Pipeline run
21b88bfe-fda2-4503-b896-a6790db3380b
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
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.
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.
Aliases — catalog
- Compaction (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- ASGI (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
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.
Microsoft ·proprietary ·since 2010 (0.99)
Microsoft Azure is a specific cloud platform name and is typically unambiguous in JDs; it is not commonly mistaken for another catalog skill.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
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.
ISO/IEC ·since 1972 (0.88)
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++.
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"
}
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.
- 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) |
Aliases — catalog
- submission state (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- sqlmap (CANONICAL) primary
Context tags (catalog)
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) |
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)
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) |
Aliases — catalog
- Cobalt Strike (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- hooks composition (CANONICAL) primary
Context tags (catalog)
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 |
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) |
Skill enrichment (orchestrator / LLM)
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.
Elastic ·apache_2 ·since 2010 (0.98)
Elasticsearch is a well-known, specific search engine/datastore name and is unlikely to be mistaken for another catalog skill in typical job descriptions.
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"
}
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.
- 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 |
Aliases — catalog
- STIX/TAXII (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- multi-stage builds (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- Column-level security (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Metabase (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
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.
Linux Containers ·gpl_v2 ·since 2008 (0.93)
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.
Not versioned
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.
- 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) |
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) |
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) |
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)
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) |
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 |
Skill enrichment (orchestrator / LLM)
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.
(0.99)
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.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
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.
(0.99)
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.
Not versioned
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.
- 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"
},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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],
"run_id": null
}
API 2 — extract-details
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},
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"slug": "programming-languages-for-test-automation",
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},
"input_skill": "JavaScript",
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"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
]
},
{
"dimension": {
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"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",
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}
]
},
{
"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": {
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"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",
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"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
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},
{
"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",
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}
]
},
{
"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",
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"roles_from_db": [
{
"display_name": "Network Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
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}
]
},
{
"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,
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]
},
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"display_name": "Programming Languages for Backend Systems",
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"slug": "programming-languages-for-backend-systems",
"source": "db"
},
"input_skill": "Python",
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"roles_from_db": [
{
"display_name": "Backend Engineer",
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"rationale": null,
"role_archetype": null,
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}
]
},
{
"dimension": {
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"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",
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},
"input_skill": "Python",
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{
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"id": 6,
"rationale": null,
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}
]
},
{
"dimension": {
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"display_name": "Programming Languages for ML Systems",
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"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,
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{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
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}
]
},
{
"dimension": {
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"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,
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"display_name": "Cybersecurity Engineer",
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"rationale": null,
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}
]
},
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"dimension": {
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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"
},
"input_skill": "Python",
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{
"display_name": "Automation Tester",
"id": 16,
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},
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"display_name": "Security Automation and Scripting",
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},
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{
"display_name": "Cybersecurity Engineer",
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"display_name": "Programming Languages for Backend Systems",
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"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.",
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"input_skill": "Node.js",
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{
"display_name": "Data Engineer",
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},
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]
},
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"input_skill": "Cassandra",
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},
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"input_skill": "Kubernetes",
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"slug": "cloud-engineer",
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"display_name": "DevOps Engineer",
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},
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"dimension": {
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"display_name": "API Integration and Data Fetching",
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]
},
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"input_skill": "Microsoft Azure",
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},
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},
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{
"dimension": {
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"slug": "relational-data-modeling",
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},
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{
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},
{
"display_name": "Data Engineer",
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"rationale": null,
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"slug": "data-engineer",
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}
]
},
{
"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"
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}
],
"input_skill": "SQL",
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"source_tag": "db",
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},
{
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"canonical": null,
"dimensions": [
{
"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": "Elasticsearch",
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{
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"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
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]
},
{
"dimension": {
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"display_name": "NoSQL and Cache Stores",
"id": 145,
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"slug": "nosql-and-cache-stores",
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},
"input_skill": "Elasticsearch",
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{
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"id": 14,
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]
}
],
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"new_alias_text": null,
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"sub_category": "search_engine_datastore",
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"enrichment": {
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"reasoning": "Elasticsearch is a well-known, specific search engine/datastore name and is unlikely to be mistaken for another catalog skill in typical job descriptions."
},
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"Beats",
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"full-text search",
"aggregations",
"query DSL",
"inverted index",
"Elasticsearch cluster",
"Elasticsearch index",
"Elasticsearch API",
"Elasticsearch ingest"
]
},
"maturity": {
"confidence": 0.96,
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"reasoning": "Commonly listed in job descriptions for search/log analytics and supported by Elastic\u2019s broad ecosystem; no vendor sunset, and it remains a standard production search engine alongside OpenSearch."
},
"skill_id": "elasticsearch",
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"license": "apache_2",
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"versioning": {
"current_version": "8.x",
"version_aliases": {
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"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"
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"reasoning": "Dim A is search-store specific: it centers on Elasticsearch, document indexing, inverted indexes, full-text search, query DSL, aggregations, mappings, shards/replicas, and relevance tuning. Dim B is a broader backend storage bucket for non-relational databases and in-memory cache stores used for low-latency access and specialized persistence patterns. A explicitly excludes cache-only usage, so Redis-style caching fits B but not A. The overlap is naming, not the same skill cluster.",
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"query DSL",
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"name": "NoSQL and Search Stores",
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{
"reason": "Elasticsearch clusters often require shard sizing, heap tuning, and query optimization, which can overlap with performance tuning.",
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"with_dim_name": null,
"with_role": "Network Engineer, Virtualization Engineer"
}
],
"tentative_id": "nosql-and-cache-stores"
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{
"description": "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.",
"exemplar_skills": [
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],
"in_scope": "Skills, tools, and practices that belong under NoSQL and Cache Stores for the target role, including items implied by the dimension rationale.",
"name": "NoSQL and Cache Stores",
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"overlap_flags": [],
"tentative_id": "nosql-and-cache-stores"
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],
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"aws-s3",
"rest-apis",
"metadata-json",
"event-logs",
"rapid7-insightvm",
"encryption-at-rest",
"aws-kms",
"ec2",
"gke",
"vmware-esxi"
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},
"skill_id": "elasticsearch",
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"Platform: ruled out \u2014 it is typically software you run yourself, not a hosted multi-tenant environment with managed APIs."
],
"confidence": 0.93,
"name": "Elasticsearch",
"reasoning": "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.",
"skill_id": "elasticsearch",
"subtype": "search_engine_datastore",
"type": "Datastore"
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"warnings": [
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]
},
"source_tag": "llm",
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},
{
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{
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"id": 684,
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},
{
"alias_text": "MongoDB 3",
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"id": 685,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 3.x",
"alias_type": "VERSION",
"id": 691,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4",
"alias_type": "VERSION",
"id": 686,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 4.x",
"alias_type": "VERSION",
"id": 692,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 5",
"alias_type": "VERSION",
"id": 687,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 5.x",
"alias_type": "VERSION",
"id": 693,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 6",
"alias_type": "VERSION",
"id": 688,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 6.x",
"alias_type": "VERSION",
"id": 694,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 7",
"alias_type": "VERSION",
"id": 689,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 7.x",
"alias_type": "VERSION",
"id": 695,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 8",
"alias_type": "VERSION",
"id": 690,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "MongoDB 8.x",
"alias_type": "VERSION",
"id": 696,
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"match_strategy": "CASE_INSENSITIVE"
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],
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{
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},
{
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]
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],
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},
{
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"id": 1282,
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],
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]
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],
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},
{
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{
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},
{
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},
{
"alias_text": "Kubernetes 1.0+",
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},
{
"alias_text": "Kubernetes 1.1",
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},
{
"alias_text": "Kubernetes 1.10",
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"id": 318,
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},
{
"alias_text": "Kubernetes 1.11",
"alias_type": "VERSION",
"id": 319,
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},
{
"alias_text": "Kubernetes 1.12",
"alias_type": "VERSION",
"id": 320,
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{
"alias_text": "Kubernetes 1.13",
"alias_type": "VERSION",
"id": 321,
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"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.14",
"alias_type": "VERSION",
"id": 322,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.15",
"alias_type": "VERSION",
"id": 323,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.16",
"alias_type": "VERSION",
"id": 324,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.17",
"alias_type": "VERSION",
"id": 325,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.18",
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"id": 326,
"is_primary": false,
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},
{
"alias_text": "Kubernetes 1.19",
"alias_type": "VERSION",
"id": 327,
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},
{
"alias_text": "Kubernetes 1.2",
"alias_type": "VERSION",
"id": 309,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.20",
"alias_type": "VERSION",
"id": 328,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.21",
"alias_type": "VERSION",
"id": 329,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.22",
"alias_type": "VERSION",
"id": 330,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.23",
"alias_type": "VERSION",
"id": 331,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.24",
"alias_type": "VERSION",
"id": 332,
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"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.25",
"alias_type": "VERSION",
"id": 333,
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"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.26",
"alias_type": "VERSION",
"id": 334,
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},
{
"alias_text": "Kubernetes 1.27",
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"id": 335,
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},
{
"alias_text": "Kubernetes 1.28",
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"id": 336,
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"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.29",
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"id": 337,
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},
{
"alias_text": "Kubernetes 1.3",
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"id": 310,
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{
"alias_text": "Kubernetes 1.30",
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"id": 338,
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},
{
"alias_text": "Kubernetes 1.4",
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"id": 311,
"is_primary": false,
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},
{
"alias_text": "Kubernetes 1.5",
"alias_type": "VERSION",
"id": 312,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.6",
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"id": 313,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.7",
"alias_type": "VERSION",
"id": 314,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes 1.8",
"alias_type": "VERSION",
"id": 315,
"is_primary": false,
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},
{
"alias_text": "Kubernetes 1.9",
"alias_type": "VERSION",
"id": 316,
"is_primary": false,
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},
{
"alias_text": "Kubernetes 1.x",
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"id": 317,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Kubernetes v1",
"alias_type": "VERSION",
"id": 306,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "k8s",
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"id": 305,
"is_primary": false,
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}
],
"canonical": {
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},
"dimensions": [
{
"dimension": {
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"display_name": "Orchestration Platforms",
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"slug": "orchestration-platforms",
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},
"input_skill": "Kubernetes",
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"roles_from_db": [
{
"display_name": "Cloud Engineer",
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"slug": "cloud-engineer",
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},
{
"display_name": "DevOps Engineer",
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"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Kubernetes",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Docker",
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"id": 299,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Docker",
"id": 153,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "docker",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Containerization and Image Delivery",
"id": 24,
"rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
"slug": "containerization-and-image-delivery",
"source": "db"
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"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"
},
"dimension_id": 113,
"input_skill": "C#",
"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": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-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 Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "C#",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 680,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "Java",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 395,
"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": "Java",
"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": 395,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 67,
"rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 67,
"input_skill": "Java",
"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": 395,
"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"
},
"dimension_id": 113,
"input_skill": "Java",
"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": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 395,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "Java",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 395,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"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"
},
"dimension_id": 1,
"input_skill": "JavaScript",
"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": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "JavaScript",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Security Work",
"id": 328,
"rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
"slug": "programming-languages-for-security-work",
"source": "db"
},
"dimension_id": 328,
"input_skill": "JavaScript",
"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": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "JavaScript",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"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"
},
"dimension_id": 210,
"input_skill": "JavaScript",
"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": "ServiceNOW Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "servicenow-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"dimension_id": 82,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"dimension_id": 48,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation and Scripting for Operations",
"id": 361,
"rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
"slug": "automation-and-scripting-for-operations",
"source": "db"
},
"dimension_id": 361,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Virtualization Engineer",
"id": 26,
"rationale": null,
"role_archetype": null,
"slug": "virtualization-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Network Automation and Scripting",
"id": 285,
"rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
"slug": "network-automation-and-scripting",
"source": "db"
},
"dimension_id": 285,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 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": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 67,
"rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 67,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 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"
},
"dimension_id": 113,
"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": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Security Work",
"id": 328,
"rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
"slug": "programming-languages-for-security-work",
"source": "db"
},
"dimension_id": 328,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 14,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 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": {
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"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,
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},
{
"chosen_role_id": 14,
"dimension": {
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"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": {
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"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.