, not a specific engineering skill; it has no meaningful JD volume or GitHub trend as a standalone hiring keyword, so market signal is effectively absent."
},
"skill_id": "knowledge",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Represents structured knowledge used by AI systems to store, retrieve, and reason over facts, entities, and relationships. The skill \"Knowledge\" fits here when it refers to building or using knowledge bases, ontologies, or retrieval-augmented context.",
"exemplar_skills": [
"Knowledge",
"Knowledge bases",
"Ontologies",
"Taxonomy design",
"Semantic graphs",
"Entity-relationship modeling"
],
"in_scope": "Knowledge, knowledge bases, ontologies, taxonomies, entity relationships, semantic graphs, retrieval-augmented knowledge, fact storage, domain knowledge modeling",
"name": "Knowledge Representation",
"out_of_scope": "Data quality assessment, statistical analysis, and dataset validation belong to data-quality-assessment; authentication, access control, and secrets handling belong to security and identity dimensions",
"overlap_flags": [
{
"reason": "Knowledge assets often need governance, but this dimension is about the structure and use of knowledge rather than permissions or stewardship.",
"with_dim_id": "data-governance-and-access-control",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Knowledge can be surfaced through tools, but tool orchestration is separate from the knowledge model itself.",
"with_dim_id": "tool-use-and-function-calling",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Knowledge",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "knowledge"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
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"suppress_on_match": []
},
"skill_id": "knowledge",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.98,
"name": "Knowledge",
"reasoning": "Knowledge is a named knowledge unit rather than a way of working, so by the Concept vs Methodology rule it is a Concept.",
"skill_id": "knowledge",
"subtype": "general_knowledge",
"type": "Concept"
},
"warnings": []
},
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"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Linux System Administration",
"id": null,
"rationale": "Covers operating and maintaining Linux-based systems, including shell usage, package management, services, permissions, filesystems, and troubleshooting. Linux belongs here because it is the core host operating system skill used to administer servers and developer environments.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Linux",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Linux",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "operating_system_concept",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
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"ambiguity_flag": false,
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},
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"shell scripting",
"systemd",
"cron",
"grep",
"sed",
"awk",
"SSH",
"iptables",
"SELinux",
"LVM",
"RAID",
"package manager",
"kernel",
"permissions"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Linux appears in a large share of DevOps, cloud, and backend job descriptions, and it underpins major vendor platforms like AWS, GCP, and Kubernetes distributions."
},
"skill_id": "linux",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers operating and maintaining Linux-based systems, including shell usage, package management, services, permissions, filesystems, and troubleshooting. Linux belongs here because it is the core host operating system skill used to administer servers and developer environments.",
"exemplar_skills": [
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"Unix administration",
"shell commands",
"systemd",
"package management",
"filesystem permissions",
"process management",
"log analysis"
],
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"name": "Linux System Administration",
"out_of_scope": "Container orchestration and image builds, which belong to containerization or build/release dimensions; database administration, which belongs to database-specific dimensions; application coding in Bash or Python, which belongs to programming-language dimensions",
"overlap_flags": [
{
"reason": "Linux administration often overlaps with incident triage and recovery when diagnosing host-level outages or service failures.",
"with_dim_id": "incident-response-and-remediation",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Linux skills frequently intersect with scripting for automation, but the scripting dimension owns the code-writing aspect.",
"with_dim_id": "security-automation-and-scripting",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
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"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "linux"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
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"bash",
"powershell",
"kubernetes",
"gke",
"ansible",
"bash-scripting",
"azure-virtual-machines"
],
"requires": [],
"skill_id": "linux",
"suppress_on_match": []
},
"skill_id": "linux",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Linux",
"reasoning": "Linux is best treated as a Concept here because the skill name refers to the operating system family/knowledge unit rather than a specific hosted platform, runtime, or user-operated tool.",
"skill_id": "linux",
"subtype": "operating_system_concept",
"type": "Concept"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Operational Monitoring and Triage",
"id": null,
"rationale": "Covers continuously observing systems, alerts, and health signals to detect degradations or failures early. The skill Monitor fits here when it means watching operational telemetry and deciding when to escalate.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Monitor",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Monitor",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Tool",
"skill_nature": "TOOL",
"sub_category": "monitoring_tool",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"monitoring"
],
"reasoning": "\"Monitor\" is a generic term and can be read as the broader monitoring skill/tool category rather than this specific tool name, so a JD extractor could confuse it with the catalog\u0027s monitoring skill."
},
"context_keywords": {
"context_keywords": [
"Prometheus",
"Grafana",
"alerting",
"dashboards",
"metrics",
"logs",
"traces",
"observability",
"SLI",
"SLO",
"APM",
"Nagios",
"Datadog",
"New Relic",
"health checks"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "niche",
"reasoning": "\"Monitor\" is too generic to show broad JD demand; market signals are fragmented across specific products like Prometheus, Datadog, and Grafana rather than a standalone skill."
},
"skill_id": "monitor",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Covers continuously observing systems, alerts, and health signals to detect degradations or failures early. The skill Monitor fits here when it means watching operational telemetry and deciding when to escalate.",
"exemplar_skills": [
"Monitor",
"alerting",
"health checks",
"metric thresholds",
"log review",
"anomaly detection",
"incident triage"
],
"in_scope": "Monitor, alerting, health checks, dashboards, log review, metric thresholds, anomaly detection, service degradation detection, incident triage, escalation triggers",
"name": "Operational Monitoring and Triage",
"out_of_scope": "Root-cause analysis, postmortems, and long-term fixes, which belong to incident response and remediation; business reporting dashboards, which belong to reporting and dashboard configuration",
"overlap_flags": [
{
"reason": "Monitoring often feeds incident response, but this dimension is specifically about observing and detecting issues rather than restoring service.",
"with_dim_id": "incident-response-and-remediation",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Both may use dashboards, but this dimension is about operational alerting and health surveillance, not reporting analytics.",
"with_dim_id": "reporting-and-dashboard-configuration",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Monitor",
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"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "monitor"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"azure-monitor",
"dashboards",
"health-checks",
"notifications",
"replication-lag-monitoring"
],
"requires": [],
"skill_id": "monitor",
"suppress_on_match": []
},
"skill_id": "monitor",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.91,
"name": "Monitor",
"reasoning": "Monitor is software you operate to observe systems, so by the Tool vs Framework rule it is a Tool rather than a framework or platform.",
"skill_id": "monitor",
"subtype": "monitoring_tool",
"type": "Tool"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "QA",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "quality_assurance",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
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"ambiguity": {
"ambiguity_flag": true,
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"test_automation"
],
"reasoning": "\"QA\" is a common abbreviation for quality assurance, but in JDs it can also be used loosely for quality control or testing work, so an extractor could confuse it with nearby quality-related skills."
},
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"test cases",
"test plans",
"test scripts",
"regression testing",
"smoke testing",
"UAT",
"defect tracking",
"bug triage",
"test automation",
"Selenium",
"JIRA",
"manual testing",
"acceptance criteria",
"traceability matrix",
"test coverage"
]
},
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"maturity": "well_known",
"reasoning": "QA appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; market demand is broad across manual and automated testing roles."
},
"skill_id": "qa",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [],
"merge_log": [
{
"into": "d_merge_01",
"into_name": "Requirements and Acceptance Validation",
"merged_from": [
"d_init_01",
"requirements-and-acceptance-validation"
],
"reasoning": "Dim A and Dim B describe the same QA-oriented cluster: validating implemented behavior against documented requirements, user stories, and acceptance criteria. Their descriptions overlap almost verbatim, and the exemplar skills in A \u2014 QA, acceptance testing, requirements validation, manual testing, and user story verification \u2014 all fit B\u2019s description as well. The only substantive difference is that A adds a bit more scope control (e.g., explicitly excluding frontend unit tests, automation framework coding, incident triage, and security review), but that does not create a separate conceptual anchor; it just clarifies boundaries around the same validation activity. Since both dimensions center on manual/acceptance-style verification of expected product behavior, they should be unified."
}
],
"placed": {
"name": "QA",
"placement_confidence": 0.0,
"primary_dimension": "d_init_00",
"reasoning": "Stub placement: no locked_dimensions after Stage 2/3; downstream containment and enrichment use placeholders only.",
"secondary_dimensions": [],
"skill_id": "qa"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"acceptance-criteria-checks",
"acceptance-criteria-validation",
"test-case-authoring",
"defect-retest",
"risk-based-testing",
"cross-browser-testing",
"session-based-testing",
"charter-based-testing",
"health-checks"
],
"requires": [],
"skill_id": "qa",
"suppress_on_match": []
},
"skill_id": "qa",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 QA is more about an operational process than a named theory or principle.",
"SoftSkill: ruled out \u2014 although people skills can be involved, QA itself is not primarily an interpersonal capability."
],
"confidence": 0.82,
"name": "QA",
"reasoning": "By the Concept vs Methodology rule, QA is best understood as a way of working focused on assuring quality rather than a standalone knowledge unit, so Methodology is the closest fit.",
"skill_id": "qa",
"subtype": "quality_assurance",
"type": "Methodology"
},
"warnings": [
"placement_stub_no_locked_dimensions"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Site Reliability Engineering",
"id": null,
"rationale": "Practices for operating production services with high availability, low latency, and fast recovery. This covers the core SRE discipline of using engineering methods to prevent incidents, manage risk, and improve service reliability.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "SRE",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "SRE",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "site_reliability_engineering",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "SRE is a well-known, specific acronym for Site Reliability Engineering in JDs; it is unlikely to be reasonably confused with a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"SLI",
"SLO",
"SLA",
"error budget",
"incident response",
"postmortem",
"blameless postmortem",
"on-call",
"pager duty",
"observability",
"Prometheus",
"Grafana",
"alerting",
"capacity planning",
"service reliability"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "SRE appears in many production engineering job descriptions and is a standard hiring keyword at large tech firms; Google\u2019s published SRE practices also made it a mainstream ops discipline."
},
"skill_id": "sre",
"vendor_license": {
"confidence": 0.98,
"license": null,
"vendor": "Google",
"year_introduced": 2003
},
"versioning": {
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"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Practices for operating production services with high availability, low latency, and fast recovery. This covers the core SRE discipline of using engineering methods to prevent incidents, manage risk, and improve service reliability.",
"exemplar_skills": [
"SRE",
"service level objectives (SLOs)",
"service level indicators (SLIs)",
"error budgets",
"toil reduction",
"capacity planning",
"production readiness",
"operational automation"
],
"in_scope": "SRE, service level objectives (SLOs), service level indicators (SLIs), error budgets, toil reduction, capacity planning, availability engineering, reliability reviews, production readiness, operational automation, incident prevention",
"name": "Site Reliability Engineering",
"out_of_scope": "Incident triage and postmortems after an outage, which belong to incident response and remediation; service-specific inference scaling and rollout safety, which belong to model service reliability; security hardening and access controls, which belong to security architecture or identity and access configuration",
"overlap_flags": [
{
"reason": "SRE overlaps with outage handling, but this dimension focuses on preventing and managing reliability rather than the remediation workflow itself.",
"with_dim_id": "incident-response-and-remediation",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Both address production reliability, but model-service-reliability is specific to inference services and ML deployment behavior.",
"with_dim_id": "model-service-reliability",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
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"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "sre"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"recovery-procedures",
"right-sizing",
"risk-based-testing",
"shutdown-sequencing",
"reconciliation"
],
"requires": [],
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"suppress_on_match": []
},
"skill_id": "sre",
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"typed": {
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"confidence": 0.93,
"name": "SRE",
"reasoning": "SRE is fundamentally a way of working for operating and improving services, so by the Concept vs Methodology rule it fits Methodology rather than a tool or architecture.",
"skill_id": "sre",
"subtype": "site_reliability_engineering",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "General Scripting",
"id": null,
"rationale": "Writing small automation programs and command-line scripts to perform repetitive tasks, glue systems together, or transform data. This fits the target skill because scripting is the broad practice of authoring lightweight executable code for automation.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Scripting",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Scripting",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Language",
"skill_nature": "LANGUAGE",
"sub_category": "scripting_language",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"shell_scripting",
"python",
"javascript"
],
"reasoning": "\u201cScripting\u201d is a broad umbrella term, not a specific language. In JDs it could mean shell scripting, Python, JavaScript, or other scripting languages, so an extractor could reasonably map it to multiple catalog skills."
},
"context_keywords": {
"context_keywords": [
"Bash",
"Shell",
"PowerShell",
"Perl",
"Ruby",
"Python",
"automation",
"CLI",
"cron",
"sed",
"awk",
"regex",
"Unix",
"batch files",
"Jenkins"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "Scripting is a broad hiring staple: job postings commonly ask for scripting in Python/Bash/PowerShell for automation, CI/CD, and ops tasks across many stacks."
},
"skill_id": "scripting",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "Writing small automation programs and command-line scripts to perform repetitive tasks, glue systems together, or transform data. This fits the target skill because scripting is the broad practice of authoring lightweight executable code for automation.",
"exemplar_skills": [
"Scripting",
"Shell scripting",
"Python scripting",
"PowerShell scripting",
"Command-line automation",
"Text processing"
],
"in_scope": "Scripting, shell scripts, Python scripts, PowerShell scripts, Ruby scripts, command-line automation, file manipulation, text processing, task automation",
"name": "General Scripting",
"out_of_scope": "Full application development, security-specific automation, database administration scripts, build/release pipelines, UI feature implementation, owned by more specialized engineering dimensions",
"overlap_flags": [
{
"reason": "Security-focused scripts are a specialized subset when the primary purpose is checks, enrichment, or remediation.",
"with_dim_id": "security-automation-and-scripting",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Database operational scripts are a narrower DBA-oriented use of scripting.",
"with_dim_id": "mysql-automation-and-scripting",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Some scripts are written in general-purpose backend languages, but the skill here is the automation style rather than backend service development.",
"with_dim_id": "programming-languages-for-backend-systems",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Scripting",
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"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "scripting"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"bash-scripting",
"powershell",
"python-scripting",
"typescript",
"bash",
"javascript",
"python",
"java"
],
"requires": [],
"skill_id": "scripting",
"suppress_on_match": []
},
"skill_id": "scripting",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Scripting",
"reasoning": "Scripting is fundamentally a programming language category rather than a tool or methodology, since it refers to writing executable code in a language used to automate tasks.",
"skill_id": "scripting",
"subtype": "scripting_language",
"type": "Language"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Conceptual Understanding",
"id": null,
"rationale": "General comprehension of a topic, process, or system at a level sufficient to explain it, reason about it, and apply it correctly. This is the best fit for the standalone skill \"Understanding\" when no narrower technical dimension is implied.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Understanding",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Understanding",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "comprehension",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"understanding_customer_needs",
"understanding_business_requirements"
],
"reasoning": "\"Understanding\" is too generic for JDs and could easily be extracted as broader comprehension or as domain-specific phrases like understanding customer needs or business requirements."
},
"context_keywords": {
"context_keywords": [
"requirements gathering",
"stakeholder needs",
"problem framing",
"root cause analysis",
"domain knowledge",
"user stories",
"acceptance criteria",
"specification",
"gap analysis",
"business context",
"trade-offs",
"edge cases",
"requirements elicitation",
"system behavior",
"inference"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "well_known",
"reasoning": "Broadly expected in JDs across engineering roles as a core comprehension skill; unlike niche tools, it appears implicitly in interview rubrics and competency frameworks rather than as a specialized stack item."
},
"skill_id": "understanding",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"locked_dimensions": [
{
"description": "General comprehension of a topic, process, or system at a level sufficient to explain it, reason about it, and apply it correctly. This is the best fit for the standalone skill \"Understanding\" when no narrower technical dimension is implied.",
"exemplar_skills": [
"Understanding",
"Comprehension",
"Conceptual understanding",
"Interpreting concepts",
"Explaining technical ideas"
],
"in_scope": "Understanding, comprehension, grasping concepts, explaining ideas in plain language, interpreting requirements, recognizing relationships, basic reasoning about a topic",
"name": "Conceptual Understanding",
"out_of_scope": "Deep domain implementation, coding, debugging, testing, or operational execution, which belong to more specific technical dimensions such as programming, validation, or architecture",
"overlap_flags": [
{
"reason": "Understanding can be used to interpret requirements, but that dimension is specifically about verifying implemented behavior against acceptance criteria.",
"with_dim_id": "requirements-and-acceptance-validation",
"with_dim_name": null,
"with_role": null
},
{
"reason": "Understanding security designs may support review work, but the review dimension owns the actual evaluation of security weaknesses.",
"with_dim_id": "security-architecture-review",
"with_dim_name": null,
"with_role": null
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Understanding",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "understanding"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "understanding",
"suppress_on_match": []
},
"skill_id": "understanding",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.93,
"name": "Understanding",
"reasoning": "Understanding is a named knowledge unit rather than a way of working, so by the Concept vs Methodology rule it fits Concept.",
"skill_id": "understanding",
"subtype": "comprehension",
"type": "Concept"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "access",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "administration",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "applications",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "architectures",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "balancing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "build",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "configuration",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "container",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "control",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "deployment",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "development",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "disaster",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "environment",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "environments",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "incident",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "issues",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "load",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "management",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "mechanisms",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "message",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "monitoring",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "networking",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "observability",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "orchestration",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "performance",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "pipelines",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "platforms",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "practices",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "processes",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "production",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "provisioning",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "queue",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "recovery",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "release",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "scaling",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "scanning",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "security",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "strategies",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "system",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "systems",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "test",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "tools",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"CI",
"Cloud",
"ELK",
"Exposure",
"Hands",
"Infrastructure",
"Knowledge",
"Linux",
"Monitor",
"QA",
"SRE",
"Scripting",
"Understanding"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "DevOps Engineer",
"id": 1,
"rationale": "DevOps Engineer is the dominant role across the most dimensions, including IaC, observability, containerization, configuration management, orchestration, and cloud platform operations.",
"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"
},
"final_input_skills": [
{
"skill": "CloudFormation",
"tag": "in_db"
},
{
"skill": "Grafana",
"tag": "in_db"
},
{
"skill": "Microservices",
"tag": "in_db"
},
{
"skill": "GitLab CI",
"tag": "in_db"
},
{
"skill": "Jenkins",
"tag": "in_db"
},
{
"skill": "Disaster Recovery",
"tag": "in_db"
},
{
"skill": "Bash",
"tag": "in_db"
},
{
"skill": "Prometheus",
"tag": "in_db"
},
{
"skill": "Datadog",
"tag": "in_db"
},
{
"skill": "shell scripting",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "Load Balancing",
"tag": "in_db"
},
{
"skill": "Redis",
"tag": "in_db"
},
{
"skill": "Ansible",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Terraform",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "GitHub Actions",
"tag": "in_db"
},
{
"skill": "Kafka",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "CI",
"tag": "new"
},
{
"skill": "Cloud",
"tag": "new"
},
{
"skill": "ELK",
"tag": "new"
},
{
"skill": "Exposure",
"tag": "new"
},
{
"skill": "Hands",
"tag": "new"
},
{
"skill": "Infrastructure",
"tag": "new"
},
{
"skill": "Knowledge",
"tag": "new"
},
{
"skill": "Linux",
"tag": "new"
},
{
"skill": "Monitor",
"tag": "new"
},
{
"skill": "QA",
"tag": "new"
},
{
"skill": "SRE",
"tag": "new"
},
{
"skill": "Scripting",
"tag": "new"
},
{
"skill": "Understanding",
"tag": "new"
},
{
"skill": "access",
"tag": "new"
},
{
"skill": "administration",
"tag": "new"
},
{
"skill": "applications",
"tag": "new"
},
{
"skill": "architectures",
"tag": "new"
},
{
"skill": "balancing",
"tag": "new"
},
{
"skill": "build",
"tag": "new"
},
{
"skill": "configuration",
"tag": "new"
},
{
"skill": "container",
"tag": "new"
},
{
"skill": "control",
"tag": "new"
},
{
"skill": "deployment",
"tag": "new"
},
{
"skill": "development",
"tag": "new"
},
{
"skill": "disaster",
"tag": "new"
},
{
"skill": "environment",
"tag": "new"
},
{
"skill": "environments",
"tag": "new"
},
{
"skill": "incident",
"tag": "new"
},
{
"skill": "issues",
"tag": "new"
},
{
"skill": "load",
"tag": "new"
},
{
"skill": "management",
"tag": "new"
},
{
"skill": "mechanisms",
"tag": "new"
},
{
"skill": "message",
"tag": "new"
},
{
"skill": "monitoring",
"tag": "new"
},
{
"skill": "networking",
"tag": "new"
},
{
"skill": "observability",
"tag": "new"
},
{
"skill": "orchestration",
"tag": "new"
},
{
"skill": "performance",
"tag": "new"
},
{
"skill": "pipelines",
"tag": "new"
},
{
"skill": "platforms",
"tag": "new"
},
{
"skill": "practices",
"tag": "new"
},
{
"skill": "processes",
"tag": "new"
},
{
"skill": "production",
"tag": "new"
},
{
"skill": "provisioning",
"tag": "new"
},
{
"skill": "queue",
"tag": "new"
},
{
"skill": "recovery",
"tag": "new"
},
{
"skill": "release",
"tag": "new"
},
{
"skill": "scaling",
"tag": "new"
},
{
"skill": "scanning",
"tag": "new"
},
{
"skill": "security",
"tag": "new"
},
{
"skill": "strategies",
"tag": "new"
},
{
"skill": "system",
"tag": "new"
},
{
"skill": "systems",
"tag": "new"
},
{
"skill": "test",
"tag": "new"
},
{
"skill": "tools",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 22,
"rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
"slug": "infrastructure-as-code",
"source": "db"
},
"dimension_id": 22,
"input_skill": "CloudFormation",
"llm_role": null,
"matched_chosen_role": true,
"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": 145,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"dimension_id": 27,
"input_skill": "Grafana",
"llm_role": null,
"matched_chosen_role": true,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
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"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 169,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Diagnostics",
"id": 151,
"rationale": "Logging, metrics, tracing, dashboards, and debugging practices used to understand backend behavior in production. This cluster is coherent because backend engineers must detect failures, performance issues, and unexpected behavior.",
"slug": "observability-and-diagnostics",
"source": "db"
},
"dimension_id": 151,
"input_skill": "Grafana",
"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": 169,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Service Architecture and Integration",
"id": 148,
"rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
"slug": "service-architecture-and-integration",
"source": "db"
},
"dimension_id": 148,
"input_skill": "Microservices",
"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": 864,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Continuous Integration Test Integration",
"id": 207,
"rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
"slug": "continuous-integration-test-integration",
"source": "db"
},
"dimension_id": 207,
"input_skill": "GitLab CI",
"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": 1251,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Continuous Integration Test Integration",
"id": 207,
"rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
"slug": "continuous-integration-test-integration",
"source": "db"
},
"dimension_id": 207,
"input_skill": "Jenkins",
"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": 1249,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Backup and Disaster Recovery",
"id": 51,
"rationale": "Plans and operates backup, restore, and disaster recovery capabilities for Azure environments. This is a distinct cluster because recovery objectives and restore mechanics are specialized operational skills.",
"slug": "backup-and-disaster-recovery",
"source": "db"
},
"dimension_id": 51,
"input_skill": "Disaster Recovery",
"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"
}
],
"skill_dimension_saved": false,
"skill_id": 278,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Build and Execution Tooling",
"id": 206,
"rationale": "Command-line and build tools used to run, package, and integrate automated tests into developer workflows. This cluster is coherent because automation testers often need to wire tests into local and shared execution paths.",
"slug": "build-and-execution-tooling",
"source": "db"
},
"dimension_id": 206,
"input_skill": "Bash",
"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": 1248,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"dimension_id": 27,
"input_skill": "Prometheus",
"llm_role": null,
"matched_chosen_role": true,
"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": 168,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Diagnostics",
"id": 151,
"rationale": "Logging, metrics, tracing, dashboards, and debugging practices used to understand backend behavior in production. This cluster is coherent because backend engineers must detect failures, performance issues, and unexpected behavior.",
"slug": "observability-and-diagnostics",
"source": "db"
},
"dimension_id": 151,
"input_skill": "Prometheus",
"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": 168,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Alerting",
"id": 27,
"rationale": "Builds feedback loops for system health through metrics, logs, traces, dashboards, and alerts. This cluster is coherent because it turns runtime behavior into actionable operational signals.",
"slug": "observability-and-alerting",
"source": "db"
},
"dimension_id": 27,
"input_skill": "Datadog",
"llm_role": null,
"matched_chosen_role": true,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
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"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": 171,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming for Data Automation",
"id": 93,
"rationale": "Lightweight scripting used to automate repetitive analysis tasks, data preparation, and report generation. This is a useful split because data scientists often need practical automation without owning full pipelines.",
"slug": "programming-for-data-automation",
"source": "db"
},
"dimension_id": 93,
"input_skill": "shell scripting",
"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": 549,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"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": true,
"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": 1,
"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": false,
"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": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Scaling and Resilience Engineering",
"id": 45,
"rationale": "Implements Azure-side patterns that improve availability, capacity, and fault tolerance. This is a coherent cluster because the role must keep environments ready for production load and failure scenarios.",
"slug": "scaling-and-resilience-engineering",
"source": "db"
},
"dimension_id": 45,
"input_skill": "Load Balancing",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 275,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "NoSQL and Cache Stores",
"id": 145,
"rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
"slug": "nosql-and-cache-stores",
"source": "db"
},
"dimension_id": 145,
"input_skill": "Redis",
"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": 846,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Configuration Management",
"id": 23,
"rationale": "Manages environment-specific settings, secrets wiring, and standardized runtime configuration across services. It is distinct from infrastructure provisioning because it focuses on how software is parameterized at deploy time.",
"slug": "configuration-management",
"source": "db"
},
"dimension_id": 23,
"input_skill": "Ansible",
"llm_role": null,
"matched_chosen_role": true,
"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": 147,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"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": "Ansible",
"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": 147,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"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": true,
"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",
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}
],
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"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": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure Provisioning Templates",
"id": 291,
"rationale": "Declarative templates and modules used to create repeatable cloud resources and environments. This cluster covers the infrastructure definitions the role applies, reviews, and updates to keep environments consistent.",
"slug": "infrastructure-provisioning-templates",
"source": "db"
},
"dimension_id": 291,
"input_skill": "Terraform",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 144,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 22,
"rationale": "Defines infrastructure and platform resources through versioned code so environments are repeatable and reviewable. This is a coherent cluster because it underpins environment consistency and change control.",
"slug": "infrastructure-as-code",
"source": "db"
},
"dimension_id": 22,
"input_skill": "Terraform",
"llm_role": null,
"matched_chosen_role": true,
"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": 144,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code and Declarative Provisioning",
"id": 36,
"rationale": "Defines cloud and platform infrastructure declaratively through versioned code so environments are repeatable, reviewable, and automatable. This includes authoring and maintaining IaC templates/modules, managing parameters and state, and using plan/apply workflows to provision and update resources across Azure and other cloud platforms.",
"slug": "infrastructure-as-code-and-declarative-provisioning",
"source": "db"
},
"dimension_id": 36,
"input_skill": "Terraform",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 144,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
"id": 26,
"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
"source": "db"
},
"dimension_id": 26,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": true,
"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": 164,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Continuous Integration Test Integration",
"id": 207,
"rationale": "Integrating automated checks into shared build and merge workflows so results are repeatable and visible. This cluster is coherent because automation testers commonly configure test execution triggers, artifacts, and reporting hooks.",
"slug": "continuous-integration-test-integration",
"source": "db"
},
"dimension_id": 207,
"input_skill": "GitHub Actions",
"llm_role": null,
"matched_chosen_role": false,
"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": 1250,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"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": "Kafka",
"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": 852,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
"id": 26,
"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
"source": "db"
},
"dimension_id": 26,
"input_skill": "AWS",
"llm_role": null,
"matched_chosen_role": true,
"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": 163,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Integration Practices",
"id": null,
"rationale": "Practices for automatically building, testing, and validating code changes whenever they are committed or merged. CI belongs here because it is the core workflow that keeps changes integrated and feedback fast.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "CI",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Platforms and Services",
"id": null,
"rationale": "Covers core cloud computing concepts, managed services, and platform primitives used to build and run workloads in public or private cloud environments. The skill Cloud belongs here because it is the umbrella concept for selecting, using, and operating cloud capabilities.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Cloud",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Log Search and Analytics",
"id": null,
"rationale": "Covers indexing, searching, visualizing, and alerting on machine-generated logs and related operational telemetry. ELK belongs here because it is commonly used as a log analytics stack for observability and troubleshooting.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "ELK",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Exposure Measurement",
"id": null,
"rationale": "Covers how much a person, system, or dataset is exposed to a condition, risk, stimulus, or signal, and how that exposure is quantified or tracked. The skill belongs here when it refers to measuring or managing exposure as an analytical or operational concept rather than access control or security posture.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Exposure",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Manual Dexterity and Hand Skills",
"id": null,
"rationale": "Covers skills that rely on direct hand use, fine motor control, and manual manipulation of objects or tools. The target skill belongs here because it refers to the physical use of hands rather than a software, data, or systems concept.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Hands",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure",
"id": null,
"rationale": "Core compute, network, storage, and platform foundations that applications run on. This skill belongs here when it refers to the underlying environments, provisioning, connectivity, and operational building blocks rather than a specific app or tool layer.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Infrastructure",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Knowledge Representation",
"id": null,
"rationale": "Represents structured knowledge used by AI systems to store, retrieve, and reason over facts, entities, and relationships. The skill \"Knowledge\" fits here when it refers to building or using knowledge bases, ontologies, or retrieval-augmented context.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Knowledge",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Linux System Administration",
"id": null,
"rationale": "Covers operating and maintaining Linux-based systems, including shell usage, package management, services, permissions, filesystems, and troubleshooting. Linux belongs here because it is the core host operating system skill used to administer servers and developer environments.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Linux",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Operational Monitoring and Triage",
"id": null,
"rationale": "Covers continuously observing systems, alerts, and health signals to detect degradations or failures early. The skill Monitor fits here when it means watching operational telemetry and deciding when to escalate.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Monitor",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Site Reliability Engineering",
"id": null,
"rationale": "Practices for operating production services with high availability, low latency, and fast recovery. This covers the core SRE discipline of using engineering methods to prevent incidents, manage risk, and improve service reliability.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "SRE",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "General Scripting",
"id": null,
"rationale": "Writing small automation programs and command-line scripts to perform repetitive tasks, glue systems together, or transform data. This fits the target skill because scripting is the broad practice of authoring lightweight executable code for automation.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Scripting",
"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": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Conceptual Understanding",
"id": null,
"rationale": "General comprehension of a topic, process, or system at a level sufficient to explain it, reason about it, and apply it correctly. This is the best fit for the standalone skill \"Understanding\" when no narrower technical dimension is implied.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Understanding",
"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": 47
},
"planner_output": null,
"run_id": "3ae1d371-ca75-4dda-b3b6-5db504aeed29"
}
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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