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Pipeline run

21b88bfe-fda2-4503-b896-a6790db3380b

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work
runtime_error
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.50 / 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): AI, ML
Evidence — skills matched in JD (22)
Java JavaScript Python AWS GCP Microsoft Azure C/C++ C# Node.js SQL Elasticsearch MongoDB Cassandra Kubernetes Docker LXC/LXD Agile CI/CD REST Microservices ML AI
Skill cluster (0 dimension groups, role-scoped)
No dimension groups computed for this JD.
Status: completed Created: 2026-05-12T07:32:37.127495Z Updated: 2026-05-12T07:34:29.237504Z API 3 duration: 5461 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

Backend Engineer

slug: backend-engineer · id: 14 · source: db

The primary skills Java, JavaScript, and Python align closely with the responsibilities of a Backend Engineer.

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

6
New skills
7
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

About the job
Make an impact with NTT DATA

Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.

Your day at NTT DATA

The Senior Associate Software Development Engineer is a developing subject matter expert, tasked with supporting the designing, developing, and testing of software systems, modules, or applications for software enhancements and new products including cloud-based or internet-related tools.

This role is accountable for supporting detailed design for certain modules/sub-systems, doing prototype for multi-vendor infrastructure, and showcasing it internally or externally to clients.

This role designs and develops functionality in a micro-services environment working with APIs, telemetry data, and running ML/AI algorithms on it, working with both structured and unstructured data.

Key responsibilities:

Receives instructions to design and develop solutions and functionality that drives the growth of business.
Contributes to writing and testing code.
Supports the execution of automated testing.
Receives instructions from various stakeholders to participate in software deployment.
Supports the delivery of software components while working in collaboration with the product team.
Supports the integration and building of solutions through automation and coding, using 3rd party software.
Receives instructions to craft, build, and debug large scale distributed systems.
Supports writing, updating and maintaining the technical program, end-user documentation, and operational procedures.
Assists with refactoring code.
Contributes to the reviewing of code written by other developers.
Performs any other related task as required.

To thrive in this role, you need to have:

Developing understanding of cloud architecture and services in multiple public clouds like AWS, GCP, Microsoft Azure, and Microsoft Office 365.
Subject matter expert in programming languages such as C/C++, C#, Java, JavaScript, Python, Node.js, libraries and frameworks.
Developing expertise of data structures, algorithms, and software design with strong analytical and debugging skills.
Developing knowledge of micro services-based software architecture and experience with API product development.
Developing expertise in SQL and no-SQL data stores including Elasticsearch, MongoDB, Cassandra.
Developing understanding of container run time (Kubernetes, Docker, LXC/LXD).
Developing proficiency with agile, lean practices and believes in test-driven development.
Possess a can-do attitude and one that takes initiative.
Excellent ability to work well in a diverse team with different backgrounds and experience levels.
Excellent ability to thrive in a dynamic, fast-paced environment.
Developing proficiency with CI/CD concepts and tools.
Developing proficiency with cloud-based infrastructure and deployments.
Excellent attention to detail.

Academic qualifications and certifications:

Bachelor's degree or equivalent in Computer Science, Engineering or a related field.
Microsoft Certified Azure Fundamentals preferred.
Relevant agile certifications preferred.

Required experience:

Moderate level experience working with geo-distributed teams through innovation, bootstrapping, pilot, and production phases with multiple stakeholders to the highest levels of quality and performance.
Moderate level experience with tools across full software delivery lifecycle, for example. IDE, source control, CI, test, mocking, work tracking, defect management.
Moderate level experience in Agile and Lean methodologies, Continuous Delivery / DevOps, Analytics / data-driven processes.
Familiarity with working with large data sets and ability to apply proper ML/AI algorithms.
Moderate level experience in developing micro-services and RESTful APIs.
Moderate level experience in software development.

Workplace type: 

On-site Working

About NTT DATA

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.

Equal Opportunity Employer

NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category. Join our growing global team and accelerate your career with us. Apply today.

Third parties fraudulently posing as NTT DATA recruiters

NTT DATA recruiters will never ask job seekers or candidates for payment or banking information during the recruitment process, for any reason. Please remain vigilant of third parties who may attempt to impersonate NTT DATA recruiters—whether in writing or by phone—in order to deceptively obtain personal data or money from you. All email communications from an NTT DATA recruiter will come from an @nttdata.com email address. If you suspect any fraudulent activity, please contact us.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

AWS Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=163 · aws

Aliases — catalog

  • Compaction (CANONICAL) primary

Context tags (catalog)

Bloom filter LSM tree SSTable checkpointing defragmentation garbage collection leveling log-structured merge policy segment merge storage engine tiered compaction tombstones vacuum write amplification

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Storage Maintenance Concept
Confidence
0.74
Version strategy
NOT_APPLICABLE

Maturity reasoning: Compaction is a standard storage-maintenance concept in widely used systems like LSM databases and Kafka; it appears in many JDs for Cassandra, RocksDB, and Kafka ops roles, indicating broad market demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
161
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platform Operations Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Cloud Security Platforms Catalog dimension db id 332

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platform Operations
cloud-platform-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GCP id=2304 · gcp

Aliases — catalog

  • ASGI (CANONICAL) primary

Context tags (catalog)

ASGI app ASGI server Django Channels FastAPI HTTP/2 Starlette WebSocket application scope asyncio background tasks concurrency event loop lifespan middleware routing

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Web Application Protocol
Vendor
Django Software Foundation
License
bsd
Year introduced
2016
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: ASGI appears in many Python web JDs for async frameworks like FastAPI/Starlette, but WSGI remains the broader default in legacy stacks; market signal shows growing adoption rather than universal demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
161
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Platforms Catalog dimension db id 332

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.98

Broadly listed in cloud/DevOps job descriptions across enterprises; Microsoft reports Azure as a core hyperscale cloud alongside AWS, with strong hiring demand and ecosystem adoption.

Vendor & license

Microsoft ·proprietary ·since 2010 (0.99)

Context keywords
Azure DevOps ARM templates Bicep AKS App Service Azure Functions Azure Active Directory Key Vault Virtual Machines Storage Accounts Azure Monitor Log Analytics Azure SQL Service Bus Terraform
Ambiguity low

Microsoft Azure is a specific cloud platform name and is typically unambiguous in JDs; it is not commonly mistaken for another catalog skill.

Versioning

Not versioned

Type assignment

Platform ·cloud_platform confidence 0.99

By the Platform vs Tool rule, Microsoft Azure is a hosted multi-tenant environment with APIs and managed services, so it is a Platform rather than a Tool.

Derived legacy fields
Category
Platform
Sub-category
cloud_platform
Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Microsoft Azure Cloud Platform

    Pipeline tentative id

    Covers the Microsoft Azure cloud platform itself: core services, resource organization, and the operational features used to build and run workloads. Microsoft Azure belongs here because it names the platform rather than a single tool or sub-service.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C/C++ Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.98

C/C++ remains a staple in job postings for systems, embedded, game, and performance-critical roles, with broad hiring demand across industries and large GitHub/OSS usage.

Vendor & license

ISO/IEC ·since 1972 (0.88)

Context keywords
pointers memory management RAII STL templates multithreading concurrency embedded systems firmware Linux POSIX CMake gdb valgrind boost
Ambiguity flagged

Could be confused with: c, cpp

The combined label C/C++ can be split or normalized to either C or C++, and JDs often mention one specifically. A parser could reasonably map it to the separate catalog skills for C or C++.

Versioning

Versioned C++23

{
  "C": "C",
  "C++03": "C++03",
  "C++0x": "C++11",
  "C++11": "C++11",
  "C++14": "C++14",
  "C++17": "C++17",
  "C++1y": "C++14",
  "C++1z": "C++17",
  "C++20": "C++20",
  "C++23": "C++23",
  "C++26": "C++26",
  "C++2a": "C++20",
  "C++2b": "C++23",
  "C++2c": "C++26",
  "C++98": "C++98",
  "C11": "C11",
  "C17": "C17",
  "C23": "C23",
  "C89": "C89",
  "C90": "C90",
  "C99": "C99"
}
Type assignment

Language ·systems_programming_language confidence 0.99

C/C++ is a programming language family, and the Language type applies because it is used to write code directly rather than as a library, framework, tool, or runtime.

Derived legacy fields
Category
Language
Sub-category
systems_programming_language
Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Systems Programming Languages

    Pipeline tentative id

    Programming in low-level, compiled languages used for performance-sensitive and systems-level software. C/C++ fits here because it is commonly used for memory control, concurrency, hardware-adjacent code, and native libraries.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: C# id=680 · c

Aliases — catalog

  • submission state (CANONICAL) primary

Context tags (catalog)

approval workflow approved audit trail draft finalize pending review rejected resubmission save as draft state machine status transition submitted validation errors withdrawn workflow state

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Form State Concept
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common form-state concept in web/app JDs and docs; widely implemented in React Hook Form, Formik, and backend validation flows, with no sunset or replacement signal.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
54
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for Backend Systems Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • Programming Languages for ML Systems Catalog dimension db id 113

    Library dimension (catalog)

    Roles linked in library: Machine Learning Engineer

  • Programming Languages for Test Automation Catalog dimension db id 193

    Library dimension (catalog)

    Roles linked in library: Automation Tester

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Java id=395 · java

Aliases — catalog

  • sqlmap (CANONICAL) primary

Context tags (catalog)

DBMS fingerprinting HTTP request SQL injection UNION-based WAF bypass blind SQLi boolean-based cookie injection enumeration error-based parameter tampering payloads tamper scripts time-based web application security

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Sql Injection Testing Tool
Vendor
sqlmap project
License
gpl_v2
Year introduced
2006
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: sqlmap appears in pentest/security JDs far less than mainstream dev tools; GitHub shows steady but specialized use, and it’s a focused SQL injection testing utility rather than a general-purpose platform.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
54
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for AI Workflows Catalog dimension db id 261

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Programming Languages for Backend Systems Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • Programming Languages for Data Work Catalog dimension db id 67

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 113

    Library dimension (catalog)

    Roles linked in library: Machine Learning Engineer

  • Programming Languages for Test Automation Catalog dimension db id 193

    Library dimension (catalog)

    Roles linked in library: Automation Tester

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: JavaScript id=1 · javascript

Aliases — catalog

  • Java (CANONICAL) primary
  • JDK (VERSION)
  • JDK 10 (VERSION)
  • JDK 11 (VERSION)
  • JDK 12 (VERSION)
  • JDK 13 (VERSION)
  • JDK 14 (VERSION)
  • JDK 15 (VERSION)
  • JDK 16 (VERSION)
  • JDK 17 (VERSION)
  • JDK 18 (VERSION)
  • JDK 19 (VERSION)
  • JDK 20 (VERSION)
  • JDK 21 (VERSION)
  • JDK 5 (VERSION)
  • JDK 6 (VERSION)
  • JDK 7 (VERSION)
  • JDK 8 (VERSION)
  • JDK 9 (VERSION)
  • Java 1.0 (VERSION)
  • Java 1.1 (VERSION)
  • Java 1.2 (VERSION)
  • Java 1.3 (VERSION)
  • Java 1.4 (VERSION)
  • Java 1.5 (VERSION)
  • Java 1.6 (VERSION)
  • Java 1.7 (VERSION)
  • Java 1.8 (VERSION)
  • Java 10 (VERSION)
  • Java 11 (VERSION)
  • Java 12 (VERSION)
  • Java 13 (VERSION)
  • Java 14 (VERSION)
  • Java 15 (VERSION)
  • Java 16 (VERSION)
  • Java 17 (VERSION)
  • Java 18 (VERSION)
  • Java 19 (VERSION)
  • Java 20 (VERSION)
  • Java 21 (VERSION)
  • Java 5 (VERSION)
  • Java 6 (VERSION)
  • Java 7 (VERSION)
  • Java 8 (VERSION)
  • Java 9 (VERSION)
  • Java11 (VERSION)
  • Java17 (VERSION)
  • Java21 (VERSION)
  • Java8 (VERSION)
  • OpenJDK 11 (VERSION)
  • OpenJDK 17 (VERSION)
  • OpenJDK 21 (VERSION)
  • OpenJDK 8 (VERSION)
  • java 11 (VERSION)
  • java 17 (VERSION)
  • java 21 (VERSION)
  • java 4 (VERSION)
  • java 5 (VERSION)
  • java 6 (VERSION)
  • java 7 (VERSION)
  • java 8 (VERSION)
  • java lts (VERSION)
  • java-11 (VERSION)
  • java-17 (VERSION)
  • java-21 (VERSION)
  • java-4 (VERSION)
  • java-5 (VERSION)
  • java-6 (VERSION)
  • java-7 (VERSION)
  • java-8 (VERSION)
  • java11 (VERSION)
  • java17 (VERSION)
  • java21 (VERSION)
  • java4 (VERSION)
  • java5 (VERSION)
  • java6 (VERSION)
  • java7 (VERSION)
  • java8 (VERSION)
  • jdk 11 (VERSION)
  • jdk 17 (VERSION)
  • jdk 21 (VERSION)
  • jdk 4 (VERSION)
  • jdk 5 (VERSION)
  • jdk 6 (VERSION)
  • jdk 7 (VERSION)
  • jdk 8 (VERSION)
  • jdk11 (VERSION)
  • jdk17 (VERSION)
  • jdk21 (VERSION)
  • jdk4 (VERSION)
  • jdk5 (VERSION)
  • jdk6 (VERSION)
  • jdk7 (VERSION)
  • jdk8 (VERSION)
  • jvm21 (VERSION)

Context tags (catalog)

APIs Apache Tomcat Concurrency Design patterns Garbage collection GraalVM Gradle Hibernate JDBC JDK JPA JUnit JVM Java 8 Java EE JavaFX Kafka Lambda expressions Maven Microservices Mockito Object-oriented REST RESTful SOAP Servlets Spring Spring Boot Tomcat microservices

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Oracle
License
other_open
Year introduced
1995
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
21

Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
54
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Frontend Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Developer

  • Programming Languages for AI Workflows Catalog dimension db id 261

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Programming Languages for Security Work Catalog dimension db id 328

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

  • Programming Languages for Test Automation Catalog dimension db id 193

    Library dimension (catalog)

    Roles linked in library: Automation Tester

  • ServiceNow Scripting and Logic Catalog dimension db id 210

    Library dimension (catalog)

    Roles linked in library: ServiceNOW Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Frontend Programming Languages
frontend-programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
ServiceNow Scripting and Logic
servicenow-scripting-and-logic
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=393 · python

Aliases — catalog

  • Cobalt Strike (CANONICAL) primary

Context tags (catalog)

Malleable C2 beacon credential dumping kerberos lateral movement payload phishing post-exploitation privilege escalation psexec red team sleep mask smb stager team server

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Adversary Simulation Tool
Vendor
Fortra
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in a limited set of red-team/pentest JDs and security vendor training, but far below mainstream devops tools; market signal is specialized adversary-simulation usage rather than broad hiring demand.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
54
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Analytical Programming Languages Catalog dimension db id 82

    Library dimension (catalog)

    Roles linked in library: Data Analyst, Data Scientist

  • Automation Scripting and CLI Catalog dimension db id 48

    Library dimension (catalog)

    Roles linked in library: Azure Cloud Engineer, Cloud Engineer

  • Automation and Scripting for Operations Catalog dimension db id 361

    Library dimension (catalog)

    Roles linked in library: Virtualization Engineer

  • Network Automation and Scripting Catalog dimension db id 285

    Library dimension (catalog)

    Roles linked in library: Network Engineer

  • Programming Languages for AI Workflows Catalog dimension db id 261

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Programming Languages for Backend Systems Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • Programming Languages for Data Work Catalog dimension db id 67

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 113

    Library dimension (catalog)

    Roles linked in library: Machine Learning Engineer

  • Programming Languages for Security Work Catalog dimension db id 328

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

  • Programming Languages for Test Automation Catalog dimension db id 193

    Library dimension (catalog)

    Roles linked in library: Automation Tester

  • Security Automation and Scripting Catalog dimension db id 258

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Analytical Programming Languages
analytical-programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Automation Scripting and CLI
automation-scripting-and-cli
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Automation and Scripting for Operations
automation-and-scripting-for-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Network Automation and Scripting
network-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Security Automation and Scripting
security-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Node.js Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Node.js id=2599 · node-js

Aliases — catalog

  • hooks composition (CANONICAL) primary

Context tags (catalog)

React component lifecycle composition pattern context API custom hooks dependency arrays functional components higher-order components memoization performance optimization react-query render props state management useEffect useState

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Hooks Composition
Confidence
0.92
Version strategy
NOT_APPLICABLE

Maturity reasoning: React job postings commonly require hooks and custom hook composition; the pattern is standard in modern React codebases and docs, with broad ecosystem adoption rather than a niche tool.

Skill profile (library / DB)

Skill nature
RUNTIME
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
271
Sub-category id
2120
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for Backend Systems Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
SQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=2601 · sql

Aliases — from this run (catalog unavailable)

  • SQL (CANONICAL)

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
55
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 71

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Data Engineer

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension saved
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.96

Commonly listed in job descriptions for search/log analytics and supported by Elastic’s broad ecosystem; no vendor sunset, and it remains a standard production search engine alongside OpenSearch.

Vendor & license

Elastic ·apache_2 ·since 2010 (0.98)

Context keywords
Kibana Logstash Beats Lucene index mapping shards replicas full-text search aggregations query DSL inverted index Elasticsearch cluster Elasticsearch index Elasticsearch API Elasticsearch ingest
Ambiguity low

Elasticsearch is a well-known, specific search engine/datastore name and is unlikely to be mistaken for another catalog skill in typical job descriptions.

Versioning

Versioned 8.x

{
  "Elasticsearch 5": "5.x",
  "Elasticsearch 5.x": "5.x",
  "Elasticsearch 6": "6.x",
  "Elasticsearch 6.x": "6.x",
  "Elasticsearch 7": "7.x",
  "Elasticsearch 7.x": "7.x",
  "Elasticsearch 8": "8.x",
  "Elasticsearch 8.x": "8.x"
}
Type assignment

Datastore ·search_engine_datastore confidence 0.93

Elasticsearch is fundamentally a system that persists and indexes data for retrieval, so under the Datastore vs Format rule it fits Datastore rather than Tool or Platform.

Derived legacy fields
Category
Datastore
Sub-category
search_engine_datastore
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • NoSQL and Cache Stores Catalog dimension db id 145

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • NoSQL and Cache Stores Catalog dimension db id 145

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

Locked dimensions (v3 placement)

  • NoSQL and Search Stores

    Reuses catalog slug

    Non-relational data stores used for flexible schema, low-latency retrieval, and indexed search over documents and events. Elasticsearch belongs here because it is commonly used as a distributed document store and search engine with query, indexing, and aggregation capabilities.

  • NoSQL and Cache Stores

    Reuses catalog slug

    Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL and Cache Stores
nosql-and-cache-stores
New skill saved · Existing dimension (library) · Role↔dimension saved
MongoDB Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MongoDB id=432 · mongodb

Aliases — catalog

  • STIX/TAXII (CANONICAL) primary

Context tags (catalog)

ATT&CK CTI IOC MISP OpenCTI STIX 2.1 TAXII 2.1 cyber threat intelligence indicator enrichment indicator of compromise malware analysis threat actor threat feed threat intelligence threat sharing

Stored enrichment (catalog DB)

Category
Standard
Sub-category
Threat Intelligence Exchange Standard
Vendor
OASIS
License
other_open
Year introduced
2012
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: STIX/TAXII appears in threat-intel and SOC job postings, but JD volume is far below mainstream standards; it’s mainly used in specialized CTI platforms and vendor integrations rather than general software roles.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
360
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL and Cache Stores Catalog dimension db id 145

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • NoSQL and Data Lake Storage Catalog dimension db id 73

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL and Cache Stores
nosql-and-cache-stores
Existing dimension (library) · Role↔dimension saved
NoSQL and Data Lake Storage
nosql-and-data-lake-storage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cassandra id=850 · cassandra

Aliases — catalog

  • multi-stage builds (CANONICAL) primary

Context tags (catalog)

CI/CD DevOps Docker Kubernetes artifact management automation build caching build pipeline build tools containerization continuous integration deployment strategies image layers microservices performance optimization versioning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Build Optimization Concept
Confidence
0.84
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common Docker practice; multi-stage builds are widely documented and frequently appear in containerization JDs and CI/CD guides as a standard image-optimization technique.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
12
Sub-category id
702
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL and Cache Stores Catalog dimension db id 145

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL and Cache Stores
nosql-and-cache-stores
Existing dimension (library) · Role↔dimension saved
Kubernetes Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=158 · kubernetes

Aliases — catalog

  • Column-level security (CANONICAL) primary

Context tags (catalog)

ABAC PII access policies attribute-based access control audit logging data governance data masking database permissions dynamic masking fine-grained access control least privilege policy enforcement row-level security sensitive data static masking

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Access Control Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in cloud/data platform JDs and vendor docs for Snowflake, BigQuery, and PostgreSQL RLS/column masking, but is not yet a universal hiring staple like core IAM or RBAC.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1524
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Orchestration Platforms Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Cloud Engineer, DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Orchestration Platforms
orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=153 · docker

Aliases — catalog

  • Metabase (CANONICAL) primary

Context tags (catalog)

BigQuery MySQL PostgreSQL Redshift SQL ad hoc analysis cards collections dashboards data visualization embedded analytics filters questions segments self-service BI

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Bi Analytics Tool
Vendor
Metabase, Inc.
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Metabase appears in many BI/analytics job postings and is growing in GitHub usage, but it is still far less universal than Tableau/Power BI in enterprise JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Containerization and Image Delivery Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Model Serving Deployment and Runtime Packaging Catalog dimension db id 52

    Library dimension (catalog)

    Roles linked in library: MLOps Engineer, Machine Learning Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Containerization and Image Delivery
containerization-and-image-delivery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LXC/LXD Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity niche confidence 0.84

LXC/LXD appears in relatively few job postings compared with Docker/Kubernetes, and Canonical has shifted emphasis toward MicroK8s and Ubuntu Pro rather than broad LXD hiring demand.

Vendor & license

Linux Containers ·gpl_v2 ·since 2008 (0.93)

Context keywords
Linux containers system containers containerd cgroups namespaces snap images profiles bridges storage pools networking cloud-init VMs proxmox orchestration
Ambiguity low

LXC/LXD is a specific container management stack; in JDs it is usually named explicitly and is unlikely to be mistaken for a different catalog skill.

Versioning

Not versioned

Type assignment

Tool ·container_management_tool confidence 0.95

LXC/LXD is software you run to manage containers on your own systems, so by the Tool vs Platform rule it is a Tool rather than a hosted platform or a framework.

Derived legacy fields
Category
Tool
Sub-category
container_management_tool
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Virtualization Cluster Provisioning and Host Lifecycle Management Proposed / LLM

    Proposed / LLM dimension (no DB id yet)

Locked dimensions (v3 placement)

  • Virtualization Cluster Provisioning and Host Lifecycle Management

    Pipeline tentative id

    Buildout, baseline configuration, and lifecycle management of virtualization clusters and hosts, including host provisioning, LXC/LXD-based host and instance management, image management, cluster expansion, maintenance mode, host draining, patching, and retirement.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Virtualization Cluster Provisioning and Host Lifecycle Management
d_merge_01
New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role)
Agile Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=2604 · agile

Aliases — from this run (catalog unavailable)

  • Agile (CANONICAL)

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
2124
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Project Delivery and Coordination Catalog dimension db id 366

    Library dimension (catalog)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Project Delivery and Coordination
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=2579 · ci-cd

Aliases — from this run (catalog unavailable)

  • CI/CD (CANONICAL)

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
2102
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: REST id=121 · rest

Aliases — catalog

  • DStreams (VERSION)
  • Spark 2.x (VERSION)
  • Spark 3.x (VERSION)
  • Spark Streaming (VERSION)
  • Spark Structured Streaming (VERSION)
  • Structured Streaming (VERSION)

Context tags (catalog)

DStreams Kafka Kinesis Structured Streaming backpressure checkpointing event time exactly-once micro-batch stateful processing streaming ETL trigger intervals watermarking window functions windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2013
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
Structured Streaming (Spark 2.0+)

Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
67
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Integration and Data Fetching Catalog dimension db id 9

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microservices Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Microservices id=864 · microservices

Aliases — from this run (catalog unavailable)

  • Microservices (CANONICAL) primary

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
663
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Service Architecture and Integration Catalog dimension db id 148

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Service Architecture and Integration
service-architecture-and-integration
Existing dimension (library) · Role↔dimension saved
ML Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

Machine learning appears in large volumes of job postings across data, product, and platform roles, and major cloud vendors offer managed ML services, indicating broad adoption rather than a niche stack.

Vendor & license

(0.99)

Context keywords
feature engineering model training supervised learning unsupervised learning classification regression cross-validation hyperparameter tuning scikit-learn TensorFlow PyTorch XGBoost random forest gradient boosting train-test split
Ambiguity flagged

Could be confused with: markup_language

"ML" is a common acronym for Machine Learning, but in JDs it can also mean Markup Language. A reasonable extractor could confuse the two without context.

Versioning

Not versioned

Type assignment

Concept ·machine_learning confidence 0.96

ML is fundamentally a knowledge unit about learning from data, so under the Concept vs Methodology rule it fits Concept rather than a tool, framework, or domain.

Derived legacy fields
Category
Concept
Sub-category
machine_learning
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Problem Framing and Analytical/ML Task Definition Proposed / LLM

    Proposed / LLM dimension (no DB id yet)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Problem Framing and Analytical/ML Task Definition

    Pipeline tentative id

    Translating ambiguous business or research needs into a well-posed analytical or machine learning problem: defining the target question or variable, choosing success metrics and baselines, assessing feasibility, and setting the boundary of the work before methods, training, or deployment are chosen. This includes business-to-model translation, hypothesis formulation, labeling strategy, and deciding whether ML or another analytical approach is appropriate.

  • Machine Learning Fundamentals

    Pipeline tentative id

    Core concepts, methods, and workflows for building predictive or pattern-learning systems. ML belongs here as the umbrella skill covering model types, training, feature use, and the basic vocabulary of supervised and unsupervised learning.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Problem Framing and Analytical/ML Task Definition
d_merge_01
New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (AWS, Microsoft, Google) market AI services as core offerings rather than niche add-ons.

Vendor & license

(0.99)

Context keywords
machine learning deep learning neural networks natural language processing computer vision reinforcement learning model training inference feature engineering transformers LLMs prompt engineering MLOps fine-tuning vector database
Ambiguity flagged

Could be confused with: machine_learning, artificial_intelligence

"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 extractors may conflate nearby catalog skills.

Versioning

Not versioned

Type assignment

Concept ·artificial_intelligence confidence 0.98

AI is fundamentally a named knowledge unit about intelligent systems, so under the Concept vs Methodology rule it fits Concept rather than a tool, platform, or architecture.

Derived legacy fields
Category
Concept
Sub-category
artificial_intelligence
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Artificial Intelligence Foundations

    Pipeline tentative id

    Core AI concepts, methods, and terminology used to build intelligent systems. This fits the target skill because "AI" is a broad umbrella term that can refer to the overall discipline rather than a specific operational sub-area.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

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
AWS in_db
Cloud Platform Operations
cloud-platform-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
C# in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
Java in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript in_db
Frontend Programming Languages
frontend-programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript in_db
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript in_db
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript in_db
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
JavaScript in_db
ServiceNow Scripting and Logic
servicenow-scripting-and-logic
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Analytical Programming Languages
analytical-programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Automation Scripting and CLI
automation-scripting-and-cli
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Automation and Scripting for Operations
automation-and-scripting-for-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Network Automation and Scripting
network-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Security Automation and Scripting
security-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Node.js in_db
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension saved
SQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension saved
SQL in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MongoDB in_db
NoSQL and Cache Stores
nosql-and-cache-stores
Existing dimension (library) · Role↔dimension saved
MongoDB in_db
NoSQL and Data Lake Storage
nosql-and-data-lake-storage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cassandra in_db
NoSQL and Cache Stores
nosql-and-cache-stores
Existing dimension (library) · Role↔dimension saved
Kubernetes in_db
Orchestration Platforms
orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Containerization and Image Delivery
containerization-and-image-delivery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile in_db
Project Delivery and Coordination
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Agile in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST in_db
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microservices in_db
Service Architecture and Integration
service-architecture-and-integration
Existing dimension (library) · Role↔dimension saved
Microsoft Azure in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C/C++ in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Elasticsearch in_db
NoSQL and Cache Stores
nosql-and-cache-stores
New skill saved · Existing dimension (library) · Role↔dimension saved
LXC/LXD in_db
Virtualization Cluster Provisioning and Host Lifecycle Management
d_merge_01
New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role)
ML in_db
Problem Framing and Analytical/ML Task Definition
d_merge_01
New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role)
ML in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_added Microsoft Azure 2629
canonical_skill_added C/C++ 2630
canonical_skill_added Elasticsearch 2631
canonical_skill_added LXC/LXD 2632
canonical_skill_added ML 2633
canonical_skill_added AI 2634
dimension_skill_link Microsoft Azure ↔ Version Control Systems 365
dimension_skill_link C/C++ ↔ Version Control Systems 365
dimension_skill_link Elasticsearch ↔ NoSQL and Cache Stores 145
dimension_skill_link LXC/LXD ↔ Virtualization Cluster Provisioning and Host Lifecycle Management 349
dimension_skill_link ML ↔ Problem Framing and Analytical/ML Task Definition 97
dimension_skill_link ML ↔ Version Control Systems 365
dimension_skill_link AI ↔ Version Control Systems 365
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": false,
      "skill_name": "AWS"
    },
    {
      "is_primary": false,
      "skill_name": "GCP"
    },
    {
      "is_primary": false,
      "skill_name": "Microsoft Azure"
    },
    {
      "is_primary": false,
      "skill_name": "C/C++"
    },
    {
      "is_primary": false,
      "skill_name": "C#"
    },
    {
      "is_primary": true,
      "skill_name": "Java"
    },
    {
      "is_primary": true,
      "skill_name": "JavaScript"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "Node.js"
    },
    {
      "is_primary": false,
      "skill_name": "SQL"
    },
    {
      "is_primary": false,
      "skill_name": "Elasticsearch"
    },
    {
      "is_primary": false,
      "skill_name": "MongoDB"
    },
    {
      "is_primary": false,
      "skill_name": "Cassandra"
    },
    {
      "is_primary": false,
      "skill_name": "Kubernetes"
    },
    {
      "is_primary": false,
      "skill_name": "Docker"
    },
    {
      "is_primary": false,
      "skill_name": "LXC/LXD"
    },
    {
      "is_primary": false,
      "skill_name": "Agile"
    },
    {
      "is_primary": false,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": false,
      "skill_name": "REST"
    },
    {
      "is_primary": false,
      "skill_name": "Microservices"
    },
    {
      "is_primary": false,
      "skill_name": "ML"
    },
    {
      "is_primary": false,
      "skill_name": "AI"
    }
  ],
  "run_id": null
}
API 2 — extract-details
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      "alias_persisted": false,
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 3043,
      "existing_alias_text": "GCP",
      "input_term": "GCP",
      "matched_canonical": {
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        "display_name": "GCP",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "gcp",
        "sub_category_id": 161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1209,
      "existing_alias_text": "C#",
      "input_term": "C#",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "c",
        "sub_category_id": 54,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "Java",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "JavaScript",
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        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      "alias_persisted": false,
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      "existing_alias_text": "Python",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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        "skill_nature": "RUNTIME",
        "slug": "node-js",
        "sub_category_id": 2120,
        "typical_lifespan": "EVERGREEN",
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      "alias_persisted": false,
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      "existing_alias_text": "SQL",
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        "skill_nature": "LANGUAGE",
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        "typical_lifespan": "EVERGREEN",
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "MongoDB",
      "input_term": "MongoDB",
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        "display_name": "MongoDB",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "sub_category_id": 360,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1282,
      "existing_alias_text": "Cassandra",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 304,
      "existing_alias_text": "Kubernetes",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 299,
      "existing_alias_text": "Docker",
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        "is_also_category": false,
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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      "alias_persisted": false,
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        "typical_lifespan": "EVERGREEN",
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        "skill_nature": "PROTOCOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "existing_alias_text": "Microservices",
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        "skill_nature": "PATTERN",
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        "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": "Java",
      "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": "Java",
      "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": "Frontend Programming Languages",
        "id": 1,
        "rationale": "Languages used to implement browser-side application logic, component behavior, and UI state. This is the core code layer for frontend features and interactive experiences.",
        "slug": "frontend-programming-languages",
        "source": "db"
      },
      "input_skill": "JavaScript",
      "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": "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": "JavaScript",
      "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 Security Work",
        "id": 328,
        "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
        "slug": "programming-languages-for-security-work",
        "source": "db"
      },
      "input_skill": "JavaScript",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Test Automation",
        "id": 193,
        "rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
        "slug": "programming-languages-for-test-automation",
        "source": "db"
      },
      "input_skill": "JavaScript",
      "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": "ServiceNow Scripting and Logic",
        "id": 210,
        "rationale": "Server-side scripting used to implement workflow behavior, validations, and record logic on the ServiceNow platform. This is the core customization layer for translating requirements into executable platform behavior.",
        "slug": "servicenow-scripting-and-logic",
        "source": "db"
      },
      "input_skill": "JavaScript",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ServiceNOW Developer",
          "id": 24,
          "rationale": null,
          "role_archetype": null,
          "slug": "servicenow-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Analytical Programming Languages",
        "id": 82,
        "rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
        "slug": "analytical-programming-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Analyst",
          "id": 20,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-analyst",
          "source": "db"
        },
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Automation Scripting and CLI",
        "id": 48,
        "rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
        "slug": "automation-scripting-and-cli",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Azure Cloud Engineer",
          "id": 4,
          "rationale": null,
          "role_archetype": null,
          "slug": "azure-cloud-engineer",
          "source": "db"
        },
        {
          "display_name": "Cloud Engineer",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Automation and Scripting for Operations",
        "id": 361,
        "rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
        "slug": "automation-and-scripting-for-operations",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Virtualization Engineer",
          "id": 26,
          "rationale": null,
          "role_archetype": null,
          "slug": "virtualization-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Network Automation and Scripting",
        "id": 285,
        "rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
        "slug": "network-automation-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Network Engineer",
          "id": 21,
          "rationale": null,
          "role_archetype": null,
          "slug": "network-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for AI Workflows",
        "id": 261,
        "rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
        "slug": "programming-languages-for-ai-workflows",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AI Engineer",
          "id": 12,
          "rationale": null,
          "role_archetype": null,
          "slug": "ai-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Backend Systems",
        "id": 140,
        "rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
        "slug": "programming-languages-for-backend-systems",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 67,
        "rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
        "id": 113,
        "rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
        "slug": "programming-languages-for-ml-systems",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Security Work",
        "id": 328,
        "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
        "slug": "programming-languages-for-security-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Test Automation",
        "id": 193,
        "rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
        "slug": "programming-languages-for-test-automation",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Automation Tester",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "automation-tester",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Security Automation and Scripting",
        "id": 258,
        "rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
        "slug": "security-automation-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "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": "Node.js",
      "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": "Relational Data Modeling",
        "id": 71,
        "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
        "slug": "relational-data-modeling",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "NoSQL and Cache Stores",
        "id": 145,
        "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
        "slug": "nosql-and-cache-stores",
        "source": "db"
      },
      "input_skill": "MongoDB",
      "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": "NoSQL and Data Lake Storage",
        "id": 73,
        "rationale": "Non-relational stores and lake storage used for semi-structured, large-scale, or raw data retention. This cluster is coherent because many pipelines land and serve data outside classic relational warehouses.",
        "slug": "nosql-and-data-lake-storage",
        "source": "db"
      },
      "input_skill": "MongoDB",
      "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": "NoSQL and Cache Stores",
        "id": 145,
        "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
        "slug": "nosql-and-cache-stores",
        "source": "db"
      },
      "input_skill": "Cassandra",
      "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": "Orchestration Platforms",
        "id": 25,
        "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
        "slug": "orchestration-platforms",
        "source": "db"
      },
      "input_skill": "Kubernetes",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Engineer",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Containerization and Image Delivery",
        "id": 24,
        "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
        "slug": "containerization-and-image-delivery",
        "source": "db"
      },
      "input_skill": "Docker",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "DevOps Engineer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Model Serving Deployment and Runtime Packaging",
        "id": 52,
        "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
        "slug": "model-serving-deployment-and-runtime-packaging",
        "source": "db"
      },
      "input_skill": "Docker",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
          "source": "db"
        },
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Project Delivery and Coordination",
        "id": 366,
        "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
        "slug": "d_init_02",
        "source": "db"
      },
      "input_skill": "Agile",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Agile",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "CI/CD",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "API Integration and Data Fetching",
        "id": 9,
        "rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
        "slug": "api-integration-and-data-fetching",
        "source": "db"
      },
      "input_skill": "REST",
      "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": "Service Architecture and Integration",
        "id": 148,
        "rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
        "slug": "service-architecture-and-integration",
        "source": "db"
      },
      "input_skill": "Microservices",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
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            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "CI/CD",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "REST",
          "alias_type": "CANONICAL",
          "id": 266,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "REST",
        "id": 121,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "rest",
        "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",
          "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"
            }
          ]
        }
      ],
      "input_skill": "REST",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Microservices",
          "alias_type": "CANONICAL",
          "id": 1307,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "Microservices",
        "id": 864,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "microservices",
        "sub_category_id": 663,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Service Architecture and Integration",
            "id": 148,
            "rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
            "slug": "service-architecture-and-integration",
            "source": "db"
          },
          "input_skill": "Microservices",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 14,
              "rationale": null,
              "role_archetype": null,
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Microservices",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": null,
            "display_name": "Problem Framing and Analytical/ML Task Definition",
            "id": null,
            "rationale": "Translating ambiguous business or research needs into a well-posed analytical or machine learning problem: defining the target question or variable, choosing success metrics and baselines, assessing feasibility, and setting the boundary of the work before methods, training, or deployment are chosen. This includes business-to-model translation, hypothesis formulation, labeling strategy, and deciding whether ML or another analytical approach is appropriate.",
            "slug": "d_merge_01",
            "source": "llm"
          },
          "input_skill": "ML",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "ML",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "ML",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "machine_learning",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": true,
            "confused_with": [
              "markup_language"
            ],
            "reasoning": "\"ML\" is a common acronym for Machine Learning, but in JDs it can also mean Markup Language. A reasonable extractor could confuse the two without context."
          },
          "context_keywords": {
            "context_keywords": [
              "feature engineering",
              "model training",
              "supervised learning",
              "unsupervised learning",
              "classification",
              "regression",
              "cross-validation",
              "hyperparameter tuning",
              "scikit-learn",
              "TensorFlow",
              "PyTorch",
              "XGBoost",
              "random forest",
              "gradient boosting",
              "train-test split"
            ]
          },
          "maturity": {
            "confidence": 0.93,
            "maturity": "well_known",
            "reasoning": "Machine learning appears in large volumes of job postings across data, product, and platform roles, and major cloud vendors offer managed ML services, indicating broad adoption rather than a niche stack."
          },
          "skill_id": "ml",
          "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": "Translating ambiguous business or research needs into a well-posed analytical or machine learning problem: defining the target question or variable, choosing success metrics and baselines, assessing feasibility, and setting the boundary of the work before methods, training, or deployment are chosen. This includes business-to-model translation, hypothesis formulation, labeling strategy, and deciding whether ML or another analytical approach is appropriate.",
            "exemplar_skills": [
              "Problem Framing and Analytical/ML Task Definition"
            ],
            "in_scope": "Skills, tools, and practices that belong under Problem Framing and Analytical/ML Task Definition for the target role, including items implied by the dimension rationale.",
            "name": "Problem Framing and Analytical/ML Task Definition",
            "out_of_scope": "Adjacent clusters explicitly not owned by Problem Framing and Analytical/ML Task Definition, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "d_merge_01"
          },
          {
            "description": "Core concepts, methods, and workflows for building predictive or pattern-learning systems. ML belongs here as the umbrella skill covering model types, training, feature use, and the basic vocabulary of supervised and unsupervised learning.",
            "exemplar_skills": [
              "ML",
              "machine learning",
              "supervised learning",
              "unsupervised learning",
              "feature engineering",
              "model training",
              "classification",
              "regression"
            ],
            "in_scope": "ML, supervised learning, unsupervised learning, regression, classification, clustering, feature engineering, training/validation splits, overfitting, underfitting, model selection",
            "name": "Machine Learning Fundamentals",
            "out_of_scope": "Model evaluation metrics and robustness checks belong to model-evaluation-and-validation, production packaging and deployment belong to model-serving-deployment-and-runtime-packaging, workflow automation around retraining belongs to retraining-triggers-and-scheduling",
            "overlap_flags": [
              {
                "reason": "ML work often includes evaluation, but this dimension is specifically about assessing model quality rather than the broader ML lifecycle.",
                "with_dim_id": "model-evaluation-and-validation",
                "with_dim_name": null,
                "with_role": "Data Scientist"
              },
              {
                "reason": "ML models are frequently deployed, but serving and packaging are operational concerns separate from core ML concepts.",
                "with_dim_id": "model-serving-deployment-and-runtime-packaging",
                "with_dim_name": null,
                "with_role": "MLOps Engineer, Machine Learning Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [
          {
            "a_dim_id": "domain-problem-framing",
            "a_name": "Domain Problem Framing",
            "a_role": "__skill_focal__",
            "b_dim_id": "domain-problem-framing",
            "b_name": "Domain Problem Framing",
            "b_role": "Data Scientist",
            "into": "d_merge_01",
            "into_name": "Problem Framing and Analytical/ML Task Definition",
            "merged_from": [
              "domain-problem-framing",
              "domain-problem-framing"
            ],
            "pair_kind": "cross_role",
            "reasoning": "Both dimensions describe the same skill cluster: framing ambiguous business/research needs into a well-defined problem before methods are chosen. Dim A focuses on ML framing, including target variable definition, success metrics, baseline selection, and feasibility assessment. Dim B states the same boundary-setting work in broader analytical terms, including analytical questions, metrics, and testable approaches. The role label differs, but the underlying skills overlap almost completely.",
            "similarity": 0.7490327905275234
          }
        ],
        "placed": {
          "name": "ML",
          "placement_confidence": 0.92,
          "primary_dimension": "d_merge_01",
          "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_01"
          ],
          "skill_id": "ml"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "r",
            "javascript",
            "aws",
            "ec2",
            "polygon",
            "microsoft-sentinel",
            "alchemy",
            "sqlmap-testing",
            "aks"
          ],
          "requires": [],
          "skill_id": "ml",
          "suppress_on_match": []
        },
        "skill_id": "ml",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.96,
          "name": "ML",
          "reasoning": "ML is fundamentally a knowledge unit about learning from data, so under the Concept vs Methodology rule it fits Concept rather than a tool, framework, or domain.",
          "skill_id": "ml",
          "subtype": "machine_learning",
          "type": "Concept"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "AI",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "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 extractors may 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",
              "LLMs",
              "prompt engineering",
              "MLOps",
              "fine-tuning",
              "vector database"
            ]
          },
          "maturity": {
            "confidence": 0.93,
            "maturity": "well_known",
            "reasoning": "AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (AWS, Microsoft, Google) market AI services as core offerings rather than niche add-ons."
          },
          "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": [
          {
            "description": "Core AI concepts, methods, and terminology used to build intelligent systems. This fits the target skill because \"AI\" is a broad umbrella term that can refer to the overall discipline rather than a specific operational sub-area.",
            "exemplar_skills": [
              "AI",
              "machine learning",
              "neural networks",
              "generative AI",
              "reinforcement learning",
              "embeddings"
            ],
            "in_scope": "AI, machine learning basics, supervised learning, unsupervised learning, reinforcement learning, neural networks, generative AI concepts, embeddings, prompting basics, model capabilities and limitations",
            "name": "Artificial Intelligence Foundations",
            "out_of_scope": "Model evaluation and validation, inference service frameworks, AI orchestration and workflow logic, retraining triggers and scheduling, which are narrower operational or lifecycle dimensions",
            "overlap_flags": [
              {
                "reason": "AI work often includes assessing model quality, but evaluation is a distinct lifecycle dimension.",
                "with_dim_id": "model-evaluation-and-validation",
                "with_dim_name": null,
                "with_role": "Data Scientist"
              },
              {
                "reason": "AI systems may use orchestration, but that is about coordinating calls and workflows rather than the AI concept itself.",
                "with_dim_id": "ai-orchestration-and-workflow-logic",
                "with_dim_name": null,
                "with_role": "AI Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "AI",
          "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": "ai"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "python",
            "r",
            "pandas",
            "go",
            "ansible",
            "aws",
            "azure",
            "aks",
            "aws-cdk",
            "ec2"
          ],
          "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 under the Concept vs Methodology rule it fits Concept rather than a tool, platform, or architecture.",
          "skill_id": "ai",
          "subtype": "artificial_intelligence",
          "type": "Concept"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Microsoft Azure",
    "C/C++",
    "Elasticsearch",
    "LXC/LXD",
    "ML",
    "AI"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Backend Engineer",
    "id": 14,
    "rationale": "The primary skills Java, JavaScript, and Python align closely with the responsibilities of a Backend Engineer.",
    "role_archetype": null,
    "slug": "backend-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "GCP",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft Azure",
      "tag": "new"
    },
    {
      "skill": "C/C++",
      "tag": "new"
    },
    {
      "skill": "C#",
      "tag": "in_db"
    },
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "JavaScript",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Node.js",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "Elasticsearch",
      "tag": "new"
    },
    {
      "skill": "MongoDB",
      "tag": "in_db"
    },
    {
      "skill": "Cassandra",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "LXC/LXD",
      "tag": "new"
    },
    {
      "skill": "Agile",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "REST",
      "tag": "in_db"
    },
    {
      "skill": "Microservices",
      "tag": "in_db"
    },
    {
      "skill": "ML",
      "tag": "new"
    },
    {
      "skill": "AI",
      "tag": "new"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 14,
        "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": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "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": true,
        "skill_id": 163,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Platforms",
          "id": 332,
          "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
          "slug": "cloud-security-platforms",
          "source": "db"
        },
        "dimension_id": 332,
        "input_skill": "AWS",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 163,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Platforms",
          "id": 332,
          "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
          "slug": "cloud-security-platforms",
          "source": "db"
        },
        "dimension_id": 332,
        "input_skill": "GCP",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2304,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "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": "C#",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 680,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "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"
        },
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          "rationale": "Languages used to implement browser-side application logic, component behavior, and UI state. This is the core code layer for frontend features and interactive experiences.",
          "slug": "frontend-programming-languages",
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        },
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          {
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            "slug": "full-stack-developer",
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        ],
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          "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
          "slug": "programming-languages-for-security-work",
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          "display_name": "ServiceNow Scripting and Logic",
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          "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.",
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        },
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            "slug": "azure-cloud-engineer",
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          "rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
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        "dimension_id": 361,
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "Virtualization Engineer",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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            "display_name": "Network Engineer",
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            "slug": "network-engineer",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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            "display_name": "AI Engineer",
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        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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          "display_name": "Programming Languages for Security Work",
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          "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
          "slug": "programming-languages-for-security-work",
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        },
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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        "roles_from_db": [
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            "display_name": "Cybersecurity Engineer",
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            "slug": "cybersecurity-engineer",
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        ],
        "skill_dimension_saved": true,
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
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          "display_name": "Programming Languages for Test Automation",
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          "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"
        },
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        "input_skill": "Python",
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        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Automation Tester",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "automation-tester",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Security Automation and Scripting",
          "id": 258,
          "rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
          "slug": "security-automation-and-scripting",
          "source": "db"
        },
        "dimension_id": 258,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "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": "Node.js",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2599,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Data Modeling",
          "id": 71,
          "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
          "slug": "relational-data-modeling",
          "source": "db"
        },
        "dimension_id": 71,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Cache Stores",
          "id": 145,
          "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
          "slug": "nosql-and-cache-stores",
          "source": "db"
        },
        "dimension_id": 145,
        "input_skill": "MongoDB",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 432,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Data Lake Storage",
          "id": 73,
          "rationale": "Non-relational stores and lake storage used for semi-structured, large-scale, or raw data retention. This cluster is coherent because many pipelines land and serve data outside classic relational warehouses.",
          "slug": "nosql-and-data-lake-storage",
          "source": "db"
        },
        "dimension_id": 73,
        "input_skill": "MongoDB",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 432,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Cache Stores",
          "id": 145,
          "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
          "slug": "nosql-and-cache-stores",
          "source": "db"
        },
        "dimension_id": 145,
        "input_skill": "Cassandra",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 850,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Orchestration Platforms",
          "id": 25,
          "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
          "slug": "orchestration-platforms",
          "source": "db"
        },
        "dimension_id": 25,
        "input_skill": "Kubernetes",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "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": true,
        "skill_id": 158,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Containerization and Image Delivery",
          "id": 24,
          "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
          "slug": "containerization-and-image-delivery",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Docker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "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": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "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,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "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": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Project Delivery and Coordination",
          "id": 366,
          "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 366,
        "input_skill": "Agile",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2604,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Agile",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2604,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "CI/CD",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2579,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "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",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "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": true,
        "skill_id": 121,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Integration",
          "id": 148,
          "rationale": "Patterns for structuring backend systems as services and coordinating calls across internal and external dependencies. This includes how services are decomposed, connected, and evolved safely.",
          "slug": "service-architecture-and-integration",
          "source": "db"
        },
        "dimension_id": 148,
        "input_skill": "Microservices",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 864,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Microsoft Azure",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2629,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "C/C++",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2630,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "NoSQL and Cache Stores",
          "id": 145,
          "rationale": "Non-relational databases and in-memory stores used for low-latency access, flexible schemas, and specialized persistence patterns. This cluster is coherent because backend services often combine these stores with relational systems.",
          "slug": "nosql-and-cache-stores",
          "source": "db"
        },
        "dimension_id": 145,
        "input_skill": "Elasticsearch",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2631,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Virtualization Cluster Provisioning and Host Lifecycle Management",
          "id": null,
          "rationale": "Buildout, baseline configuration, and lifecycle management of virtualization clusters and hosts, including host provisioning, LXC/LXD-based host and instance management, image management, cluster expansion, maintenance mode, host draining, patching, and retirement.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 349,
        "input_skill": "LXC/LXD",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2632,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": null,
          "display_name": "Problem Framing and Analytical/ML Task Definition",
          "id": null,
          "rationale": "Translating ambiguous business or research needs into a well-posed analytical or machine learning problem: defining the target question or variable, choosing success metrics and baselines, assessing feasibility, and setting the boundary of the work before methods, training, or deployment are chosen. This includes business-to-model translation, hypothesis formulation, labeling strategy, and deciding whether ML or another analytical approach is appropriate.",
          "slug": "d_merge_01",
          "source": "llm"
        },
        "dimension_id": 97,
        "input_skill": "ML",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2633,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "ML",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2633,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 14,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "AI",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2634,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 6,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 7,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "21b88bfe-fda2-4503-b896-a6790db3380b"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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