Pipeline run
bab35f46-92ce-4790-bb73-d35c8ee4d31b
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)
0.20 / 5
· Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
agentic AI, agentic, AI, Artificial Intelligence
Evidence — skills matched in JD (0)
Skill cluster (0 dimension groups, role-scoped)
Status:
completed
Created: 2026-05-08T13:06:05.514038Z
Updated: 2026-05-08T13:08:06.242653Z
API 3 duration: 686 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
DevOps Engineer
slug: devops-engineer · id: 1 · source: db
DevOps Engineer is the clearest fit across the strongest dimensions: CI/CD, deployment automation, containerization, cloud platform operations, and orchestration.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
48
Skipped
Job description
Job Description Role Overview: We are seeking a seasoned Cloud Engineer to join our dynamic Technology research team. The ideal candidate will work collaboratively with the AI, Product and other teams to optimize building Devops cloud-based pipelines, enabling cloud infrastructure and services, ensuring the full benefits of standardized Cloud Tools are achieved across various teams of the vertical. This individual will play a crucial role in developing Infrastructure as a Code, AI models deployment pipelines, security ops, automating CI/CD pipelines, and streamlining cloud operations to improve efficiency, reduce costs, and enhance system availability, performance, and scalability. Responsibilities: Be an active member of the team to build, own cloud based platform, its services, Devops pipelines for AI Model deployment, development pipelines, and provisioning public Cloud Native platform infrastructure in AWS or Azure or GCP. Develop and maintain Infrastructure as a Code (IaC) scripts to deploy cloud resources through pipelines and automate application installs. Continually evolve deployment methods and processes to effectively deliver products across all environments. Will be responsible for building docker containerization pipelines for Nvidia Nemo/NIM models and services. Will be involved in Containerization and Containers orchestration using Kubernetes Good experience in setting up end to end CI/CD using AWS/Azure Devops (components such as web app, function app, logic app and APIM) Building feedback loops to transfer data from client end to optimization hub in WNS environment. Will be involved in integrating platform with other tools like Junit, Jmeter, Jira, Jenkins etc Will be involved in sandbox cloud VM creation and replication for multiple users where they can collaborate to dev AI/Agentic AI applications. Will be involved in scaling product deployment - Authentication, Authorization, Session management, rest APIs and code building to scale applications which can be used by 10K+ users. Should have experience with AI Model versioning, code versioning through SVN and Git repository configuration and management Collaborate with the senior members of the AI Software Engineering team, Product Manager, and Product team to continuously improve the processes for the implementation, operation, and maintenance of the platform Implement automation, monitoring, logging, alerting, and deployment solutions to improve buildAI Model deployment experience and developer productivity. Ensure that all cloud solutions follow internally defined security and compliance standards and controls. Also take care of Containers and Code security while deploying the product/solution in client environment. Being the Cloud Native advocate to challenge status quo and ensure leveraging the full capabilities of the Cloud Platform Skills: Possess one or more experience(AWS/Azure/GCP). Possess experience in containers development, Kubernetes, Devops tools, CI/CD pipeline development, Jenkins etc. Possess deep and broad knowledge and experience in general IT and public cloud, including Networking, Infrastructure, Security, Storage, Operating Systems (Linux/Windows). Experience with cloud infrastructure as code tooling.5. Proficiency in server OS, including Windows and/or Linux.6. Strong skills in Python and PowerShell/Bash scripting. Qualifications Bachelors or master’s in computer science, Artificial Intelligence, or a related field 7+ years of experience in Devops and Cloud Experience tool chaining (GitHub, Jira), building CICD Pipeline, deployment automation, and containerization (Kubernetes, Docker). Excellent problem solving, troubleshooting and analytical skill
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 |
|---|---|---|---|---|---|---|
| JUnit | in_db |
Authentication Flows and Session Handling
authentication-flows-and-session-handling
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| JUnit | in_db |
Test Frameworks and Runners
test-frameworks-and-runners
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| JUnit | in_db |
Testing and Quality Assurance
testing-and-quality-assurance
|
— | — | — | 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) |
| 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) |
| Kubernetes | in_db |
Orchestration Platforms
orchestration-platforms
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| PowerShell | in_db |
Deployment Automation Scripts
deployment-automation-scripts
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| REST APIs | in_db |
API Integration and Data Fetching
api-integration-and-data-fetching
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| REST APIs | in_db |
API Integration and Serialization
api-integration-and-serialization
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| REST APIs | in_db |
Network Automation and Scripting
network-automation-and-scripting
|
— | — | — | 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) |
| automation | in_db |
Programming for Data Automation
programming-for-data-automation
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| alerting | in_db |
Observability and Incident Triage
observability-and-incident-triage
|
— | — | — | 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) |
| Bash scripting | in_db |
Deployment Automation Scripts
deployment-automation-scripts
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Bash scripting | in_db |
MySQL Automation and Scripting
mysql-automation-and-scripting
|
— | — | — | 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) |
| APIM | new |
API Management
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| APIs | new |
API Design and Integration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Authentication | new |
Authentication
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| CD | new |
Continuous Delivery Practices
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| CI | new |
Continuous Integration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| CICD | new |
Continuous Integration and Delivery
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Cloud | new |
Cloud Platforms and Services
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Containers | new |
Containerization
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Develop | new |
Cloud Application Development
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Devops | new |
Delivery Pipeline Automation
d_init_02
|
— | — | — | skill_not_in_db_v3_proposed |
| Devops | new |
Scaling and Resilience Engineering
scaling-and-resilience-engineering
|
— | — | — | skill_not_in_db_v3_proposed |
| Engineer | new |
Cloud Engineering
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Engineering | new |
General Engineering Practice
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| IaC | new |
Infrastructure as Code Practices
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Infrastructure | new |
Infrastructure Architecture
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| NIM | new |
NIM Deployment and Serving
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Nemo | new |
Nemo Platform Operations
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Platform | new |
Platform Administration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Storage | new |
Storage Systems
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Systems | new |
Systems Engineering
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| WNS | new |
WNS Platform Operations
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| Windows | new |
Windows Operating System Administration
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | AI | type=Concept subtype=artificial_intelligence nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | APIM | type=Platform subtype=api_management_platform nature=PLATFORM lifespan=EVERGREEN | |
| canonical_skill_proposed | APIs | type=Protocol subtype=application_programming_interfaces nature=PROTOCOL lifespan=EVERGREEN | |
| canonical_skill_proposed | Authentication | type=Concept subtype=authentication nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | CD | type=Methodology subtype=continuous_delivery nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | CI | type=Methodology subtype=continuous_integration_methodology nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | CICD | type=Methodology subtype=continuous_integration_continuous_delivery nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Cloud | type=Domain subtype=cloud_computing nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Containers | type=Architecture subtype=containerization_architecture nature=PATTERN lifespan=EVERGREEN | |
| canonical_skill_proposed | Develop | type=Methodology subtype=software_development_process nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Devops | type=Methodology subtype=devops_practices nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Engineer | type=Domain subtype=engineering nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Engineering | type=Domain subtype=engineering nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | IaC | type=Methodology subtype=infrastructure_as_code nature=METHODOLOGY lifespan=EVERGREEN | |
| canonical_skill_proposed | Infrastructure | type=Domain subtype=infrastructure nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | NIM | type=Language subtype=systems_programming_language nature=LANGUAGE lifespan=EVERGREEN | |
| canonical_skill_proposed | Nemo | type=Tool subtype=desktop_application nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | Networking | type=Domain subtype=networking nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Pipeline | type=Architecture subtype=data_pipeline_architecture nature=PATTERN lifespan=EVERGREEN | |
| canonical_skill_proposed | Platform | type=Platform subtype=hosted_platform nature=PLATFORM lifespan=EVERGREEN | |
| canonical_skill_proposed | Security | type=Domain subtype=cybersecurity nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Storage | type=Datastore subtype=storage_system nature=TOOL lifespan=EVERGREEN | |
| canonical_skill_proposed | Systems | type=Domain subtype=systems_engineering nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | WNS | type=Domain subtype=business_process_outsourcing nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Windows | type=Platform subtype=operating_system_platform nature=PLATFORM lifespan=EVERGREEN | |
| dimension_proposed | API Management | |
| dimension_skill_link_proposed | APIM ↔ API Management | |
| dimension_proposed | API Design and Integration | |
| dimension_skill_link_proposed | APIs ↔ API Design and Integration | |
| dimension_proposed | Authentication | |
| dimension_skill_link_proposed | Authentication ↔ Authentication | |
| dimension_proposed | Continuous Delivery Practices | |
| dimension_skill_link_proposed | CD ↔ Continuous Delivery Practices | |
| dimension_proposed | Continuous Integration | |
| dimension_skill_link_proposed | CI ↔ Continuous Integration | |
| dimension_proposed | Continuous Integration and Delivery | |
| dimension_skill_link_proposed | CICD ↔ Continuous Integration and Delivery | |
| dimension_proposed | Cloud Platforms and Services | |
| dimension_skill_link_proposed | Cloud ↔ Cloud Platforms and Services | |
| dimension_proposed | Containerization | |
| dimension_skill_link_proposed | Containers ↔ Containerization | |
| dimension_proposed | Cloud Application Development | |
| dimension_skill_link_proposed | Develop ↔ Cloud Application Development | |
| dimension_proposed | Delivery Pipeline Automation | |
| dimension_skill_link_proposed | Devops ↔ Delivery Pipeline Automation | |
| dimension_skill_link_proposed | Devops ↔ Scaling and Resilience Engineering | |
| dimension_proposed | Cloud Engineering | |
| dimension_skill_link_proposed | Engineer ↔ Cloud Engineering | |
| dimension_proposed | General Engineering Practice | |
| dimension_skill_link_proposed | Engineering ↔ General Engineering Practice | |
| dimension_proposed | Infrastructure as Code Practices | |
| dimension_skill_link_proposed | IaC ↔ Infrastructure as Code Practices | |
| dimension_proposed | Infrastructure Architecture | |
| dimension_skill_link_proposed | Infrastructure ↔ Infrastructure Architecture | |
| dimension_proposed | NIM Deployment and Serving | |
| dimension_skill_link_proposed | NIM ↔ NIM Deployment and Serving | |
| dimension_proposed | Nemo Platform Operations | |
| dimension_skill_link_proposed | Nemo ↔ Nemo Platform Operations | |
| dimension_proposed | Platform Administration | |
| dimension_skill_link_proposed | Platform ↔ Platform Administration | |
| dimension_proposed | Storage Systems | |
| dimension_skill_link_proposed | Storage ↔ Storage Systems | |
| dimension_proposed | Systems Engineering | |
| dimension_skill_link_proposed | Systems ↔ Systems Engineering | |
| dimension_proposed | WNS Platform Operations | |
| dimension_skill_link_proposed | WNS ↔ WNS Platform Operations | |
| dimension_proposed | Windows Operating System Administration | |
| dimension_skill_link_proposed | Windows ↔ Windows Operating System Administration |
API 1 — extract-from-jd click to toggle
{
"filtered_unknown_words": [
"10K+",
"AI",
"APIM",
"APIs",
"Agentic",
"Artificial",
"Authentication",
"Bachelors",
"CD",
"CI",
"CICD",
"Cloud",
"Code",
"Containers",
"Description",
"Develop",
"Devops",
"Engineer",
"Engineering",
"Experience",
"IaC",
"Infrastructure",
"Intelligence",
"Job",
"Linux.6",
"Manager",
"Model",
"NIM",
"Native",
"Nemo",
"Networking",
"Nvidia",
"OS",
"Operating",
"Overview",
"Pipeline",
"Platform",
"Product",
"Proficiency",
"Qualifications",
"Responsibilities",
"Role",
"SVN",
"Security",
"Skills",
"Software",
"Storage",
"Systems",
"Technology",
"Tools",
"WNS",
"Windows",
"advocate",
"app",
"application",
"applications",
"availability",
"benefits",
"buildAI",
"building",
"candidate",
"capabilities",
"care",
"chaining",
"client",
"cloud",
"code",
"compliance",
"components",
"computer",
"configuration",
"containers",
"controls",
"costs",
"creation",
"data",
"deployment",
"dev",
"developer",
"development",
"efficiency",
"end",
"environment",
"environments",
"experience",
"experience(AWS",
"feedback",
"field",
"function",
"hub",
"implementation",
"individual",
"infrastructure",
"installs",
"knowledge",
"logic",
"loops",
"maintenance",
"master",
"member",
"members",
"methods",
"models",
"monitoring",
"operation",
"operations",
"ops",
"optimization",
"orchestration",
"performance",
"pipeline",
"pipelines",
"platform",
"problem",
"processes",
"product",
"productivity",
"products",
"quo",
"repository",
"research",
"resources",
"role",
"sandbox",
"science",
"scripts",
"security",
"server",
"services",
"skill",
"skills",
"solution",
"solutions",
"solving",
"standards",
"status",
"system",
"team",
"teams",
"tool",
"tooling.5",
"tools",
"users",
"versioning",
"web",
"years"
],
"final_non_skills": [
"10K+",
"Agentic",
"Artificial",
"Bachelors",
"Code",
"Description",
"Experience",
"Job",
"Linux.6",
"Manager",
"Model",
"Native",
"Nvidia",
"OS",
"Operating",
"Overview",
"Product",
"Proficiency",
"Qualifications",
"Responsibilities",
"Role",
"SVN",
"Skills",
"Software",
"Technology",
"Tools",
"advocate",
"app",
"availability",
"benefits",
"buildAI",
"building",
"candidate",
"capabilities",
"care",
"chaining",
"client",
"compliance",
"components",
"computer",
"configuration",
"controls",
"costs",
"creation",
"data",
"dev",
"developer",
"efficiency",
"end",
"environment",
"environments",
"experience(AWS",
"feedback",
"field",
"function",
"hub",
"implementation",
"individual",
"installs",
"knowledge",
"logic",
"loops",
"maintenance",
"master",
"member",
"members",
"methods",
"models",
"operation",
"operations",
"optimization",
"performance",
"problem",
"processes",
"products",
"quo",
"repository",
"research",
"sandbox",
"science",
"server",
"skill",
"solution",
"solving",
"standards",
"status",
"system",
"team",
"teams",
"tooling.5",
"users",
"years"
],
"final_skills": [
"JUnit",
"Jenkins",
"Python",
"Kubernetes",
"PowerShell",
"REST APIs",
"Docker",
"automation",
"alerting",
"Azure",
"Bash scripting",
"AWS",
"AI",
"APIM",
"APIs",
"Authentication",
"CD",
"CI",
"CICD",
"Cloud",
"Containers",
"Develop",
"Devops",
"Engineer",
"Engineering",
"IaC",
"Infrastructure",
"NIM",
"Nemo",
"Networking",
"Pipeline",
"Platform",
"Security",
"Storage",
"Systems",
"WNS",
"Windows",
"application",
"applications",
"deployment",
"development",
"monitoring",
"ops",
"orchestration",
"pipelines",
"productivity",
"resources",
"scripts",
"services",
"solutions",
"tool",
"versioning",
"web"
],
"initial_skills": [
"JUnit",
"Jenkins",
"Python",
"Kubernetes",
"PowerShell",
"REST APIs",
"Docker",
"automation",
"alerting",
"Azure",
"Bash scripting",
"AWS"
],
"jd_role_hint": {
"display_name": "Cloud Engineer",
"rationale": "The excerpt centers on cloud platforms, DevOps pipelines, infrastructure as code, deployment automation, and cloud operations.",
"role_archetype": "Cloud infrastructure engineer focused on DevOps, CI/CD, IaC, and platform operations.",
"slug": "cloud-engineer"
},
"llm_non_skills": [
"10K+",
"Agentic",
"Artificial",
"Bachelors",
"Code",
"Description",
"Experience",
"Job",
"Linux.6",
"Manager",
"Model",
"Native",
"Nvidia",
"OS",
"Operating",
"Overview",
"Product",
"Proficiency",
"Qualifications",
"Responsibilities",
"Role",
"SVN",
"Skills",
"Software",
"Technology",
"Tools",
"advocate",
"app",
"availability",
"benefits",
"buildAI",
"building",
"candidate",
"capabilities",
"care",
"chaining",
"client",
"compliance",
"components",
"computer",
"configuration",
"controls",
"costs",
"creation",
"data",
"dev",
"developer",
"efficiency",
"end",
"environment",
"environments",
"experience(AWS",
"feedback",
"field",
"function",
"hub",
"implementation",
"individual",
"installs",
"knowledge",
"logic",
"loops",
"maintenance",
"master",
"member",
"members",
"methods",
"models",
"operation",
"operations",
"optimization",
"performance",
"problem",
"processes",
"products",
"quo",
"repository",
"research",
"sandbox",
"science",
"server",
"skill",
"solution",
"solving",
"standards",
"status",
"system",
"team",
"teams",
"tooling.5",
"users",
"years"
],
"llm_skills": [
"AI",
"APIM",
"APIs",
"Authentication",
"CD",
"CI",
"CICD",
"Cloud",
"Containers",
"Develop",
"Devops",
"Engineer",
"Engineering",
"IaC",
"Infrastructure",
"NIM",
"Nemo",
"Networking",
"Pipeline",
"Platform",
"Security",
"Storage",
"Systems",
"WNS",
"Windows",
"application",
"applications",
"cloud",
"containers",
"deployment",
"development",
"infrastructure",
"monitoring",
"ops",
"orchestration",
"pipeline",
"pipelines",
"platform",
"productivity",
"resources",
"scripts",
"security",
"services",
"solutions",
"tool",
"versioning",
"web"
],
"run_id": null,
"unknown_words": [
"10K+",
"AI",
"APIM",
"APIs",
"Agentic",
"Artificial",
"Authentication",
"Bachelors",
"CD",
"CI",
"CICD",
"Cloud",
"Code",
"Containers",
"Description",
"Develop",
"Devops",
"Engineer",
"Engineering",
"Experience",
"IaC",
"Infrastructure",
"Intelligence",
"Job",
"Linux.6",
"Manager",
"Model",
"NIM",
"Native",
"Nemo",
"Networking",
"Nvidia",
"OS",
"Operating",
"Overview",
"Pipeline",
"Platform",
"Product",
"Proficiency",
"Qualifications",
"Responsibilities",
"Role",
"SVN",
"Security",
"Skills",
"Software",
"Storage",
"Systems",
"Technology",
"Tools",
"WNS",
"Windows",
"advocate",
"app",
"application",
"applications",
"availability",
"benefits",
"buildAI",
"building",
"candidate",
"capabilities",
"care",
"chaining",
"client",
"cloud",
"code",
"compliance",
"components",
"computer",
"configuration",
"containers",
"controls",
"costs",
"creation",
"data",
"deployment",
"dev",
"developer",
"development",
"efficiency",
"end",
"environment",
"environments",
"experience",
"experience(AWS",
"feedback",
"field",
"function",
"hub",
"implementation",
"individual",
"infrastructure",
"installs",
"knowledge",
"logic",
"loops",
"maintenance",
"master",
"member",
"members",
"methods",
"models",
"monitoring",
"operation",
"operations",
"ops",
"optimization",
"orchestration",
"performance",
"pipeline",
"pipelines",
"platform",
"problem",
"processes",
"product",
"productivity",
"products",
"quo",
"repository",
"research",
"resources",
"role",
"sandbox",
"science",
"scripts",
"security",
"server",
"services",
"skill",
"skills",
"solution",
"solutions",
"solving",
"standards",
"status",
"system",
"team",
"teams",
"tool",
"tooling.5",
"tools",
"users",
"versioning",
"web",
"years"
]
}
API 2 — extract-details
{
"alias_matches": [],
"candidate_roles": [
{
"display_name": "Android Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
},
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
},
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
},
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "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": "iOS Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ios-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
],
"chosen_role": {
"display_name": "DevOps Engineer",
"id": 1,
"rationale": "DevOps Engineer is the clearest fit across the strongest dimensions: CI/CD, deployment automation, containerization, cloud platform operations, and orchestration.",
"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": "Authentication Flows and Session Handling",
"id": 10,
"rationale": "Client-side implementation of sign-in, sign-out, session persistence, and protected navigation. This is separated from general API integration because auth flows have distinct UX and state concerns.",
"slug": "authentication-flows-and-session-handling",
"source": "db"
},
"input_skill": "JUnit",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Android Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Test Frameworks and Runners",
"id": 194,
"rationale": "Frameworks and execution tools used to author, organize, and run automated tests. This cluster is coherent because it defines how test cases are structured, parameterized, and reported.",
"slug": "test-frameworks-and-runners",
"source": "db"
},
"input_skill": "JUnit",
"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": "Testing and Quality Assurance",
"id": 150,
"rationale": "Automated testing approaches used to verify backend behavior across units, integrations, contracts, and end-to-end flows. This cluster is coherent because backend engineers need confidence in request handling, data changes, and service interactions.",
"slug": "testing-and-quality-assurance",
"source": "db"
},
"input_skill": "JUnit",
"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": "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": "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": "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": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"input_skill": "PowerShell",
"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": "API Integration and Data Fetching",
"id": 9,
"rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
"slug": "api-integration-and-data-fetching",
"source": "db"
},
"input_skill": "REST APIs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "API Integration and Serialization",
"id": 128,
"rationale": "Client-side integration with backend services, including request handling, response parsing, and contract alignment. This cluster is coherent because iOS features frequently depend on stable data exchange with server APIs.",
"slug": "api-integration-and-serialization",
"source": "db"
},
"input_skill": "REST APIs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "iOS Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ios-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": "REST APIs",
"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": "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": "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": "automation",
"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": "Observability and Incident Triage",
"id": 61,
"rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
"slug": "observability-and-incident-triage",
"source": "db"
},
"input_skill": "alerting",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"input_skill": "Bash scripting",
"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": "MySQL Automation and Scripting",
"id": 176,
"rationale": "Covers scripts and operational automation used to run repeatable DBA tasks. This cluster is coherent because production database administration depends on safe, repeatable execution of checks, maintenance, and change steps.",
"slug": "mysql-automation-and-scripting",
"source": "db"
},
"input_skill": "Bash scripting",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "AWS",
"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": null,
"display_name": "API Management",
"id": null,
"rationale": "Covers managing API gateways and the lifecycle controls around publishing, securing, throttling, and observing APIs. APIM belongs here because it is commonly shorthand for API Management platforms and practices.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "APIM",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "API Design and Integration",
"id": null,
"rationale": "Covers designing, consuming, and integrating application programming interfaces across services and clients. APIs belong here because they define the contract, transport, and interaction patterns used by systems to communicate.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "APIs",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Authentication",
"id": null,
"rationale": "Covers mechanisms for verifying identity and establishing trusted access to systems and services. This skill belongs here when the focus is on proving who a user or service is, rather than what they are allowed to do after login.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Authentication",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Delivery Practices",
"id": null,
"rationale": "Covers the software delivery discipline of automating build, test, and release steps so changes can move safely to production. CD belongs here when it refers to the delivery methodology rather than a specific environment-management task.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CD",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Integration",
"id": null,
"rationale": "Practices for automatically building, validating, and merging code changes through shared pipelines. CI belongs here because it is the core delivery discipline for integrating changes early and catching regressions before release.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CI",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Integration and Delivery",
"id": null,
"rationale": "Automates code integration, testing, packaging, and release promotion from commit to deployment. CICD belongs here because it is the core delivery pipeline practice for building, validating, and shipping software changes reliably.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CICD",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Platforms and Services",
"id": null,
"rationale": "Covers core cloud computing concepts and the use of public cloud services to build, run, and manage systems. This fits the target skill because \"Cloud\" usually refers to working with cloud provider capabilities, deployment models, and managed services rather than a narrower sub-discipline.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Cloud",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Containerization",
"id": null,
"rationale": "Covers packaging and running software in isolated containers, including the core concepts and tooling used to create and manage container images and runtimes. Containers belongs here because it is the foundational abstraction for container-based deployment and execution.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Containers",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Application Development",
"id": null,
"rationale": "Building application logic, services, and integrations that run in cloud environments. The skill \u0027Develop\u0027 fits here when it refers to writing the code that implements cloud-native features and service behavior.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Develop",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Delivery Pipeline Automation",
"id": null,
"rationale": "Covers automating build, test, release, and deployment workflows that move software from source control to running environments. DevOps belongs here when it refers to the operational practice of continuous integration and continuous delivery.",
"slug": "d_init_02",
"source": "llm"
},
"input_skill": "Devops",
"llm_role": null,
"roles_from_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": "Devops",
"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": null,
"display_name": "Cloud Engineering",
"id": null,
"rationale": "Builds and operates cloud-based infrastructure, services, and deployment foundations. This is the best fit for the broad role label \"Engineer\" with a Cloud Engineer hint, since it typically spans provisioning, networking, reliability, and platform operations.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Engineer",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "General Engineering Practice",
"id": null,
"rationale": "Broad engineering work that spans designing, building, and maintaining technical systems when no narrower specialty is implied. This skill is too generic to map cleanly to a more specific catalog dimension, so it serves as the umbrella for general implementation and problem-solving work.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Engineering",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure as Code Practices",
"id": null,
"rationale": "Covers authoring and maintaining declarative infrastructure definitions used to provision and manage cloud resources. IaC belongs here because it is the core practice for codifying infrastructure instead of configuring it manually.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "IaC",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure Architecture",
"id": null,
"rationale": "Covers the foundational cloud and systems layer that supports applications, services, and operations. Use this when Infrastructure is meant broadly as the underlying compute, storage, networking, and platform foundation rather than a narrower operational specialty.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Infrastructure",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "NIM Deployment and Serving",
"id": null,
"rationale": "Covers deploying, configuring, and operating NVIDIA NIM services for model inference in cloud environments. This fits the target skill because NIM is primarily about packaging and serving AI models through a managed runtime and API surface.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "NIM",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Nemo Platform Operations",
"id": null,
"rationale": "Operational use of the Nemo platform for running, configuring, and troubleshooting cloud workloads. This fits the target skill because Nemo is treated here as a platform-specific operational competency rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Nemo",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Platform Administration",
"id": null,
"rationale": "Covers administering the underlying platform layer used to configure, operate, and support environments and instances. The skill \"Platform\" fits here when it refers to the operational platform itself rather than a specific app feature or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Platform",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Storage Systems",
"id": null,
"rationale": "Covers storage as a computing capability, including how data is persisted, organized, and accessed through block, file, and object interfaces. This skill belongs here when it refers to storage concepts or services rather than physical hardware internals.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Storage",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Systems Engineering",
"id": null,
"rationale": "Covers understanding and designing how software, infrastructure, and operational components work together as a whole. The skill \"Systems\" belongs here when it refers to broad system behavior, dependencies, reliability, and tradeoffs across cloud environments.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Systems",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "WNS Platform Operations",
"id": null,
"rationale": "Covers operational work specific to WNS-managed enterprise platforms and services, including setup, support, monitoring, and issue resolution. This skill belongs here when WNS refers to the platform or service environment being administered rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "WNS",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "Windows Operating System Administration",
"id": null,
"rationale": "Covers administering Microsoft Windows as an operating system, including desktop and server configuration, patching, services, users, and troubleshooting. This skill belongs here because it refers to the Windows platform itself rather than a narrower product or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Windows",
"llm_role": null,
"roles_from_db": []
}
],
"input_final_skills": [
"JUnit",
"Jenkins",
"Python",
"Kubernetes",
"PowerShell",
"REST APIs",
"Docker",
"automation",
"alerting",
"Azure",
"Bash scripting",
"AWS",
"AI",
"APIM",
"APIs",
"Authentication",
"CD",
"CI",
"CICD",
"Cloud",
"Containers",
"Develop",
"Devops",
"Engineer",
"Engineering",
"IaC",
"Infrastructure",
"NIM",
"Nemo",
"Networking",
"Pipeline",
"Platform",
"Security",
"Storage",
"Systems",
"WNS",
"Windows",
"application",
"applications",
"deployment",
"development",
"monitoring",
"ops",
"orchestration",
"pipelines",
"productivity",
"resources",
"scripts",
"services",
"solutions",
"tool",
"versioning",
"web"
],
"input_llm_skills": [
"AI",
"APIM",
"APIs",
"Authentication",
"CD",
"CI",
"CICD",
"Cloud",
"Containers",
"Develop",
"Devops",
"Engineer",
"Engineering",
"IaC",
"Infrastructure",
"NIM",
"Nemo",
"Networking",
"Pipeline",
"Platform",
"Security",
"Storage",
"Systems",
"WNS",
"Windows",
"application",
"applications",
"deployment",
"development",
"monitoring",
"ops",
"orchestration",
"pipelines",
"productivity",
"resources",
"scripts",
"services",
"solutions",
"tool",
"versioning",
"web"
],
"new_aliases_persisted": 0,
"run_id": "bab35f46-92ce-4790-bb73-d35c8ee4d31b",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "JUnit",
"alias_type": "CANONICAL",
"id": 1325,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit 3",
"alias_type": "VERSION",
"id": 1326,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit 4",
"alias_type": "VERSION",
"id": 1327,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit 5",
"alias_type": "VERSION",
"id": 1393,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit 5.x",
"alias_type": "VERSION",
"id": 1328,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit Jupiter",
"alias_type": "VERSION",
"id": 1329,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "JUnit Vintage",
"alias_type": "VERSION",
"id": 1395,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "JUnit",
"id": 882,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "junit",
"sub_category_id": 63,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Authentication Flows and Session Handling",
"id": 10,
"rationale": "Client-side implementation of sign-in, sign-out, session persistence, and protected navigation. This is separated from general API integration because auth flows have distinct UX and state concerns.",
"slug": "authentication-flows-and-session-handling",
"source": "db"
},
"input_skill": "JUnit",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Android Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Test Frameworks and Runners",
"id": 194,
"rationale": "Frameworks and execution tools used to author, organize, and run automated tests. This cluster is coherent because it defines how test cases are structured, parameterized, and reported.",
"slug": "test-frameworks-and-runners",
"source": "db"
},
"input_skill": "JUnit",
"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": "Testing and Quality Assurance",
"id": 150,
"rationale": "Automated testing approaches used to verify backend behavior across units, integrations, contracts, and end-to-end flows. This cluster is coherent because backend engineers need confidence in request handling, data changes, and service interactions.",
"slug": "testing-and-quality-assurance",
"source": "db"
},
"input_skill": "JUnit",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
]
}
],
"input_skill": "JUnit",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Jenkins",
"alias_type": "CANONICAL",
"id": 1799,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Jenkins",
"id": 1249,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "jenkins",
"sub_category_id": 166,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
}
]
}
],
"input_skill": "Jenkins",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Python",
"alias_type": "CANONICAL",
"id": 608,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 2",
"alias_type": "VERSION",
"id": 611,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 2.x",
"alias_type": "VERSION",
"id": 613,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3",
"alias_type": "VERSION",
"id": 612,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.10",
"alias_type": "VERSION",
"id": 2330,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.11",
"alias_type": "VERSION",
"id": 2331,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.12",
"alias_type": "VERSION",
"id": 2332,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.x",
"alias_type": "VERSION",
"id": 614,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "py2",
"alias_type": "VERSION",
"id": 609,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "py3",
"alias_type": "VERSION",
"id": 610,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 2",
"alias_type": "VERSION",
"id": 2152,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 2.x",
"alias_type": "VERSION",
"id": 2154,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3",
"alias_type": "VERSION",
"id": 990,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3.10",
"alias_type": "VERSION",
"id": 992,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3.11",
"alias_type": "VERSION",
"id": 993,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3.12",
"alias_type": "VERSION",
"id": 994,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3.x",
"alias_type": "VERSION",
"id": 991,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python2",
"alias_type": "VERSION",
"id": 2150,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python3",
"alias_type": "VERSION",
"id": 989,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Python",
"id": 393,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 54,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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"
}
]
}
],
"input_skill": "Python",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Kubernetes",
"alias_type": "CANONICAL",
"id": 304,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0",
"alias_type": "VERSION",
"id": 307,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.0+",
"alias_type": "VERSION",
"id": 2366,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.1",
"alias_type": "VERSION",
"id": 308,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.10",
"alias_type": "VERSION",
"id": 318,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.11",
"alias_type": "VERSION",
"id": 319,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.12",
"alias_type": "VERSION",
"id": 320,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.13",
"alias_type": "VERSION",
"id": 321,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.14",
"alias_type": "VERSION",
"id": 322,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.15",
"alias_type": "VERSION",
"id": 323,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.16",
"alias_type": "VERSION",
"id": 324,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.17",
"alias_type": "VERSION",
"id": 325,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.18",
"alias_type": "VERSION",
"id": 326,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.19",
"alias_type": "VERSION",
"id": 327,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.2",
"alias_type": "VERSION",
"id": 309,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.20",
"alias_type": "VERSION",
"id": 328,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.21",
"alias_type": "VERSION",
"id": 329,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.22",
"alias_type": "VERSION",
"id": 330,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.23",
"alias_type": "VERSION",
"id": 331,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.24",
"alias_type": "VERSION",
"id": 332,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.25",
"alias_type": "VERSION",
"id": 333,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.26",
"alias_type": "VERSION",
"id": 334,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.27",
"alias_type": "VERSION",
"id": 335,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.28",
"alias_type": "VERSION",
"id": 336,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.29",
"alias_type": "VERSION",
"id": 337,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.3",
"alias_type": "VERSION",
"id": 310,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.30",
"alias_type": "VERSION",
"id": 338,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.4",
"alias_type": "VERSION",
"id": 311,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.5",
"alias_type": "VERSION",
"id": 312,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.6",
"alias_type": "VERSION",
"id": 313,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.7",
"alias_type": "VERSION",
"id": 314,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.8",
"alias_type": "VERSION",
"id": 315,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.9",
"alias_type": "VERSION",
"id": 316,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.x",
"alias_type": "VERSION",
"id": 317,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes v1",
"alias_type": "VERSION",
"id": 306,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "k8s",
"alias_type": "VERSION",
"id": 305,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Kubernetes",
"id": 158,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "kubernetes",
"sub_category_id": 1524,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Orchestration Platforms",
"id": 25,
"rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
"slug": "orchestration-platforms",
"source": "db"
},
"input_skill": "Kubernetes",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Kubernetes",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "PowerShell scripting",
"alias_type": "CANONICAL",
"id": 374,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 5.1",
"alias_type": "VERSION",
"id": 377,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell 7.x",
"alias_type": "VERSION",
"id": 378,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "PowerShell Core",
"alias_type": "VERSION",
"id": 379,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Windows PowerShell",
"alias_type": "VERSION",
"id": 380,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "powershell",
"alias_type": "VERSION",
"id": 376,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "pwsh",
"alias_type": "VERSION",
"id": 375,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "PowerShell scripting",
"id": 189,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "powershell-scripting",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"input_skill": "PowerShell",
"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"
}
]
}
],
"input_skill": "PowerShell",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "REST APIs",
"alias_type": "CANONICAL",
"id": 174,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "REST APIs",
"id": 49,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PROTOCOL",
"slug": "rest-apis",
"sub_category_id": 67,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "API Integration and Data Fetching",
"id": 9,
"rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
"slug": "api-integration-and-data-fetching",
"source": "db"
},
"input_skill": "REST APIs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "API Integration and Serialization",
"id": 128,
"rationale": "Client-side integration with backend services, including request handling, response parsing, and contract alignment. This cluster is coherent because iOS features frequently depend on stable data exchange with server APIs.",
"slug": "api-integration-and-serialization",
"source": "db"
},
"input_skill": "REST APIs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "iOS Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ios-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": "REST APIs",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
}
]
}
],
"input_skill": "REST APIs",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Docker",
"alias_type": "CANONICAL",
"id": 299,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Docker",
"id": 153,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "docker",
"sub_category_id": 170,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Delivery",
"id": 24,
"rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
"slug": "containerization-and-image-delivery",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Serving Deployment and Runtime Packaging",
"id": 52,
"rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
"slug": "model-serving-deployment-and-runtime-packaging",
"source": "db"
},
"input_skill": "Docker",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
}
],
"input_skill": "Docker",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "automation",
"alias_type": "CANONICAL",
"id": 851,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "automation",
"id": 553,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "automation",
"sub_category_id": 397,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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": "automation",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
}
],
"input_skill": "automation",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "alerting",
"alias_type": "CANONICAL",
"id": 975,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "alerting",
"id": 665,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "alerting",
"sub_category_id": 356,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Observability and Incident Triage",
"id": 61,
"rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
"slug": "observability-and-incident-triage",
"source": "db"
},
"input_skill": "alerting",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
}
],
"input_skill": "alerting",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Azure",
"alias_type": "CANONICAL",
"id": 349,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Azure",
"id": 164,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "azure",
"sub_category_id": 161,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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"
}
]
}
],
"input_skill": "Azure",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "Bash scripting",
"alias_type": "CANONICAL",
"id": 373,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Bash scripting",
"id": 188,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "bash-scripting",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"input_skill": "Bash scripting",
"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": "MySQL Automation and Scripting",
"id": 176,
"rationale": "Covers scripts and operational automation used to run repeatable DBA tasks. This cluster is coherent because production database administration depends on safe, repeatable execution of checks, maintenance, and change steps.",
"slug": "mysql-automation-and-scripting",
"source": "db"
},
"input_skill": "Bash scripting",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
]
}
],
"input_skill": "Bash scripting",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [
{
"alias_text": "AWS",
"alias_type": "CANONICAL",
"id": 348,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "AWS",
"id": 163,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "aws",
"sub_category_id": 161,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"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": "AWS",
"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"
}
]
}
],
"input_skill": "AWS",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": false
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "AI",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "artificial_intelligence",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"machine_learning",
"artificial_intelligence"
],
"reasoning": "\"AI\" is a common abbreviation for artificial intelligence, but in JDs it can also be used loosely to mean machine learning or related AI/ML work, so an extractor could conflate nearby catalog skills."
},
"context_keywords": {
"context_keywords": [
"machine learning",
"deep learning",
"neural networks",
"natural language processing",
"computer vision",
"reinforcement learning",
"model training",
"inference",
"feature engineering",
"transformers",
"LLM",
"prompt engineering",
"MLOps",
"data labeling",
"fine-tuning"
]
},
"maturity": {
"confidence": 0.95,
"maturity": "well_known",
"reasoning": "AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (Microsoft, Google, AWS) market AI services as core platform offerings."
},
"skill_id": "ai",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [],
"merge_log": [
{
"a_dim_id": "d_init_01",
"a_name": "AI Service Architecture",
"a_role": "__skill_focal__",
"b_dim_id": "ai-service-architecture-patterns",
"b_name": "AI Service Architecture Patterns",
"b_role": "AI Engineer",
"into": "d_merge_01",
"into_name": "AI Service Architecture and Orchestration",
"merged_from": [
"d_init_01",
"ai-service-architecture-patterns"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same skill cluster: placing and structuring AI capabilities inside an application/service architecture. Dim A covers AI feature placement, prompt/response flow, orchestration around inference, and gateway vs worker vs handler placement, with exemplars like AI service design and prompt orchestration. Dim B says the same thing via structuring AI capabilities and deciding whether AI logic lives in handlers, workers, gateways, or orchestration services. No separate conceptual anchor appears.",
"similarity": 0.814122578228551
}
],
"placed": {
"name": "AI",
"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": "ai"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "ai",
"suppress_on_match": []
},
"skill_id": "ai",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.98,
"name": "AI",
"reasoning": "AI is fundamentally a named knowledge unit about intelligent systems, so by the Concept vs Methodology rule it is a Concept rather than a tool, platform, or architecture.",
"skill_id": "ai",
"subtype": "artificial_intelligence",
"type": "Concept"
},
"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": "API Management",
"id": null,
"rationale": "Covers managing API gateways and the lifecycle controls around publishing, securing, throttling, and observing APIs. APIM belongs here because it is commonly shorthand for API Management platforms and practices.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "APIM",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "APIM",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "api_management_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"api_management"
],
"reasoning": "APIM is a common abbreviation for API Management, so a JD mention could refer to the broader API management skill rather than this specific platform entry."
},
"context_keywords": {
"context_keywords": [
"Azure API Management",
"gateway",
"policies",
"developer portal",
"subscriptions",
"products",
"rate limiting",
"throttling",
"OpenAPI",
"Swagger",
"JWT",
"OAuth 2.0",
"backend services",
"revision",
"versioning"
]
},
"maturity": {
"confidence": 0.84,
"maturity": "well_known",
"reasoning": "API management platforms are broadly listed in enterprise JDs, especially Azure API Management, Apigee, and Kong; vendor docs and hiring demand show it as a standard integration skill."
},
"skill_id": "apim",
"vendor_license": {
"confidence": 0.93,
"license": "proprietary",
"vendor": "Microsoft",
"year_introduced": 2013
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers managing API gateways and the lifecycle controls around publishing, securing, throttling, and observing APIs. APIM belongs here because it is commonly shorthand for API Management platforms and practices.",
"exemplar_skills": [
"APIM",
"Azure API Management",
"API gateway configuration",
"API versioning",
"rate limiting",
"API policies"
],
"in_scope": "APIM, Azure API Management, API gateway configuration, API publishing, API versioning, rate limiting, quotas, request/response transformation, developer portal setup, API policies, subscription keys",
"name": "API Management",
"out_of_scope": "Service-to-service architecture patterns, backend endpoint implementation, client-side API consumption, test automation for APIs, cloud network routing and load balancing",
"overlap_flags": [
{
"reason": "API management often sits alongside service integration, but this dimension is about gateway policy and publication rather than service decomposition.",
"with_dim_id": "service-architecture-and-integration",
"with_dim_name": null,
"with_role": "Backend Engineer"
},
{
"reason": "APIM enforces access controls and security policies, but broader cloud security guardrails own baseline security architecture.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "APIM",
"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": "apim"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"rest-apis",
"aws-iam",
"rbac",
"saml",
"mfa",
"acls",
"policy-as-code",
"portal-pages"
],
"requires": [],
"skill_id": "apim",
"suppress_on_match": []
},
"skill_id": "apim",
"split_log": [],
"typed": {
"alternatives_considered": [
"Service: ruled out \u2014 APIM is typically the broader managed API management offering rather than one specific capability inside another platform.",
"Tool: ruled out \u2014 the Platform vs Tool rule favors a hosted managed environment over software you run locally."
],
"confidence": 0.88,
"name": "APIM",
"reasoning": "By the Platform vs Tool rule, APIM is best treated as a hosted, multi-tenant API management environment with managed capabilities rather than a single user-run tool.",
"skill_id": "apim",
"subtype": "api_management_platform",
"type": "Platform"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "API Design and Integration",
"id": null,
"rationale": "Covers designing, consuming, and integrating application programming interfaces across services and clients. APIs belong here because they define the contract, transport, and interaction patterns used by systems to communicate.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "APIs",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "APIs",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Protocol",
"skill_nature": "PROTOCOL",
"sub_category": "application_programming_interfaces",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "APIs is a standard, widely used term in JDs and usually refers unambiguously to application programming interfaces; it is not a short acronym with a common catalog collision."
},
"context_keywords": {
"context_keywords": [
"REST",
"GraphQL",
"OpenAPI",
"Swagger",
"JSON",
"XML",
"OAuth 2.0",
"JWT",
"webhooks",
"endpoint",
"rate limiting",
"API gateway",
"microservices",
"SDK",
"versioning"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and is foundational in cloud vendor docs and SDK ecosystems."
},
"skill_id": "apis",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers designing, consuming, and integrating application programming interfaces across services and clients. APIs belong here because they define the contract, transport, and interaction patterns used by systems to communicate.",
"exemplar_skills": [
"APIs",
"REST API design",
"gRPC service interfaces",
"Webhook integration",
"API versioning",
"Service contracts"
],
"in_scope": "APIs, REST endpoints, request/response schemas, API versioning, pagination, idempotency, webhooks, gRPC, service-to-service integration",
"name": "API Design and Integration",
"out_of_scope": "UI component interactions, database schema design, infrastructure provisioning, API testing automation, authentication-specific login/session flows",
"overlap_flags": [
{
"reason": "API work often overlaps with automated validation, but this dimension is about designing and integrating APIs rather than testing them.",
"with_dim_id": "api-and-service-test-automation",
"with_dim_name": null,
"with_role": "Automation Tester"
},
{
"reason": "APIs frequently carry auth concerns, but login/session implementation is a separate client-side cluster.",
"with_dim_id": "authentication-flows-and-session-handling",
"with_dim_name": null,
"with_role": "Android Engineer, Frontend Engineer, Full Stack Developer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "APIs",
"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": "apis"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"rest-apis",
"grpc",
"http",
"json",
"oauth-2-0"
],
"requires": [],
"skill_id": "apis",
"suppress_on_match": []
},
"skill_id": "apis",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 APIs are not primarily a knowledge unit but an interface specification.",
"Standard: ruled out \u2014 APIs are often specified by standards, but the term itself is broader than a formal industry standard."
],
"confidence": 0.78,
"name": "APIs",
"reasoning": "By the Protocol vs Standard rule, APIs are best treated as the interface contract systems use to communicate, rather than a software product or a data format.",
"skill_id": "apis",
"subtype": "application_programming_interfaces",
"type": "Protocol"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Authentication",
"id": null,
"rationale": "Covers mechanisms for verifying identity and establishing trusted access to systems and services. This skill belongs here when the focus is on proving who a user or service is, rather than what they are allowed to do after login.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Authentication",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Authentication",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "authentication",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "Authentication is a standard, well-scoped security concept in JDs and is unlikely to be confused with a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"OAuth 2.0",
"OpenID Connect",
"SAML",
"JWT",
"SSO",
"MFA",
"2FA",
"identity provider",
"session management",
"token-based auth",
"passwordless",
"LDAP",
"Kerberos",
"federated identity"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "Authentication is a core requirement in most software JDs (login, SSO, OAuth/OIDC, MFA) and is widely implemented across web, mobile, and cloud systems; vendor docs and hiring pipelines treat it as a standard competency."
},
"skill_id": "authentication",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers mechanisms for verifying identity and establishing trusted access to systems and services. This skill belongs here when the focus is on proving who a user or service is, rather than what they are allowed to do after login.",
"exemplar_skills": [
"Authentication",
"Login",
"Sign-in flows",
"Multi-factor authentication",
"OAuth authentication",
"Single sign-on",
"Token-based authentication"
],
"in_scope": "Authentication, login, sign-in, sign-out, MFA, password verification, OAuth authentication, SSO authentication, token-based authentication, certificate-based authentication",
"name": "Authentication",
"out_of_scope": "Authorization rules, role-based access control, cloud IAM policy administration, session persistence, protected navigation, network perimeter security",
"overlap_flags": [
{
"reason": "If the target skill is about app-side login/session behavior, that catalog dimension is a closer fit; this new dimension is the broader identity-verification concept.",
"with_dim_id": "authentication-flows-and-session-handling",
"with_dim_name": null,
"with_role": "Android Engineer, Frontend Engineer, Full Stack Developer"
},
{
"reason": "Identity configuration covers roles and access setup, but not the core act of authenticating users or services.",
"with_dim_id": "identity-and-access-configuration",
"with_dim_name": null,
"with_role": "ServiceNOW Developer"
},
{
"reason": "Cloud security guardrails may define authentication standards, but they do not own the authentication mechanism itself.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Authentication",
"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": "authentication"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"jwt",
"oauth-2-0",
"saml",
"openid-connect",
"refresh-tokens",
"session-cookies",
"aws-iam",
"managed-identities",
"rbac",
"endpoint-hardening"
],
"requires": [],
"skill_id": "authentication",
"suppress_on_match": []
},
"skill_id": "authentication",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.97,
"name": "Authentication",
"reasoning": "Authentication is a named knowledge unit about verifying identity, so by the Concept vs Methodology rule it is a Concept rather than a protocol or tool.",
"skill_id": "authentication",
"subtype": "authentication",
"type": "Concept"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Delivery Practices",
"id": null,
"rationale": "Covers the software delivery discipline of automating build, test, and release steps so changes can move safely to production. CD belongs here when it refers to the delivery methodology rather than a specific environment-management task.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CD",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "CD",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "continuous_delivery",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"continuous_integration",
"continuous_deployment"
],
"reasoning": "\u201cCD\u201d is a common abbreviation that can mean continuous delivery, continuous deployment, or even compact disc in some JDs. A reasonable extractor could confuse it with the closely related CI/CD skills."
},
"context_keywords": {
"context_keywords": [
"CI/CD",
"deployment pipeline",
"release automation",
"blue-green deployment",
"canary release",
"feature flags",
"rollback",
"staging environment",
"production deployment",
"build pipeline",
"artifact repository",
"versioning",
"infrastructure as code",
"GitOps",
"automated testing"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "Continuous Delivery is a standard DevOps practice and appears in many job descriptions alongside CI/CD and cloud platforms; major vendors like AWS, Azure, and GitHub document it as a mainstream delivery pattern."
},
"skill_id": "cd",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers the software delivery discipline of automating build, test, and release steps so changes can move safely to production. CD belongs here when it refers to the delivery methodology rather than a specific environment-management task.",
"exemplar_skills": [
"CD",
"continuous delivery",
"release automation",
"deployment automation",
"build-test-release pipeline",
"automated rollback"
],
"in_scope": "CD, continuous delivery, release automation, deployment automation, build-test-release pipelines, automated rollback, release orchestration, deployment verification",
"name": "Continuous Delivery Practices",
"out_of_scope": "Infrastructure provisioning, cloud account setup, and environment access management, which belong to environment operations and provisioning dimensions.",
"overlap_flags": [
{
"reason": "Both cover moving software through stages, but this dimension emphasizes delivery automation while the catalog dimension emphasizes environment lifecycle management.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "Delivery automation may be implemented on orchestration tools, but the orchestration platform itself is a separate operational skill cluster.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "CD",
"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": "cd"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"contract-tests",
"defect-retest"
],
"requires": [],
"skill_id": "cd",
"suppress_on_match": []
},
"skill_id": "cd",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.93,
"name": "CD",
"reasoning": "CD here most naturally means Continuous Delivery, which is a way of working for releasing software continuously, so by the Concept vs Methodology rule it is a Methodology.",
"skill_id": "cd",
"subtype": "continuous_delivery",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Integration",
"id": null,
"rationale": "Practices for automatically building, validating, and merging code changes through shared pipelines. CI belongs here because it is the core delivery discipline for integrating changes early and catching regressions before release.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CI",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "CI",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "continuous_integration_methodology",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"continuous_delivery",
"continuous_deployment"
],
"reasoning": "\"CI\" is a common abbreviation for continuous integration, but in JDs it can be loosely used alongside or mistaken for continuous delivery/deployment. The acronym is short enough that extractors may conflate these related DevOps practices."
},
"context_keywords": {
"context_keywords": [
"Jenkins",
"GitHub Actions",
"GitLab CI",
"CircleCI",
"Travis CI",
"build pipeline",
"automated testing",
"unit tests",
"integration tests",
"artifact repository",
"pipeline as code",
"merge request",
"pull request",
"build agent",
"deployment pipeline"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "CI is a standard hiring-pipeline requirement in many software JDs, with widespread GitHub/GitLab Actions, Jenkins, and Azure DevOps usage across teams."
},
"skill_id": "ci",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Practices for automatically building, validating, and merging code changes through shared pipelines. CI belongs here because it is the core delivery discipline for integrating changes early and catching regressions before release.",
"exemplar_skills": [
"CI",
"continuous integration",
"build validation",
"pull request checks",
"automated build pipelines",
"unit test gating"
],
"in_scope": "CI, continuous integration pipelines, automated build triggers, merge validation, pull request checks, unit test execution, linting, static analysis, artifact creation, pipeline status checks",
"name": "Continuous Integration",
"out_of_scope": "Continuous delivery/release promotion, deployment orchestration, environment provisioning, runtime monitoring and alerting, manual test case design",
"overlap_flags": [
{
"reason": "CI pipelines often use build tools, but this dimension is about the integration workflow rather than the tools themselves.",
"with_dim_id": "build-and-execution-tooling",
"with_dim_name": null,
"with_role": "Automation Tester"
},
{
"reason": "CI commonly runs automated tests, but the test framework dimension owns the test implementation details.",
"with_dim_id": "testing-and-automation-frameworks",
"with_dim_name": null,
"with_role": "iOS Engineer"
},
{
"reason": "CI may deploy to ephemeral environments, but promotion and environment lifecycle are separate concerns.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "CI",
"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": "ci"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"junit",
"confidence-intervals",
"confidence-thresholds"
],
"requires": [],
"skill_id": "ci",
"suppress_on_match": []
},
"skill_id": "ci",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.9,
"name": "CI",
"reasoning": "CI is fundamentally a way of working for integrating code frequently and validating changes continuously, so it fits the Concept vs Methodology rule as a Methodology rather than a Tool or Framework.",
"skill_id": "ci",
"subtype": "continuous_integration_methodology",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Continuous Integration and Delivery",
"id": null,
"rationale": "Automates code integration, testing, packaging, and release promotion from commit to deployment. CICD belongs here because it is the core delivery pipeline practice for building, validating, and shipping software changes reliably.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "CICD",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "CICD",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "continuous_integration_continuous_delivery",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "CICD is a standard DevOps acronym with a specific meaning in JDs; it is unlikely to be reasonably confused with a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"Jenkins",
"GitHub Actions",
"GitLab CI",
"Azure DevOps",
"CircleCI",
"Travis CI",
"pipeline",
"build automation",
"deployment pipeline",
"release automation",
"artifact repository",
"unit tests",
"integration tests",
"blue-green deployment",
"rollback"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "CI/CD appears in a large share of DevOps and platform engineering job descriptions and is a standard requirement in major cloud/vendor docs and tooling ecosystems."
},
"skill_id": "cicd",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Automates code integration, testing, packaging, and release promotion from commit to deployment. CICD belongs here because it is the core delivery pipeline practice for building, validating, and shipping software changes reliably.",
"exemplar_skills": [
"CICD",
"continuous integration",
"continuous delivery",
"continuous deployment",
"build pipeline automation",
"release automation"
],
"in_scope": "CICD, continuous integration, continuous delivery, continuous deployment, build pipelines, test automation in pipelines, artifact packaging, release automation",
"name": "Continuous Integration and Delivery",
"out_of_scope": "manual QA test case design, cloud infrastructure provisioning, runtime orchestration platforms, which are handled by other dimensions",
"overlap_flags": [
{
"reason": "CI/CD uses build tools and runners, but this dimension focuses on the end-to-end delivery pipeline rather than local execution tooling.",
"with_dim_id": "build-and-execution-tooling",
"with_dim_name": null,
"with_role": "Automation Tester"
},
{
"reason": "Pipeline promotion often coordinates environments, but that dimension owns environment lifecycle while this one owns integration and delivery automation.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "Deployment steps may target orchestration platforms, but platform operation is distinct from the CI/CD workflow itself.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "CICD",
"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": "cicd"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"runbooks",
"ansible",
"kubernetes",
"aws-cdk",
"azure-cli",
"cloud-run",
"policy-as-code",
"health-checks",
"circuit-breakers",
"shutdown-sequencing",
"escalation-paths"
],
"requires": [],
"skill_id": "cicd",
"suppress_on_match": []
},
"skill_id": "cicd",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 CI/CD is an operational process, not just a knowledge unit.",
"Architecture: ruled out \u2014 it describes delivery practices, not a system-shape pattern."
],
"confidence": 0.93,
"name": "CICD",
"reasoning": "By the Concept vs Methodology rule, CI/CD is a way of working for building, testing, and deploying software continuously rather than a software product or system shape.",
"skill_id": "cicd",
"subtype": "continuous_integration_continuous_delivery",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Platforms and Services",
"id": null,
"rationale": "Covers core cloud computing concepts and the use of public cloud services to build, run, and manage systems. This fits the target skill because \"Cloud\" usually refers to working with cloud provider capabilities, deployment models, and managed services rather than a narrower sub-discipline.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Cloud",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Cloud",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "cloud_computing",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "In JDs, \"cloud\" almost always denotes cloud computing as a domain. It is broad, but not typically confused with a distinct catalog skill name."
},
"context_keywords": {
"context_keywords": [
"AWS",
"Azure",
"Google Cloud",
"IaaS",
"PaaS",
"SaaS",
"Kubernetes",
"Docker",
"serverless",
"virtual machines",
"load balancer",
"auto-scaling",
"VPC",
"IAM",
"Terraform"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Cloud computing is a hiring-pipeline staple: AWS, Azure, and GCP appear in a large share of infrastructure and platform engineering JDs, and major vendors continue expanding cloud services rather than sunsetting them."
},
"skill_id": "cloud",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers core cloud computing concepts and the use of public cloud services to build, run, and manage systems. This fits the target skill because \"Cloud\" usually refers to working with cloud provider capabilities, deployment models, and managed services rather than a narrower sub-discipline.",
"exemplar_skills": [
"Cloud",
"AWS",
"Azure",
"Google Cloud Platform",
"IaaS",
"PaaS",
"SaaS"
],
"in_scope": "Cloud, AWS, Azure, Google Cloud Platform, cloud service models, IaaS, PaaS, SaaS, regions and availability zones, managed compute, managed storage, managed networking",
"name": "Cloud Platforms and Services",
"out_of_scope": "Cloud security hardening and policy controls, which belong in cloud-security-guardrails; environment promotion and release flow, which belong in environment-provisioning-and-promotion; workload orchestration, which belongs in orchestration-platforms",
"overlap_flags": [
{
"reason": "Cloud work often includes security configuration, but baseline security controls are a distinct cluster.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
},
{
"reason": "Cloud environments are often provisioned and promoted through delivery workflows, which is a separate operational concern.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [
{
"a_dim_id": "d_init_02",
"a_name": "Migration and Modernization Planning",
"a_role": "__skill_focal__",
"b_dim_id": "migration-and-modernization-planning",
"b_name": "Migration and Modernization Planning",
"b_role": "Cloud Architect",
"into": "d_merge_01",
"into_name": "Cloud Migration and Modernization Planning",
"merged_from": [
"d_init_02",
"migration-and-modernization-planning"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same cloud migration/modernization planning cluster. A includes cloud migration, adoption planning, rehosting/replatforming/refactoring, dependency mapping, landing zone planning, and cutover sequencing. B covers the same work: moving existing systems to cloud target states with sequencing, dependency analysis, modernization options, migration waves, and risk controls. The overlap is substantive, not just wording.",
"similarity": 0.8895464696759947
}
],
"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 industry/problem-space body of knowledge rather than a specific platform, service, tool, 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": "Containerization",
"id": null,
"rationale": "Covers packaging and running software in isolated containers, including the core concepts and tooling used to create and manage container images and runtimes. Containers belongs here because it is the foundational abstraction for container-based deployment and execution.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Containers",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Containers",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Architecture",
"skill_nature": "PATTERN",
"sub_category": "containerization_architecture",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"docker",
"kubernetes",
"containerization"
],
"reasoning": "\u201cContainers\u201d is broad in JDs and can mean containerization generally, or be mistaken for specific platform skills like Docker or Kubernetes. It may also overlap with the catalog\u2019s containerization concept."
},
"context_keywords": {
"context_keywords": [
"Docker",
"Kubernetes",
"Podman",
"containerd",
"OCI",
"images",
"registries",
"orchestration",
"Helm",
"Dockerfile",
"namespaces",
"cgroups",
"microservices",
"CI/CD",
"ECS"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Containers are a hiring-pipeline staple: Docker/Kubernetes appear in a large share of cloud/devops JDs, and major vendors (AWS, Azure, GCP) all offer first-class container services."
},
"skill_id": "containers",
"vendor_license": {
"confidence": 0.78,
"license": "apache_2",
"vendor": "Docker, Inc.",
"year_introduced": 2013
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers packaging and running software in isolated containers, including the core concepts and tooling used to create and manage container images and runtimes. Containers belongs here because it is the foundational abstraction for container-based deployment and execution.",
"exemplar_skills": [
"Containers",
"Docker",
"Container images",
"Container runtime",
"Container isolation",
"Container registries"
],
"in_scope": "Containers, Docker, container images, container runtime basics, image layers, container lifecycle, container isolation, container registries",
"name": "Containerization",
"out_of_scope": "Kubernetes scheduling and cluster operations, CI/CD pipeline design, VM provisioning, network policy design, application code inside containers",
"overlap_flags": [
{
"reason": "Containerization overlaps with orchestration because containers are commonly deployed on Kubernetes and similar platforms, but orchestration owns scheduling and cluster management.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
},
{
"reason": "Container images are often promoted across environments, but that dimension focuses on environment lifecycle rather than container fundamentals.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Containers",
"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": "containers"
},
"relationships": {
"child_skills": [
"kubernetes"
],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "containers",
"suppress_on_match": []
},
"skill_id": "containers",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Containers",
"reasoning": "Containers are fundamentally a system-shape pattern for packaging and isolating applications, so by the Architecture vs Concept rule they fit Architecture rather than a tool or format.",
"skill_id": "containers",
"subtype": "containerization_architecture",
"type": "Architecture"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Application Development",
"id": null,
"rationale": "Building application logic, services, and integrations that run in cloud environments. The skill \u0027Develop\u0027 fits here when it refers to writing the code that implements cloud-native features and service behavior.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Develop",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Develop",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "software_development_process",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"software_development"
],
"reasoning": "\"Develop\" is a generic verb and can easily be mistaken for the broader software development skill in JDs; the standalone term is too unspecific to reliably map without context."
},
"context_keywords": {
"context_keywords": [
"SDLC",
"Agile",
"Scrum",
"Kanban",
"requirements gathering",
"user stories",
"sprint planning",
"code review",
"CI/CD",
"version control",
"Git",
"testing",
"deployment",
"refactoring",
"technical debt"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "Software development process is a core hiring requirement across most engineering JDs and appears in standard SDLC/Agile/DevOps role descriptions; it is broadly adopted rather than specialized."
},
"skill_id": "develop",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Building application logic, services, and integrations that run in cloud environments. The skill \u0027Develop\u0027 fits here when it refers to writing the code that implements cloud-native features and service behavior.",
"exemplar_skills": [
"Develop",
"cloud application development",
"service implementation",
"API integration",
"server-side coding",
"workflow implementation"
],
"in_scope": "Develop, cloud application code, service implementation, API integration, business logic, SDK usage, server-side scripting, workflow code",
"name": "Cloud Application Development",
"out_of_scope": "Cloud provisioning, IAM configuration, network setup, container orchestration, monitoring, and security hardening, which belong to platform or operations dimensions",
"overlap_flags": [
{
"reason": "Application code often deploys onto orchestrated runtimes, but this dimension is about writing the code rather than running it.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
},
{
"reason": "Developed applications move through environments, but environment lifecycle is a separate delivery concern.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Develop",
"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": "develop"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "develop",
"suppress_on_match": []
},
"skill_id": "develop",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 the term is too action-oriented and does not name a standalone knowledge unit.",
"SoftSkill: ruled out \u2014 it is not an interpersonal capability."
],
"confidence": 0.67,
"name": "Develop",
"reasoning": "By the Concept vs Methodology rule, \"Develop\" is best treated as a way of working or process rather than a knowledge unit, so it fits Methodology.",
"skill_id": "develop",
"subtype": "software_development_process",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Delivery Pipeline Automation",
"id": null,
"rationale": "Covers automating build, test, release, and deployment workflows that move software from source control to running environments. DevOps belongs here when it refers to the operational practice of continuous integration and continuous delivery.",
"slug": "d_init_02",
"source": "llm"
},
"input_skill": "Devops",
"llm_role": null,
"roles_from_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": "Devops",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
}
]
}
],
"input_skill": "Devops",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "devops_practices",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cDevOps\u201d is a well-established methodology term with a specific meaning in JDs; it is unlikely to be reasonably confused with a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"CI/CD",
"Jenkins",
"GitLab CI",
"GitHub Actions",
"Terraform",
"Ansible",
"Kubernetes",
"Docker",
"Helm",
"Prometheus",
"Grafana",
"AWS",
"Azure",
"Linux",
"Infrastructure as Code"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "DevOps is a standard hiring keyword across software and platform engineering JDs, and major vendors like AWS, Azure, and Google Cloud all market DevOps toolchains and certifications, indicating broad adoption."
},
"skill_id": "devops",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [
{
"a_dim_id": "d_init_03",
"a_name": "Scaling and Resilience Engineering",
"a_role": "__skill_focal__",
"b_dim_id": "scaling-and-resilience-engineering",
"b_name": "Scaling and Resilience Engineering",
"b_role": "Azure Cloud Engineer",
"pair_kind": "cross_role",
"reasoning": "Same wording, but different role scope. Dim A is broader DevOps/SRE-style reliability: its description covers keeping services available under load and during failures, with exemplars like high availability, failover, autoscaling, capacity planning, and resilience testing. Dim B is Azure Cloud Engineer-specific and focuses on Azure-side patterns to improve availability, capacity, and fault tolerance. The overlap is in generic scaling/reliability terms, but A targets operational service health, while B targets cloud-platform implementation in Azure.",
"similarity": 0.7799495308324915
}
],
"locked_dimensions": [
{
"description": "Covers automating build, test, release, and deployment workflows that move software from source control to running environments. DevOps belongs here when it refers to the operational practice of continuous integration and continuous delivery.",
"exemplar_skills": [
"Devops",
"CI/CD",
"deployment automation",
"release pipelines",
"build pipelines",
"continuous delivery"
],
"in_scope": "DevOps, CI/CD, build pipelines, release pipelines, deployment automation, pipeline triggers, artifact promotion, rollback automation, Git-based delivery workflows",
"name": "Delivery Pipeline Automation",
"out_of_scope": "Manual test case design, application feature development, runtime cluster administration, cloud network design, security hardening policies",
"overlap_flags": [
{
"reason": "Both involve build-related automation, but this dimension is about end-to-end delivery pipelines rather than local build/test tools.",
"with_dim_id": "build-and-execution-tooling",
"with_dim_name": null,
"with_role": "Automation Tester"
},
{
"reason": "Pipelines often run tests, but test framework design and authoring belong to the testing dimension.",
"with_dim_id": "testing-and-automation-frameworks",
"with_dim_name": null,
"with_role": "iOS Engineer"
}
],
"tentative_id": "d_init_02"
},
{
"description": "Covers operational patterns that keep services available under load and during failures, including capacity planning, redundancy, and recovery behavior. DevOps can map here when used in the broader site reliability sense of keeping systems healthy in production.",
"exemplar_skills": [
"Devops",
"high availability",
"autoscaling",
"failover",
"capacity planning",
"fault tolerance"
],
"in_scope": "DevOps, high availability, failover, autoscaling, capacity planning, redundancy, fault tolerance, resilience testing, operational reliability, service recovery",
"name": "Scaling and Resilience Engineering",
"out_of_scope": "Security controls, environment provisioning, application feature logic, data pipeline monitoring, container image packaging",
"overlap_flags": [
{
"reason": "Both may use monitoring signals, but this dimension focuses on resilience outcomes while observability owns the diagnostic instrumentation.",
"with_dim_id": "data-pipeline-observability",
"with_dim_name": null,
"with_role": "Data Engineer"
},
{
"reason": "Resilient architectures often intersect with secure cloud design, but security controls are not the primary concern here.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
}
],
"tentative_id": "d_init_03"
}
],
"merge_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Environment Provisioning and Promotion",
"a_role": "__skill_focal__",
"b_dim_id": "environment-provisioning-and-promotion",
"b_name": "Environment Provisioning and Promotion",
"b_role": "DevOps Engineer",
"into": "d_merge_01",
"into_name": "Environment Lifecycle Provisioning and Promotion",
"merged_from": [
"d_init_01",
"environment-provisioning-and-promotion"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same DevOps skill cluster: provisioning, configuring, and promoting consistent dev/test/staging/prod environments. Dim A\u2019s scope includes environment provisioning, release promotion, dev/test/staging/prod parity, infrastructure templates, and configuration drift control; its exemplars are Devops, environment provisioning, release promotion, deployment automation, environment parity, and configuration management. Dim B says the same thing in different words and also centers on lifecycle and parity between environments. The overlap is substantive, not just lexical.",
"similarity": 0.8580755562211444
}
],
"placed": {
"name": "Devops",
"placement_confidence": 0.92,
"primary_dimension": "d_init_02",
"reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [
"d_init_03"
],
"skill_id": "devops"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"kubernetes",
"ansible",
"policy-as-code",
"runbooks",
"terraform",
"aws-cdk",
"aws-cloudformation",
"azure-monitor",
"dashboards",
"failover"
],
"requires": [],
"skill_id": "devops",
"suppress_on_match": []
},
"skill_id": "devops",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.91,
"name": "Devops",
"reasoning": "DevOps is fundamentally a way of working that combines development and operations practices, so by the Concept vs Methodology rule it fits Methodology rather than a tool or architecture.",
"skill_id": "devops",
"subtype": "devops_practices",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Engineering",
"id": null,
"rationale": "Builds and operates cloud-based infrastructure, services, and deployment foundations. This is the best fit for the broad role label \"Engineer\" with a Cloud Engineer hint, since it typically spans provisioning, networking, reliability, and platform operations.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Engineer",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Engineer",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "engineering",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cEngineer\u201d is a broad job title/domain term, but in JDs it usually denotes the role itself rather than a distinct catalog skill that would be confused with another specific skill name."
},
"context_keywords": {
"context_keywords": [
"CAD",
"FEA",
"prototyping",
"design review",
"requirements gathering",
"systems engineering",
"manufacturing",
"tolerancing",
"DFM",
"root cause analysis",
"test plan",
"validation",
"commissioning",
"technical drawings",
"BOM"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "\u201cEngineer\u201d is a core job-family title across nearly all technical job postings and hiring pipelines, with very high JD volume on major boards and ATS taxonomies."
},
"skill_id": "engineer",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Builds and operates cloud-based infrastructure, services, and deployment foundations. This is the best fit for the broad role label \"Engineer\" with a Cloud Engineer hint, since it typically spans provisioning, networking, reliability, and platform operations.",
"exemplar_skills": [
"Engineer",
"cloud infrastructure design",
"cloud resource provisioning",
"cloud operations",
"virtual networks",
"compute services",
"storage services",
"cloud architecture"
],
"in_scope": "Engineer, cloud infrastructure design, IaaS and PaaS operations, virtual networks, compute and storage services, cloud resource provisioning, cloud architecture basics, deployment foundations, cloud operations",
"name": "Cloud Engineering",
"out_of_scope": "Application feature development, data analysis workflows, mobile app implementation, model training and experimentation, end-user UI design",
"overlap_flags": [
{
"reason": "Cloud engineering often participates in migration planning, but that dimension focuses on sequencing and target-state strategy rather than steady-state cloud operations.",
"with_dim_id": "migration-and-modernization-planning",
"with_dim_name": null,
"with_role": "Cloud Architect"
},
{
"reason": "Both involve creating environments, but this dimension is broader and includes the underlying cloud platform and infrastructure layer.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "Cloud engineers often implement scaling and fault-tolerance patterns, but that catalog dimension is specifically about availability and resilience tactics.",
"with_dim_id": "scaling-and-resilience-engineering",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Engineer",
"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": "engineer"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "engineer",
"suppress_on_match": []
},
"skill_id": "engineer",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.91,
"name": "Engineer",
"reasoning": "Engineer is best treated as a Domain because it denotes a broad professional field/body of knowledge rather than a tool, framework, or methodology.",
"skill_id": "engineer",
"subtype": "engineering",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "General Engineering Practice",
"id": null,
"rationale": "Broad engineering work that spans designing, building, and maintaining technical systems when no narrower specialty is implied. This skill is too generic to map cleanly to a more specific catalog dimension, so it serves as the umbrella for general implementation and problem-solving work.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Engineering",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Engineering",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "engineering",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cEngineering\u201d is a broad domain label, but in a JD it usually denotes the field itself rather than a different catalog skill; it\u2019s not a short acronym or product name with a likely collision."
},
"context_keywords": {
"context_keywords": [
"CAD",
"FEA",
"prototyping",
"design review",
"requirements analysis",
"systems engineering",
"manufacturing",
"tolerance stack-up",
"DFM",
"root cause analysis",
"test plan",
"validation",
"verification",
"BOM",
"technical specifications"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Engineering is a core hiring-pipeline domain across nearly all tech JDs and university programs; market demand is broad and persistent rather than niche or sunsetted."
},
"skill_id": "engineering",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Broad engineering work that spans designing, building, and maintaining technical systems when no narrower specialty is implied. This skill is too generic to map cleanly to a more specific catalog dimension, so it serves as the umbrella for general implementation and problem-solving work.",
"exemplar_skills": [
"Engineering",
"software engineering",
"systems engineering",
"technical implementation",
"solution engineering"
],
"in_scope": "Engineering, software implementation, technical problem solving, system building, code changes, cross-functional technical delivery",
"name": "General Engineering Practice",
"out_of_scope": "Cloud architecture, security hardening, orchestration platforms, testing, data engineering, and other specialized disciplines owned by narrower dimensions",
"overlap_flags": [],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Engineering",
"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": "engineering"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "engineering",
"suppress_on_match": []
},
"skill_id": "engineering",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.98,
"name": "Engineering",
"reasoning": "Engineering is a vertical/problem-space body of knowledge rather than a tool, language, or methodology, so it fits the Domain type.",
"skill_id": "engineering",
"subtype": "engineering",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure as Code Practices",
"id": null,
"rationale": "Covers authoring and maintaining declarative infrastructure definitions used to provision and manage cloud resources. IaC belongs here because it is the core practice for codifying infrastructure instead of configuring it manually.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "IaC",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "IaC",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "infrastructure_as_code",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"infrastructure_as_code"
],
"reasoning": "\"IaC\" is a common abbreviation for Infrastructure as Code, but in JDs it can also be written out as the broader methodology skill. A parser could confuse the abbreviation with the full skill entry."
},
"context_keywords": {
"context_keywords": [
"Terraform",
"CloudFormation",
"Pulumi",
"Ansible",
"Chef",
"Puppet",
"Bicep",
"ARM templates",
"CDK",
"declarative",
"state management",
"provisioning",
"immutable infrastructure",
"GitOps",
"drift detection"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "IaC is broadly listed in DevOps/SRE job descriptions and supported by major vendors (Terraform, CloudFormation, Pulumi), making it a hiring-pipeline staple rather than a niche practice."
},
"skill_id": "iac",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers authoring and maintaining declarative infrastructure definitions used to provision and manage cloud resources. IaC belongs here because it is the core practice for codifying infrastructure instead of configuring it manually.",
"exemplar_skills": [
"IaC",
"Terraform",
"CloudFormation",
"Bicep",
"ARM templates",
"Pulumi",
"infrastructure modules",
"drift detection"
],
"in_scope": "IaC, Terraform, CloudFormation, Bicep, ARM templates, Pulumi, declarative infrastructure, infrastructure modules, state management, plan/apply workflows, drift detection",
"name": "Infrastructure as Code Practices",
"out_of_scope": "Manual console-based provisioning, application deployment packaging, container orchestration, and app release automation, which belong to deployment or orchestration dimensions.",
"overlap_flags": [
{
"reason": "IaC is commonly used to create and promote consistent environments, so it overlaps with environment lifecycle management.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "Infrastructure definitions often encode security baselines and policy controls, overlapping with cloud security guardrails.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
},
{
"reason": "IaC can define redundancy, autoscaling, and failover resources, which overlaps with resilience-oriented infrastructure design.",
"with_dim_id": "scaling-and-resilience-engineering",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "IaC",
"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": "iac"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"aws-cdk",
"cspm",
"clean-architecture"
],
"requires": [],
"skill_id": "iac",
"suppress_on_match": []
},
"skill_id": "iac",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 IaC is an operational practice/process, not just a knowledge unit.",
"Architecture: ruled out \u2014 it describes how infrastructure is managed, not a system shape pattern."
],
"confidence": 0.91,
"name": "IaC",
"reasoning": "IaC is fundamentally a way of working for provisioning and managing infrastructure through code, so by the Concept vs Methodology rule it fits Methodology rather than a tool or language.",
"skill_id": "iac",
"subtype": "infrastructure_as_code",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Infrastructure Architecture",
"id": null,
"rationale": "Covers the foundational cloud and systems layer that supports applications, services, and operations. Use this when Infrastructure is meant broadly as the underlying compute, storage, networking, and platform foundation rather than a narrower operational specialty.",
"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": "In JDs, \"Infrastructure\" usually denotes the general IT/cloud/platform domain, not a specific overloaded term. It is broad, but not typically confused with a distinct catalog skill."
},
"context_keywords": {
"context_keywords": [
"Terraform",
"Ansible",
"Kubernetes",
"Docker",
"CI/CD",
"AWS",
"Azure",
"GCP",
"IaC",
"Linux",
"networking",
"load balancer",
"VPC",
"monitoring",
"observability"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Infrastructure is a core hiring-pipeline domain across cloud/DevOps JDs; roles routinely list AWS/Azure, Terraform, Kubernetes, and CI/CD under infrastructure responsibilities."
},
"skill_id": "infrastructure",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers the foundational cloud and systems layer that supports applications, services, and operations. Use this when Infrastructure is meant broadly as the underlying compute, storage, networking, and platform foundation rather than a narrower operational specialty.",
"exemplar_skills": [
"Infrastructure",
"cloud infrastructure",
"compute",
"storage",
"networking",
"virtual machines",
"load balancers",
"landing zones"
],
"in_scope": "Infrastructure, cloud infrastructure, compute, storage, networking, virtual machines, subnets, load balancers, resource groups, platform foundations, landing zones, shared services",
"name": "Infrastructure Architecture",
"out_of_scope": "Application development, container image packaging, orchestration platform administration, security policy enforcement, backup execution, observability tooling",
"overlap_flags": [
{
"reason": "Provisioning and promotion are concrete lifecycle activities within broader infrastructure architecture.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "Infrastructure includes networking components, but packet-level troubleshooting belongs to the network analysis dimension.",
"with_dim_id": "network-protocols-and-packet-analysis",
"with_dim_name": null,
"with_role": "Network Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [
{
"a_dim_id": "d_init_02",
"a_name": "Scaling and Resilience Engineering",
"a_role": "__skill_focal__",
"b_dim_id": "scaling-and-resilience-engineering",
"b_name": "Scaling and Resilience Engineering",
"b_role": "Azure Cloud Engineer",
"into": "d_merge_01",
"into_name": "Cloud Scaling and Resilience Engineering",
"merged_from": [
"d_init_02",
"scaling-and-resilience-engineering"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same infrastructure resilience cluster: keeping cloud systems available, fault-tolerant, and scalable. Dim A covers capacity, redundancy, failover, HA, load balancing, DR, and multi-zone deployment; Dim B says Azure-side patterns for availability, capacity, and fault tolerance. The exemplar skills in A (autoscaling, high availability, failover, redundancy, capacity planning, disaster recovery) are the same substance as B\u2019s Azure resilience focus. This is a role-specific wording difference, not a distinct skill area.",
"similarity": 0.7651382651957203
},
{
"a_dim_id": "d_init_03",
"a_name": "Environment Provisioning and Promotion",
"a_role": "__skill_focal__",
"b_dim_id": "environment-provisioning-and-promotion",
"b_name": "Environment Provisioning and Promotion",
"b_role": "DevOps Engineer",
"into": "d_merge_02",
"into_name": "Environment Provisioning, Parity, and Promotion",
"merged_from": [
"d_init_03",
"environment-provisioning-and-promotion"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same cluster: provisioning and promoting cloud/platform environments across dev/test/staging/prod. A includes concrete IaC examples (Terraform, CloudFormation, ARM/Bicep) and environment readiness/parity; B says the same lifecycle-focused work and parity. The exemplar skills in A would clearly fit B, and B adds no distinct anchor beyond the same concept.",
"similarity": 0.8482991319046442
}
],
"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",
"thin-provisioning",
"terraform",
"capacity-forecasting",
"dependency-mapping",
"acls",
"expressroute",
"pulumi",
"endpoint-hardening",
"feature-flags",
"policy-enforcement"
],
"requires": [],
"skill_id": "infrastructure",
"suppress_on_match": []
},
"skill_id": "infrastructure",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.93,
"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": "NIM Deployment and Serving",
"id": null,
"rationale": "Covers deploying, configuring, and operating NVIDIA NIM services for model inference in cloud environments. This fits the target skill because NIM is primarily about packaging and serving AI models through a managed runtime and API surface.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "NIM",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "NIM",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Language",
"skill_nature": "LANGUAGE",
"sub_category": "systems_programming_language",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "Nim is a distinct programming language name; in JDs it is usually unambiguous and not commonly confused with another catalog skill."
},
"context_keywords": {
"context_keywords": [
"metaprogramming",
"macros",
"compile-time",
"ARC",
"ORC",
"GC",
"C interop",
"FFI",
"cross-compilation",
"static typing",
"generics",
"templates",
"asyncdispatch",
"nimble",
"C backend"
]
},
"maturity": {
"confidence": 0.91,
"maturity": "niche",
"reasoning": "Nim appears in relatively few job postings compared with mainstream languages, and GitHub activity remains far below Python/Go/Rust; it has a small but active community rather than broad hiring demand."
},
"skill_id": "nim",
"vendor_license": {
"confidence": 0.93,
"license": "mit",
"vendor": "Nim Foundation",
"year_introduced": 2008
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers deploying, configuring, and operating NVIDIA NIM services for model inference in cloud environments. This fits the target skill because NIM is primarily about packaging and serving AI models through a managed runtime and API surface.",
"exemplar_skills": [
"NIM",
"NVIDIA NIM",
"deploying NIM containers",
"configuring NIM inference endpoints",
"operating GPU model serving"
],
"in_scope": "NIM, NVIDIA NIM, NIM containers, NIM microservices, model inference endpoints, GPU-backed serving, model runtime configuration, deployment manifests, service exposure, scaling NIM workloads",
"name": "NIM Deployment and Serving",
"out_of_scope": "Training foundation models, prompt engineering, general Kubernetes administration, data preprocessing pipelines, which belong to model development, application logic, orchestration platforms, or data engineering dimensions",
"overlap_flags": [
{
"reason": "NIM is a specific model-serving runtime and packaging approach, so it overlaps with the broader model deployment dimension.",
"with_dim_id": "model-serving-deployment-and-runtime-packaging",
"with_dim_name": null,
"with_role": "MLOps Engineer, Machine Learning Engineer"
},
{
"reason": "NIM deployments often run on Kubernetes or similar schedulers, but the dimension is about the serving runtime rather than the orchestration layer.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "NIM",
"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": "nim"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"nunit",
"numpy",
"npm",
"newman",
"json"
],
"requires": [],
"skill_id": "nim",
"suppress_on_match": []
},
"skill_id": "nim",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.99,
"name": "NIM",
"reasoning": "NIM is fundamentally a programming language, so it fits the Language type rather than a library, tool, or framework.",
"skill_id": "nim",
"subtype": "systems_programming_language",
"type": "Language"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Nemo Platform Operations",
"id": null,
"rationale": "Operational use of the Nemo platform for running, configuring, and troubleshooting cloud workloads. This fits the target skill because Nemo is treated here as a platform-specific operational competency rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Nemo",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Nemo",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Tool",
"skill_nature": "TOOL",
"sub_category": "desktop_application",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\"Nemo\" is a specific desktop file manager name and is not a common overloaded JD term; typical job descriptions would not plausibly confuse it with another catalog skill."
},
"context_keywords": {
"context_keywords": [
"GStreamer",
"DeepStream",
"RTSP",
"ONNX",
"TensorRT",
"CUDA",
"OpenCV",
"FFmpeg",
"PyTorch",
"TensorFlow",
"object detection",
"video analytics",
"multimedia pipeline",
"streaming",
"inference"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "niche",
"reasoning": "Nemo is the Linux Cinnamon file manager; job postings rarely list it directly, and market demand is usually for general Linux desktop/admin skills rather than Nemo specifically."
},
"skill_id": "nemo",
"vendor_license": {
"confidence": 0.78,
"license": "other_open",
"vendor": "NVIDIA",
"year_introduced": 2018
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Operational use of the Nemo platform for running, configuring, and troubleshooting cloud workloads. This fits the target skill because Nemo is treated here as a platform-specific operational competency rather than a generic cloud concept.",
"exemplar_skills": [
"Nemo",
"Nemo deployment workflows",
"Nemo runtime configuration",
"Nemo troubleshooting",
"Nemo operational monitoring"
],
"in_scope": "Nemo, Nemo deployment workflows, Nemo runtime configuration, Nemo job execution, Nemo troubleshooting, Nemo environment setup, Nemo operational monitoring",
"name": "Nemo Platform Operations",
"out_of_scope": "orchestration-platforms such as Kubernetes or Airflow, cloud-security-guardrails, service-architecture-and-integration, streaming-data-processing, model-serving-deployment-and-runtime-packaging",
"overlap_flags": [
{
"reason": "If Nemo is used to schedule or run workloads, it can overlap with general orchestration platform operations.",
"with_dim_id": "orchestration-platforms",
"with_dim_name": null,
"with_role": "Cloud Engineer, DevOps Engineer"
},
{
"reason": "Nemo setup may intersect with environment creation and promotion when the platform is provisioned across stages.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Nemo",
"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": "nemo"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "nemo",
"suppress_on_match": []
},
"skill_id": "nemo",
"split_log": [],
"typed": {
"alternatives_considered": [
"Framework: ruled out \u2014 Nemo is not a codebase you build applications inside.",
"Platform: ruled out \u2014 Nemo is not a hosted multi-tenant environment with APIs.",
"Library: ruled out \u2014 Nemo is not primarily a package imported by application code."
],
"confidence": 0.62,
"name": "Nemo",
"reasoning": "By the Tool vs Framework rule, Nemo is best treated as software a user runs rather than something used to build applications, and it is not a hosted multi-tenant environment so Platform does not fit.",
"skill_id": "nemo",
"subtype": "desktop_application",
"type": "Tool"
},
"warnings": []
},
"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": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "networking",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cNetworking\u201d is a broad domain term, but in JDs it usually clearly refers to computer networking. It is not a short acronym or product name likely to be mistaken for a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"TCP/IP",
"OSI model",
"subnetting",
"routing",
"switching",
"VLAN",
"DNS",
"DHCP",
"BGP",
"VPN",
"firewall",
"load balancer",
"NAT",
"LAN",
"WAN"
]
},
"maturity": {
"confidence": 0.97,
"maturity": "well_known",
"reasoning": "Networking is a core requirement in many infrastructure, backend, and SRE job descriptions, and vendor cert tracks like Cisco CCNA/CCNP and AWS networking services show sustained market demand."
},
"skill_id": "networking",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [],
"merge_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Network Protocols and Packet Analysis",
"a_role": "__skill_focal__",
"b_dim_id": "network-protocols-and-packet-analysis",
"b_name": "Network Protocols and Packet Analysis",
"b_role": "Network Engineer",
"into": "d_merge_01",
"into_name": "Network Protocols, Packet Analysis, and Connectivity Troubleshooting",
"merged_from": [
"d_init_01",
"network-protocols-and-packet-analysis"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same low-level networking cluster: protocol behavior, packet inspection, and troubleshooting traffic flow. Dim A includes TCP/IP, UDP, HTTP, DNS, TLS, Wireshark, traceroute, ping, subnetting, MTU, and latency analysis, all framed around packet flow and communication issues. Dim B says the same thing in different words: lower-level protocol behavior and packet inspection, with troubleshooting and design based on packet exchanges and protocol states. The exemplar skills in A map directly to B\u2019s description, so this is a true duplicate, not a cross-role distinction.",
"similarity": 0.8230221840491633
}
],
"placed": {
"name": "Networking",
"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": "networking"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"subnetting",
"http",
"grpc",
"replication",
"failover",
"security-groups",
"kubernetes"
],
"requires": [],
"skill_id": "networking",
"suppress_on_match": []
},
"skill_id": "networking",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.96,
"name": "Networking",
"reasoning": "Networking is best treated as a Domain because it denotes a broad technical problem-space/body of knowledge rather than a specific protocol, tool, or architecture.",
"skill_id": "networking",
"subtype": "networking",
"type": "Domain"
},
"warnings": [
"placement_stub_no_locked_dimensions"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Pipeline",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Architecture",
"skill_nature": "PATTERN",
"sub_category": "data_pipeline_architecture",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"ci_cd_pipeline",
"etl_pipeline",
"ml_pipeline"
],
"reasoning": "\u201cPipeline\u201d is overloaded in JDs and can mean CI/CD, ETL/data pipelines, or ML pipelines. A generic mention could plausibly refer to these other catalog skills."
},
"context_keywords": {
"context_keywords": [
"ETL",
"ELT",
"orchestration",
"Airflow",
"Dagster",
"Apache Beam",
"Kafka",
"Spark",
"batch processing",
"stream processing",
"data ingestion",
"data lake",
"data warehouse",
"workflow scheduling",
"data transformation"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "well_known",
"reasoning": "Data pipeline architecture is a common requirement in data/analytics JDs and cloud vendor docs; roles frequently ask for ETL/ELT, Airflow, and Kafka-based pipelines."
},
"skill_id": "pipeline",
"vendor_license": {
"confidence": 0.98,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [],
"merge_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Pipeline Observability",
"a_role": "__skill_focal__",
"b_dim_id": "data-pipeline-observability",
"b_name": "Data Pipeline Observability",
"b_role": "Data Engineer",
"into": "d_merge_01",
"into_name": "Data Pipeline Observability and Troubleshooting",
"merged_from": [
"d_init_01",
"data-pipeline-observability"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same pipeline observability cluster. A includes monitoring/troubleshooting for health, freshness, throughput, failure modes, alerts, diagnostics, retry analysis, DAG health, and dashboards. B covers monitoring/troubleshooting for health, freshness, throughput, and detecting failures before consumers are impacted. A\u0027s exemplar skills (pipeline monitoring, data freshness monitoring, DAG health checks, job failure triage, throughput alerting, pipeline diagnostics) fit B exactly. The cross-role label does not change the substance.",
"similarity": 0.8073238714047576
},
{
"a_dim_id": "d_init_02",
"a_name": "Service Architecture and Integration",
"a_role": "__skill_focal__",
"b_dim_id": "service-architecture-and-integration",
"b_name": "Service Architecture and Integration",
"b_role": "Backend Engineer",
"into": "d_merge_02",
"into_name": "Service Architecture, Integration, and Service Flow Design",
"merged_from": [
"d_init_02",
"service-architecture-and-integration"
],
"pair_kind": "cross_role",
"reasoning": "Both dims describe the same backend service-architecture cluster: how services are decomposed and connected into an end-to-end flow. Dim A focuses on \"Designing how services and processing steps connect\" with exemplars like \"Pipeline,\" \"service pipeline,\" \"integration flow,\" and \"request routing.\" Dim B frames the same idea as \"structuring backend systems as services and coordinating calls across internal and external dependencies,\" including safe evolution. The wording differs, but the skills overlap substantially.",
"similarity": 0.6891199722119484
}
],
"placed": {
"name": "Pipeline",
"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": "pipeline"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"dependency-mapping",
"dispatchers",
"failover",
"shutdown-sequencing"
],
"requires": [],
"skill_id": "pipeline",
"suppress_on_match": []
},
"skill_id": "pipeline",
"split_log": [],
"typed": {
"alternatives_considered": [
"Concept: ruled out \u2014 while pipelines are a common idea, the term here more fundamentally refers to a structural system pattern.",
"Methodology: ruled out \u2014 it is not a way of working or process like Agile or TDD."
],
"confidence": 0.78,
"name": "Pipeline",
"reasoning": "Pipeline is best treated as an Architecture because, by the Architecture vs Concept rule, it describes a system shape or flow pattern for moving and transforming work/data rather than a specific knowledge unit or tool.",
"skill_id": "pipeline",
"subtype": "data_pipeline_architecture",
"type": "Architecture"
},
"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": "Platform Administration",
"id": null,
"rationale": "Covers administering the underlying platform layer used to configure, operate, and support environments and instances. The skill \"Platform\" fits here when it refers to the operational platform itself rather than a specific app feature or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Platform",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Platform",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "hosted_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"platform_engineering",
"platform_as_a_service"
],
"reasoning": "\u201cPlatform\u201d is very generic in JDs and can refer to platform engineering or a platform-as-a-service offering rather than this specific catalog skill."
},
"context_keywords": {
"context_keywords": [
"SaaS",
"PaaS",
"multi-tenant",
"tenant isolation",
"self-service provisioning",
"service catalog",
"deployment pipeline",
"CI/CD",
"observability",
"SRE",
"Kubernetes",
"Terraform",
"API gateway",
"RBAC",
"feature flags"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "well_known",
"reasoning": "Broadly listed in JDs across cloud/DevOps roles; major vendors (AWS, Azure, GCP) and platform engineering teams make it a common hiring signal."
},
"skill_id": "platform",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers administering the underlying platform layer used to configure, operate, and support environments and instances. The skill \"Platform\" fits here when it refers to the operational platform itself rather than a specific app feature or cloud service.",
"exemplar_skills": [
"Platform",
"platform administration",
"instance setup",
"tenant configuration",
"admin console operations",
"environment readiness"
],
"in_scope": "Platform, instance setup, tenant configuration, admin console operations, environment readiness, platform settings, operational support, release coordination",
"name": "Platform Administration",
"out_of_scope": "Application feature coding, cloud network architecture, container orchestration, identity policy design, and backup recovery, which belong to other engineering dimensions",
"overlap_flags": [
{
"reason": "If the target refers specifically to ServiceNow, this overlaps strongly with instance setup and administration in that catalog dimension.",
"with_dim_id": "platform-administration-and-instance-setup",
"with_dim_name": null,
"with_role": "ServiceNOW Developer"
},
{
"reason": "Platform administration often supports environment creation and promotion, but that dimension is broader across the delivery lifecycle.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Platform",
"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": "platform"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"kubernetes",
"cloud-run",
"pulumi",
"terraform"
],
"requires": [],
"skill_id": "platform",
"suppress_on_match": []
},
"skill_id": "platform",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.99,
"name": "Platform",
"reasoning": "By the Platform vs Tool rule, \"Platform\" is the hosted multi-tenant environment category itself, not a user-run tool or a specific service.",
"skill_id": "platform",
"subtype": "hosted_platform",
"type": "Platform"
},
"warnings": []
},
"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": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "cybersecurity",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "In JDs, \"security\" in a cybersecurity domain is usually clear from context and not typically confused with another catalog skill."
},
"context_keywords": {
"context_keywords": [
"threat modeling",
"vulnerability assessment",
"penetration testing",
"SIEM",
"IDS/IPS",
"zero trust",
"IAM",
"SOC 2",
"ISO 27001",
"CIS Controls",
"incident response",
"risk assessment",
"encryption",
"MFA",
"OWASP"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Security is a standard requirement in most engineering JDs and hiring pipelines; roles commonly list AppSec, cloud security, and secure SDLC alongside core stack skills."
},
"skill_id": "security",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [],
"merge_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Cloud Security Guardrails",
"a_role": "__skill_focal__",
"b_dim_id": "cloud-security-guardrails",
"b_name": "Cloud Security Guardrails",
"b_role": "Cloud Engineer",
"into": "d_merge_01",
"into_name": "Cloud Security Guardrails and Baseline Controls",
"merged_from": [
"d_init_01",
"cloud-security-guardrails"
],
"pair_kind": "cross_role",
"reasoning": "These two dimensions describe the same conceptual cluster. Dim A defines cloud security as baseline security architecture and preventive controls, explicitly including security hardening, policy-as-code, encryption at rest, network segmentation, and least privilege. Dim B says the same thing in slightly different words: baseline security architecture for cloud environments, preventive controls, and hardening standards that implementation teams must follow across workloads and accounts. The exemplar skills in A (security hardening, least privilege, policy-as-code, encryption at rest, network segmentation) all fit B\u2019s description, and B adds no distinct skill area beyond the same guardrail concept. The cross-role difference is only organizational emphasis, not a different skill cluster.",
"similarity": 0.8845228342290689
}
],
"placed": {
"name": "Security",
"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": "security"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"policy-enforcement",
"endpoint-hardening",
"encryption-at-rest",
"rbac",
"acls",
"azure-rbac",
"certificate-pinning",
"jwt",
"saml",
"security-groups",
"private-endpoints",
"service-principals"
],
"requires": [],
"skill_id": "security",
"suppress_on_match": []
},
"skill_id": "security",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.96,
"name": "Security",
"reasoning": "Security is best treated as a Domain because it denotes a broad vertical/body of knowledge rather than a specific tool, protocol, or methodology.",
"skill_id": "security",
"subtype": "cybersecurity",
"type": "Domain"
},
"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": "Storage Systems",
"id": null,
"rationale": "Covers storage as a computing capability, including how data is persisted, organized, and accessed through block, file, and object interfaces. This skill belongs here when it refers to storage concepts or services rather than physical hardware internals.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Storage",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Storage",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Datastore",
"skill_nature": "TOOL",
"sub_category": "storage_system",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"cloud_storage",
"data_storage",
"storage_area_network",
"object_storage"
],
"reasoning": "\"Storage\" is very broad in JDs and can refer to generic data storage, cloud/object storage, or SAN infrastructure rather than a specific skill."
},
"context_keywords": {
"context_keywords": [
"SAN",
"NAS",
"object storage",
"block storage",
"file storage",
"RAID",
"LUN",
"iSCSI",
"Fibre Channel",
"snapshot",
"replication",
"deduplication",
"tiering",
"backup",
"disaster recovery"
]
},
"maturity": {
"confidence": 0.96,
"maturity": "well_known",
"reasoning": "Storage systems are a core hiring staple across cloud, backend, and DevOps JDs; major vendors (AWS S3, Azure Blob, GCS) and widespread use in production architectures signal broad adoption."
},
"skill_id": "storage",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [
{
"a_dim_id": "d_init_01",
"a_name": "Storage Systems",
"a_role": "__skill_focal__",
"b_dim_id": "storage-hardware-and-firmware",
"b_name": "Storage Hardware and Firmware",
"b_role": "Storage Engineer",
"pair_kind": "cross_role",
"reasoning": "Dim A is the logical storage-service layer: it covers persistence and access via block/file/object interfaces, with exemplars like block storage, file storage, object storage, and volume management; it even excludes physical disk firmware and RAID controller hardware. Dim B is the physical/firmware layer: storage systems hardware, controllers, disks, shelves, and firmware for reliability and lifecycle management. Same word, different cluster.",
"similarity": 0.6839872415072968
}
],
"locked_dimensions": [
{
"description": "Covers storage as a computing capability, including how data is persisted, organized, and accessed through block, file, and object interfaces. This skill belongs here when it refers to storage concepts or services rather than physical hardware internals.",
"exemplar_skills": [
"Storage",
"block storage",
"file storage",
"object storage",
"volume management"
],
"in_scope": "Storage, block storage, file storage, object storage, volumes, mounts, buckets, persistence, retention, replication",
"name": "Storage Systems",
"out_of_scope": "Backup scheduling and restore testing, physical disk firmware, RAID controller hardware, database schema design, network packet routing",
"overlap_flags": [
{
"reason": "Physical storage platforms and firmware are adjacent, but this dimension focuses on logical storage services and access models.",
"with_dim_id": "storage-hardware-and-firmware",
"with_dim_name": null,
"with_role": "Storage Engineer"
},
{
"reason": "Backups rely on storage, but backup creation and restore execution are operational recovery skills rather than storage architecture.",
"with_dim_id": "backup-and-restore-operations",
"with_dim_name": null,
"with_role": "Storage Engineer"
},
{
"reason": "Storage replication and durability contribute to resilience, but broader availability and capacity engineering sits in the resilience dimension.",
"with_dim_id": "scaling-and-resilience-engineering",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Storage",
"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": "storage"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"sqlite",
"aws-s3",
"azure-backup",
"ebs-snapshots",
"thin-provisioning",
"snapshot-retention",
"retention-policies",
"encryption-at-rest",
"capacity-planning",
"capacity-forecasting",
"reserved-instances",
"rollback-readiness",
"dependency-mapping",
"retention-metric"
],
"requires": [],
"skill_id": "storage",
"suppress_on_match": []
},
"skill_id": "storage",
"split_log": [],
"typed": {
"alternatives_considered": [
"Platform: ruled out \u2014 it is not necessarily a hosted multi-tenant environment with APIs.",
"Tool: ruled out \u2014 the term names a capability/system category rather than software a user operates directly.",
"Concept: ruled out \u2014 this is more concrete than a general knowledge unit."
],
"confidence": 0.62,
"name": "Storage",
"reasoning": "By the Datastore vs Format rule, \"Storage\" most fundamentally refers to a system that persists data rather than a format or protocol, though the term is broad and context-dependent.",
"skill_id": "storage",
"subtype": "storage_system",
"type": "Datastore"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Systems Engineering",
"id": null,
"rationale": "Covers understanding and designing how software, infrastructure, and operational components work together as a whole. The skill \"Systems\" belongs here when it refers to broad system behavior, dependencies, reliability, and tradeoffs across cloud environments.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Systems",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Systems",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "systems_engineering",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"operating_systems",
"distributed_systems",
"embedded_systems"
],
"reasoning": "\u201cSystems\u201d is very broad in JDs and can refer to operating systems, distributed systems, or embedded systems rather than the general domain skill."
},
"context_keywords": {
"context_keywords": [
"requirements engineering",
"MBSE",
"SysML",
"architecture tradeoff",
"verification and validation",
"interface control",
"traceability matrix",
"lifecycle management",
"conops",
"FMEA",
"hazard analysis",
"integration testing",
"configuration management",
"technical baseline",
"stakeholder analysis"
]
},
"maturity": {
"confidence": 0.88,
"maturity": "well_known",
"reasoning": "Systems engineering is a common hiring requirement in aerospace, defense, automotive, and infrastructure JDs, and is a standard discipline in INCOSE/IEEE-aligned job postings."
},
"skill_id": "systems",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers understanding and designing how software, infrastructure, and operational components work together as a whole. The skill \"Systems\" belongs here when it refers to broad system behavior, dependencies, reliability, and tradeoffs across cloud environments.",
"exemplar_skills": [
"Systems",
"systems thinking",
"distributed systems",
"fault tolerance",
"high availability",
"capacity planning",
"performance analysis"
],
"in_scope": "Systems, system behavior, distributed components, reliability tradeoffs, dependency interactions, capacity planning, fault tolerance, high availability, performance bottlenecks",
"name": "Systems Engineering",
"out_of_scope": "Container image build workflows, API contract design, database administration, packet-level network analysis, and platform scheduling details, which belong to more specific engineering dimensions",
"overlap_flags": [
{
"reason": "Both involve understanding how services interact, but this dimension is broader and less focused on API and integration patterns.",
"with_dim_id": "service-architecture-and-integration",
"with_dim_name": null,
"with_role": "Backend Engineer"
},
{
"reason": "Reliability and capacity topics overlap, but that catalog dimension is more specifically about scaling and fault-tolerance implementation.",
"with_dim_id": "scaling-and-resilience-engineering",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Systems",
"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": "systems"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"capacity-planning",
"runbooks",
"dashboards",
"policy-as-code",
"cspm",
"exception-management"
],
"requires": [],
"skill_id": "systems",
"suppress_on_match": []
},
"skill_id": "systems",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Systems",
"reasoning": "Systems is best treated as a Domain because it names a broad problem-space/body of knowledge rather than a specific architecture, concept, tool, or methodology.",
"skill_id": "systems",
"subtype": "systems_engineering",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "WNS Platform Operations",
"id": null,
"rationale": "Covers operational work specific to WNS-managed enterprise platforms and services, including setup, support, monitoring, and issue resolution. This skill belongs here when WNS refers to the platform or service environment being administered rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "WNS",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "WNS",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Domain",
"skill_nature": "CONCEPT",
"sub_category": "business_process_outsourcing",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"wipro",
"tcs",
"infosys"
],
"reasoning": "WNS is a company name and BPO vendor, so a JD mention could be mistaken for another outsourcing firm or a generic services reference in the same catalog."
},
"context_keywords": {
"context_keywords": [
"BPO",
"KPO",
"shared services",
"customer support",
"finance and accounting",
"accounts payable",
"accounts receivable",
"order to cash",
"procure to pay",
"collections",
"claims processing",
"back office",
"contact center",
"SLAs",
"process transition"
]
},
"maturity": {
"confidence": 0.86,
"maturity": "niche",
"reasoning": "WNS appears in BPO/outsourcing job postings and vendor listings, but JD volume is far below mainstream cloud or programming skills; it\u2019s a specialized services domain rather than a broadly requested engineering stack."
},
"skill_id": "wns",
"vendor_license": {
"confidence": 0.97,
"license": null,
"vendor": "WNS Global Services",
"year_introduced": 1996
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers operational work specific to WNS-managed enterprise platforms and services, including setup, support, monitoring, and issue resolution. This skill belongs here when WNS refers to the platform or service environment being administered rather than a generic cloud concept.",
"exemplar_skills": [
"WNS",
"WNS administration",
"WNS tenant setup",
"WNS incident triage",
"WNS operational support"
],
"in_scope": "WNS administration, WNS tenant setup, WNS service monitoring, WNS incident triage, WNS configuration changes, WNS access coordination, WNS operational support",
"name": "WNS Platform Operations",
"out_of_scope": "Cloud architecture design, generic Azure/AWS provisioning, application coding, security policy design, which belong to other cloud or platform dimensions",
"overlap_flags": [
{
"reason": "WNS work may include environment setup and promotion, but this dimension is broader and owns lifecycle promotion mechanics.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
},
{
"reason": "If WNS is being used as a managed platform instance, some setup tasks overlap with platform administration.",
"with_dim_id": "platform-administration-and-instance-setup",
"with_dim_name": null,
"with_role": "ServiceNOW Developer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "WNS",
"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": "wns"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "wns",
"suppress_on_match": []
},
"skill_id": "wns",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "WNS",
"reasoning": "WNS is fundamentally a business-services industry/domain rather than a software product, so it fits the Domain type.",
"skill_id": "wns",
"subtype": "business_process_outsourcing",
"type": "Domain"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Windows Operating System Administration",
"id": null,
"rationale": "Covers administering Microsoft Windows as an operating system, including desktop and server configuration, patching, services, users, and troubleshooting. This skill belongs here because it refers to the Windows platform itself rather than a narrower product or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Windows",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Windows",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "operating_system_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "SEPARATE_ENTITY",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"microsoft_windows",
"windows_server"
],
"reasoning": "\"Windows\" can mean the desktop OS, Windows Server, or generic window/UI references in JDs; a extractor could confuse it with other Windows-family platform skills."
},
"context_keywords": {
"context_keywords": [
"Active Directory",
"Group Policy",
"PowerShell",
"Registry",
"Event Viewer",
"IIS",
"Hyper-V",
"WSL",
"RDP",
"NTFS",
"Windows Server",
"SCCM",
"Intune",
"PowerShell Remoting"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Windows remains a broad hiring-pipeline staple across enterprise IT and desktop support; job postings routinely list Windows administration alongside Active Directory and Microsoft 365, indicating sustained market demand."
},
"skill_id": "windows",
"vendor_license": {
"confidence": 0.99,
"license": "proprietary",
"vendor": "Microsoft",
"year_introduced": 1985
},
"versioning": {
"current_version": "Windows 11",
"version_aliases": {
"Vista": "Windows Vista",
"Win10": "Windows 10",
"Win11": "Windows 11",
"Win7": "Windows 7",
"Win8": "Windows 8",
"Win8.1": "Windows 8.1",
"Windows 10": "Windows 10",
"Windows 11": "Windows 11",
"Windows 7": "Windows 7",
"Windows 8": "Windows 8",
"Windows 8.1": "Windows 8.1",
"Windows Vista": "Windows Vista",
"Windows XP": "Windows XP",
"XP": "Windows XP"
},
"versioned": true
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers administering Microsoft Windows as an operating system, including desktop and server configuration, patching, services, users, and troubleshooting. This skill belongs here because it refers to the Windows platform itself rather than a narrower product or cloud service.",
"exemplar_skills": [
"Windows",
"Windows Server administration",
"Group Policy",
"PowerShell",
"Windows event log troubleshooting",
"Windows patch management"
],
"in_scope": "Windows, Windows Server, Windows client administration, system settings, services, registry, Group Policy, PowerShell for OS tasks, patching, event logs, device drivers, local users and groups",
"name": "Windows Operating System Administration",
"out_of_scope": "Azure cloud services, Windows application development, Active Directory design, endpoint security policy, virtualization platforms, database administration",
"overlap_flags": [
{
"reason": "Windows hardening and baseline security settings can overlap with cloud security controls when Windows hosts run in cloud environments.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
},
{
"reason": "Windows image setup and machine provisioning may intersect with environment creation workflows, but this dimension owns the OS administration details.",
"with_dim_id": "environment-provisioning-and-promotion",
"with_dim_name": null,
"with_role": "DevOps Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Windows",
"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": "windows"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"azure-virtual-machines",
"powershell",
"bash"
],
"requires": [],
"skill_id": "windows",
"suppress_on_match": []
},
"skill_id": "windows",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Windows",
"reasoning": "By the Platform vs Tool rule, Windows is best treated as a hosted operating environment with APIs and managed services rather than software you merely run, so Platform fits better than Tool.",
"skill_id": "windows",
"subtype": "operating_system_platform",
"type": "Platform"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "application",
"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": "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": "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": "ops",
"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": "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": "productivity",
"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": "resources",
"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": "scripts",
"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": "services",
"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": "solutions",
"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": "tool",
"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": "versioning",
"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": "web",
"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": [
"AI",
"APIM",
"APIs",
"Authentication",
"CD",
"CI",
"CICD",
"Cloud",
"Containers",
"Develop",
"Devops",
"Engineer",
"Engineering",
"IaC",
"Infrastructure",
"NIM",
"Nemo",
"Networking",
"Pipeline",
"Platform",
"Security",
"Storage",
"Systems",
"WNS",
"Windows"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "DevOps Engineer",
"id": 1,
"rationale": "DevOps Engineer is the clearest fit across the strongest dimensions: CI/CD, deployment automation, containerization, cloud platform operations, and orchestration.",
"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": "JUnit",
"tag": "in_db"
},
{
"skill": "Jenkins",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Kubernetes",
"tag": "in_db"
},
{
"skill": "PowerShell",
"tag": "in_db"
},
{
"skill": "REST APIs",
"tag": "in_db"
},
{
"skill": "Docker",
"tag": "in_db"
},
{
"skill": "automation",
"tag": "in_db"
},
{
"skill": "alerting",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "Bash scripting",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "AI",
"tag": "new"
},
{
"skill": "APIM",
"tag": "new"
},
{
"skill": "APIs",
"tag": "new"
},
{
"skill": "Authentication",
"tag": "new"
},
{
"skill": "CD",
"tag": "new"
},
{
"skill": "CI",
"tag": "new"
},
{
"skill": "CICD",
"tag": "new"
},
{
"skill": "Cloud",
"tag": "new"
},
{
"skill": "Containers",
"tag": "new"
},
{
"skill": "Develop",
"tag": "new"
},
{
"skill": "Devops",
"tag": "new"
},
{
"skill": "Engineer",
"tag": "new"
},
{
"skill": "Engineering",
"tag": "new"
},
{
"skill": "IaC",
"tag": "new"
},
{
"skill": "Infrastructure",
"tag": "new"
},
{
"skill": "NIM",
"tag": "new"
},
{
"skill": "Nemo",
"tag": "new"
},
{
"skill": "Networking",
"tag": "new"
},
{
"skill": "Pipeline",
"tag": "new"
},
{
"skill": "Platform",
"tag": "new"
},
{
"skill": "Security",
"tag": "new"
},
{
"skill": "Storage",
"tag": "new"
},
{
"skill": "Systems",
"tag": "new"
},
{
"skill": "WNS",
"tag": "new"
},
{
"skill": "Windows",
"tag": "new"
},
{
"skill": "application",
"tag": "new"
},
{
"skill": "applications",
"tag": "new"
},
{
"skill": "deployment",
"tag": "new"
},
{
"skill": "development",
"tag": "new"
},
{
"skill": "monitoring",
"tag": "new"
},
{
"skill": "ops",
"tag": "new"
},
{
"skill": "orchestration",
"tag": "new"
},
{
"skill": "pipelines",
"tag": "new"
},
{
"skill": "productivity",
"tag": "new"
},
{
"skill": "resources",
"tag": "new"
},
{
"skill": "scripts",
"tag": "new"
},
{
"skill": "services",
"tag": "new"
},
{
"skill": "solutions",
"tag": "new"
},
{
"skill": "tool",
"tag": "new"
},
{
"skill": "versioning",
"tag": "new"
},
{
"skill": "web",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Authentication Flows and Session Handling",
"id": 10,
"rationale": "Client-side implementation of sign-in, sign-out, session persistence, and protected navigation. This is separated from general API integration because auth flows have distinct UX and state concerns.",
"slug": "authentication-flows-and-session-handling",
"source": "db"
},
"dimension_id": 10,
"input_skill": "JUnit",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Android Engineer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 882,
"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": "Test Frameworks and Runners",
"id": 194,
"rationale": "Frameworks and execution tools used to author, organize, and run automated tests. This cluster is coherent because it defines how test cases are structured, parameterized, and reported.",
"slug": "test-frameworks-and-runners",
"source": "db"
},
"dimension_id": 194,
"input_skill": "JUnit",
"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": 882,
"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": "Testing and Quality Assurance",
"id": 150,
"rationale": "Automated testing approaches used to verify backend behavior across units, integrations, contracts, and end-to-end flows. This cluster is coherent because backend engineers need confidence in request handling, data changes, and service interactions.",
"slug": "testing-and-quality-assurance",
"source": "db"
},
"dimension_id": 150,
"input_skill": "JUnit",
"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": 882,
"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": "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": "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",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 158,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"dimension_id": 31,
"input_skill": "PowerShell",
"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": 189,
"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": "API Integration and Data Fetching",
"id": 9,
"rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
"slug": "api-integration-and-data-fetching",
"source": "db"
},
"dimension_id": 9,
"input_skill": "REST APIs",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 49,
"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": "API Integration and Serialization",
"id": 128,
"rationale": "Client-side integration with backend services, including request handling, response parsing, and contract alignment. This cluster is coherent because iOS features frequently depend on stable data exchange with server APIs.",
"slug": "api-integration-and-serialization",
"source": "db"
},
"dimension_id": 128,
"input_skill": "REST APIs",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "iOS Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ios-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 49,
"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": "REST APIs",
"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": 49,
"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": "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": "automation",
"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": 553,
"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 Incident Triage",
"id": 61,
"rationale": "Using logs, metrics, traces, and model-specific signals to investigate failures in production model systems. This is a coherent cluster because MLOps must diagnose both infrastructure symptoms and model behavior regressions.",
"slug": "observability-and-incident-triage",
"source": "db"
},
"dimension_id": 61,
"input_skill": "alerting",
"llm_role": null,
"matched_chosen_role": 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": 665,
"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": "Deployment Automation Scripts",
"id": 31,
"rationale": "Implements the scripts and command-line automation used to execute deployments and environment changes. This cluster covers the practical glue code that pipelines and operators rely on.",
"slug": "deployment-automation-scripts",
"source": "db"
},
"dimension_id": 31,
"input_skill": "Bash scripting",
"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": 188,
"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": "MySQL Automation and Scripting",
"id": 176,
"rationale": "Covers scripts and operational automation used to run repeatable DBA tasks. This cluster is coherent because production database administration depends on safe, repeatable execution of checks, maintenance, and change steps.",
"slug": "mysql-automation-and-scripting",
"source": "db"
},
"dimension_id": 176,
"input_skill": "Bash scripting",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 188,
"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": "API Management",
"id": null,
"rationale": "Covers managing API gateways and the lifecycle controls around publishing, securing, throttling, and observing APIs. APIM belongs here because it is commonly shorthand for API Management platforms and practices.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "APIM",
"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": "API Design and Integration",
"id": null,
"rationale": "Covers designing, consuming, and integrating application programming interfaces across services and clients. APIs belong here because they define the contract, transport, and interaction patterns used by systems to communicate.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "APIs",
"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": "Authentication",
"id": null,
"rationale": "Covers mechanisms for verifying identity and establishing trusted access to systems and services. This skill belongs here when the focus is on proving who a user or service is, rather than what they are allowed to do after login.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Authentication",
"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": "Continuous Delivery Practices",
"id": null,
"rationale": "Covers the software delivery discipline of automating build, test, and release steps so changes can move safely to production. CD belongs here when it refers to the delivery methodology rather than a specific environment-management task.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "CD",
"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": "Continuous Integration",
"id": null,
"rationale": "Practices for automatically building, validating, and merging code changes through shared pipelines. CI belongs here because it is the core delivery discipline for integrating changes early and catching regressions before release.",
"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": "Continuous Integration and Delivery",
"id": null,
"rationale": "Automates code integration, testing, packaging, and release promotion from commit to deployment. CICD belongs here because it is the core delivery pipeline practice for building, validating, and shipping software changes reliably.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "CICD",
"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 and the use of public cloud services to build, run, and manage systems. This fits the target skill because \"Cloud\" usually refers to working with cloud provider capabilities, deployment models, and managed services rather than a narrower sub-discipline.",
"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": "Containerization",
"id": null,
"rationale": "Covers packaging and running software in isolated containers, including the core concepts and tooling used to create and manage container images and runtimes. Containers belongs here because it is the foundational abstraction for container-based deployment and execution.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Containers",
"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 Application Development",
"id": null,
"rationale": "Building application logic, services, and integrations that run in cloud environments. The skill \u0027Develop\u0027 fits here when it refers to writing the code that implements cloud-native features and service behavior.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Develop",
"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": "Delivery Pipeline Automation",
"id": null,
"rationale": "Covers automating build, test, release, and deployment workflows that move software from source control to running environments. DevOps belongs here when it refers to the operational practice of continuous integration and continuous delivery.",
"slug": "d_init_02",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Devops",
"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": "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": "Devops",
"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": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": 1,
"dimension": {
"difficulty_hint": null,
"display_name": "Cloud Engineering",
"id": null,
"rationale": "Builds and operates cloud-based infrastructure, services, and deployment foundations. This is the best fit for the broad role label \"Engineer\" with a Cloud Engineer hint, since it typically spans provisioning, networking, reliability, and platform operations.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Engineer",
"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 Engineering Practice",
"id": null,
"rationale": "Broad engineering work that spans designing, building, and maintaining technical systems when no narrower specialty is implied. This skill is too generic to map cleanly to a more specific catalog dimension, so it serves as the umbrella for general implementation and problem-solving work.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Engineering",
"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 as Code Practices",
"id": null,
"rationale": "Covers authoring and maintaining declarative infrastructure definitions used to provision and manage cloud resources. IaC belongs here because it is the core practice for codifying infrastructure instead of configuring it manually.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "IaC",
"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 Architecture",
"id": null,
"rationale": "Covers the foundational cloud and systems layer that supports applications, services, and operations. Use this when Infrastructure is meant broadly as the underlying compute, storage, networking, and platform foundation rather than a narrower operational specialty.",
"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": "NIM Deployment and Serving",
"id": null,
"rationale": "Covers deploying, configuring, and operating NVIDIA NIM services for model inference in cloud environments. This fits the target skill because NIM is primarily about packaging and serving AI models through a managed runtime and API surface.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "NIM",
"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": "Nemo Platform Operations",
"id": null,
"rationale": "Operational use of the Nemo platform for running, configuring, and troubleshooting cloud workloads. This fits the target skill because Nemo is treated here as a platform-specific operational competency rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Nemo",
"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": "Platform Administration",
"id": null,
"rationale": "Covers administering the underlying platform layer used to configure, operate, and support environments and instances. The skill \"Platform\" fits here when it refers to the operational platform itself rather than a specific app feature or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Platform",
"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": "Storage Systems",
"id": null,
"rationale": "Covers storage as a computing capability, including how data is persisted, organized, and accessed through block, file, and object interfaces. This skill belongs here when it refers to storage concepts or services rather than physical hardware internals.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Storage",
"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": "Systems Engineering",
"id": null,
"rationale": "Covers understanding and designing how software, infrastructure, and operational components work together as a whole. The skill \"Systems\" belongs here when it refers to broad system behavior, dependencies, reliability, and tradeoffs across cloud environments.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Systems",
"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": "WNS Platform Operations",
"id": null,
"rationale": "Covers operational work specific to WNS-managed enterprise platforms and services, including setup, support, monitoring, and issue resolution. This skill belongs here when WNS refers to the platform or service environment being administered rather than a generic cloud concept.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "WNS",
"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": "Windows Operating System Administration",
"id": null,
"rationale": "Covers administering Microsoft Windows as an operating system, including desktop and server configuration, patching, services, users, and troubleshooting. This skill belongs here because it refers to the Windows platform itself rather than a narrower product or cloud service.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Windows",
"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": 48
},
"planner_output": null,
"run_id": "bab35f46-92ce-4790-bb73-d35c8ee4d31b"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.
Loading…