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Pipeline run

5fe1b1d3-985f-42b6-8256-74c3da2d27d6

Pipeline LLM cost (USD)
API 1: $0.0033 API 2: $0.0574 API 3: $0.0001 Total: $0.0607

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · End-to-end feature delivery
Build responsive Next.js front ends and Python FastAPI/Flask services, design REST APIs and data layers, and ship production-ready full-stack features with performance, reliability, and scalability in mind.
""Architect end-to-end solutions across frontend, backend, databases, and integration layers""
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.50 / 5
· Title match
Has AI skill
· AI skill (primary)
AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): AI
Evidence — skills matched in JD (10)
Next.js Python FastAPI Flask REST Relational Databases NoSQL Microservices AI Code Review
Skill cluster (5 dimension groups, role-scoped)
Web Application Frameworks
FastAPI Flask
API Design and Contracts
REST
Programming Languages
Python
UI Frameworks and Rendering
Next.js
Cross-cutting / unaligned
Relational Databases NoSQL Microservices AI Code Review
Show KRA description ↓
The Full Stack Engineer at Inception is responsible for designing, developing, and maintaining scalable, end-to-end software systems that enable seamless data and service integration. The role involves building high-performance front-end and back-end systems, ensuring robust architecture, and delivering reliable, production-ready applications. • Design and develop responsive, high-performance front-end applications using Next.js, translating UI/UX designs into scalable solutions • Build and maintain backend services and APIs using Python (FastAPI/Flask), ensuring performance, scalability, and reliability • Architect end-to-end solutions across frontend, backend, databases, and integration layers • Design, develop, and optimize RESTful APIs and service interfaces to support product functionality • Ensure non-functional requirements (performance, security, scalability, maintainability) are embedded in all solutions • Develop and manage relational and NoSQL databases, ensuring efficient data storage and retrieval • Contribute to system architecture decisions, including monolithic and microservices-based approaches • Collaborate with data scientists, applied scientists, and engineering teams to integrate AI capabilities into applications • Troubleshoot, debug, and resolve issues across the full technology stack • Participate in code reviews and enforce best practices for code quality, testing, and documentation

Signals

Skill backend-engineer
0.30
Alias frontend-engineer
0.39
KRA android-engineer
0.48

Post-classification

Centroid
Alias collision log
New-role queue#30
New skills captured0
New KRA captured

v3 pipeline · Full Stack Engineer

approved
0 · charter approved 2026-05-27T16:00:30.272878Z 21.1s
1 · anchor approved 2026-05-27T16:00:55.622767Z 14.2s
2 · dim_gen approved 2026-05-27T16:01:14.153467Z 98.0s
3 · reconciler awaiting_review 2026-05-27T16:02:57.506712Z 423.0s
4 · typing pending
5 · placement pending
6 · containment pending
7 · enrichment pending
8 · catalog_load approved 2026-05-19T11:19:17.969630Z 93.1s
Status: completed Created: 2026-05-18T23:24:57.787354Z Updated: 2026-05-18T23:27:05.119693Z API 3 duration: 21171 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Full Stack Engineer

CASE F

slug: full-stack-engineer · id: 15 · source: db

The primary skills involve both frontend and backend technologies, making Full Stack Engineer the best fit.

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

4
New skills
4
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

About the job
Inception, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception is committed to creating positive societal impact. For more information, please visitwww.inceptionai.ai



Role Overview

The Full Stack Engineer at Inception is responsible for designing, developing, and maintaining scalable, end-to-end software systems that enable seamless data and service integration. The role involves building high-performance front-end and back-end systems, ensuring robust architecture, and delivering reliable, production-ready applications.



This role is a full-time position based in India (remote). 



Key Responsibilities

 • Design and develop responsive, high-performance front-end applications using Next.js, translating UI/UX designs into scalable solutions

 • Build and maintain backend services and APIs using Python (FastAPI/Flask), ensuring performance, scalability, and reliability

 • Architect end-to-end solutions across frontend, backend, databases, and integration layers

 • Design, develop, and optimize RESTful APIs and service interfaces to support product functionality

 • Ensure non-functional requirements (performance, security, scalability, maintainability) are embedded in all solutions

 • Develop and manage relational and NoSQL databases, ensuring efficient data storage and retrieval

 • Contribute to system architecture decisions, including monolithic and microservices-based approaches

 • Collaborate with data scientists, applied scientists, and engineering teams to integrate AI capabilities into applications

 • Troubleshoot, debug, and resolve issues across the full technology stack

 • Participate in code reviews and enforce best practices for code quality, testing, and documentation



Qualifications

 • Bachelor’s or Master’s degree in Computer Science or related field

 • Strong experience in Python and backend frameworks (FastAPI or Flask)

 • Experience building frontend applications using Next.js / React ecosystem

 • Strong knowledge of API design and distributed systems

 • Experience with relational and NoSQL databases

 • Familiarity with Docker/Kubernetes and cloud platforms (preferably Azure)

 • Experience with version control (Git) and modern development practices



The successful candidate will be engaged via an external workforce partner in line with Inception’s operating model in the region.

Skills from this JD

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

Next.js Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Next.js id=705 · next-js

Aliases — catalog

  • Next.js (CANONICAL) primary
  • Next 10 (VERSION)
  • Next 11 (VERSION)
  • Next 12 (VERSION)
  • Next 13 (VERSION)
  • Next 14 (VERSION)
  • Next 15 (VERSION)
  • Next 2 (VERSION)
  • Next 3 (VERSION)
  • Next 4 (VERSION)
  • Next 5 (VERSION)
  • Next 6 (VERSION)
  • Next 7 (VERSION)
  • Next 8 (VERSION)
  • Next 9 (VERSION)
  • Next.js 1 (VERSION)
  • Next.js 10 (VERSION)
  • Next.js 11 (VERSION)
  • Next.js 12 (VERSION)
  • Next.js 13 (VERSION)
  • Next.js 14 (VERSION)
  • Next.js 15 (VERSION)
  • Next.js 2 (VERSION)
  • Next.js 3 (VERSION)
  • Next.js 4 (VERSION)
  • Next.js 5 (VERSION)
  • Next.js 6 (VERSION)
  • Next.js 7 (VERSION)
  • Next.js 8 (VERSION)
  • Next.js 9 (VERSION)
  • next (VERSION)
  • next.js (VERSION)
  • next.js 14 (VERSION)
  • nextjs (VERSION)
  • nextjs 14 (VERSION)

Context tags (catalog)

API routes App Router CSS-in-JS Client Components ISR JAMstack Pages Router React SSG SSR Server Components Tailwind CSS TypeScript Vercel Webpack dynamic routing getServerSideProps getStaticProps headless CMS incremental static regeneration middleware server-side rendering static generation webpack

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Vercel
License
mit
Year introduced
2016
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Next.js appears in many frontend/full-stack job descriptions and is a common React meta-framework for production apps; Vercel’s ecosystem and strong GitHub adoption signal broad market demand.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Meta-Frameworks & SSR Catalog dimension db id 130

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Meta-Frameworks & SSR
meta-frameworks-ssr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
416
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: FastAPI id=1201 · fastapi

Aliases — catalog

  • FastAPI (CANONICAL) primary

Context tags (catalog)

API documentation ASGI CORS JSON JSON Schema OAuth2 OpenAPI Pydantic RESTful Starlette UVicorn WebSocket async async programming data validation dependency injection middleware path parameters query parameters type hints uvicorn

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Web Framework
Vendor
Sebastián Ramírez
License
mit
Year introduced
2018
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: FastAPI appears in many Python backend job postings and has strong GitHub adoption; it’s now a common choice for API development alongside Flask/Django rather than a niche tool.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
35
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flask Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

Flask appears in many Python web backend job postings and remains a common lightweight framework in hiring pipelines, alongside strong GitHub usage and ecosystem activity.

Vendor & license

Pallets Projects ·bsd ·since 2010 (0.95)

Context keywords
Jinja2 Werkzeug Flask-SQLAlchemy Flask-RESTful Blueprints Flask-Migrate unit testing middleware RESTful APIs JSON session management template rendering debugging virtual environments deployment
Ambiguity low

“Flask” in JDs typically refers specifically to the Python web microframework; unlikely to be confused with other catalog skills.

Versioning

Versioned 3.x

{
  "flask 2": "2.x",
  "flask 2.x": "2.x",
  "flask 3": "3.x",
  "flask 3.x": "3.x",
  "flask2": "2.x",
  "flask3": "3.x",
  "flask\u003e=3": "3.x"
}
Type assignment

Framework ·web_framework confidence 0.99

Flask is a lightweight web framework that you build applications inside, so it fits the Framework rule rather than a Tool or Library.

Derived legacy fields
Category
Framework
Sub-category
web_framework
Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
SEPARATE_ENTITY

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Python Web Application Frameworks

    Pipeline tentative id

    Frameworks used to build server-side web applications, APIs, and request/response handling in Python. Flask belongs here because it is a Python microframework for routing, middleware, templating, and web service development.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: REST id=11 · rest

Aliases — catalog

  • REST (CANONICAL) primary

Context tags (catalog)

API API design API versioning CRUD DELETE GET HATEOAS HTTP JSON OAuth OAuth2 OpenAPI POST PUT Postman RESTful Swagger URI Webhooks XML authentication client-server content negotiation endpoint endpoints middleware resource resource-oriented serialization stateless status codes versioning web services

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Api Architecture Style
Year introduced
2000
Confidence
0.88
Version strategy
NOT_APPLICABLE

Maturity reasoning: REST is a default API architecture in many job descriptions and is widely supported by major vendors/frameworks; OpenAPI and RESTful endpoints remain standard in hiring pipelines.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
48
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Design and Contracts Catalog dimension db id 3

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Design and Contracts
api-design-and-contracts
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Databases Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.98

Relational databases remain a hiring staple across most backend/data JDs, with PostgreSQL, MySQL, and SQL Server appearing routinely; cloud vendors also center managed RDBMS offerings, signaling broad adoption.

Vendor & license

(0.90)

Context keywords
SQL PostgreSQL MySQL Oracle ACID Normalization Joins Indexes Transactions Stored Procedures Data Modeling Entity-Relationship Schema Design Database Migration Query Optimization
Ambiguity low

“Relational Databases” is a clear domain term (SQL/relational DBs) and is unlikely to be confused with other distinct catalog skills.

Versioning

Not versioned

Type assignment

Domain ·relational_database_management confidence 0.90

Relational Databases is a vertical body of knowledge about the relational data model and database systems, so it fits the Domain type rather than a specific datastore product.

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

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Relational Database Systems

    Pipeline tentative id

    Relational database systems used to store, query, and manage structured data with tables, schemas, keys, and SQL. This skill belongs here because it covers the core database technology and operational concepts behind application data storage.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.96

NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech.

Vendor & license

(0.95)

Context keywords
MongoDB Cassandra Redis DynamoDB document store key-value store column-family graph database eventual consistency sharding horizontal scaling schema-less CAP theorem data modeling query language
Ambiguity low

“NoSQL” is a widely used, specific database paradigm term; typical JDs won’t confuse it with other distinct skills in the catalog.

Versioning

Not versioned

Type assignment

Concept ·database_paradigm confidence 0.93

NoSQL is fundamentally a knowledge unit describing a class of non-relational database approaches, so it fits the Concept type rather than a specific datastore or tool.

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

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

Locked dimensions (v3 placement)

  • NoSQL Databases

    Pipeline tentative id

    Non-relational database systems used for flexible schemas, horizontal scaling, and workload-specific data access patterns. NoSQL belongs here because it refers to the database family rather than a single product or query language.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL Databases
d_init_01
New skill saved · Existing dimension (embedding dedup) · Role↔dimension skipped (dimension not under chosen role)
Microservices Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: microservices id=41 · microservices

Aliases — catalog

  • microservices (CANONICAL) primary

Context tags (catalog)

API Gateway API gateway CQRS DevOps Docker Kubernetes REST API RESTful services Saga pattern Spring Boot circuit breaker containerization decentralized distributed tracing domain-driven design event sourcing event-driven event-driven architecture gRPC load balancing message broker microservices patterns monitoring scalability service discovery service mesh

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Distributed System Architecture
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Microservices is a common architecture in job descriptions across backend/cloud roles, and major vendors like AWS, Google Cloud, and Kubernetes ecosystems provide first-class support and reference patterns.

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • Microservices and Distributed Systems Catalog dimension db id 9

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Microservices and Distributed Systems
microservices-and-distributed-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

AI appears in a large and growing share of job descriptions across software, data, and product roles; major vendors like Microsoft, Google, and AWS have broad AI offerings and hiring demand reflects mainstream adoption.

Vendor & license

(1.00)

Context keywords
machine learning neural networks deep learning natural language processing computer vision reinforcement learning TensorFlow PyTorch data preprocessing model training predictive analytics AI ethics algorithm optimization feature engineering supervised learning
Ambiguity flagged

Could be confused with: ml

“AI” in JDs is often used interchangeably with “ML” (machine learning), so an extractor may map it to the ML skill.

Versioning

Not versioned

Type assignment

Concept ·artificial_intelligence confidence 0.98

AI is fundamentally a named knowledge unit about intelligent systems, so it fits the Concept category rather than a tool, platform, or methodology.

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

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Artificial Intelligence Concepts

    Pipeline tentative id

    Core concepts, capabilities, and terminology for building or using AI systems. The skill AI is too broad to map cleanly to a narrower catalog entry, so this dimension captures the general AI knowledge base.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Code Review Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Code Review id=516 · code-review

Aliases — catalog

  • Code Review (CANONICAL)

Context tags (catalog)

Bitbucket GitHub GitLab PR review approval workflow branch protection code quality diff inline comments linting merge request pair programming pull request review checklist static analysis

Stored enrichment (catalog DB)

Category
SoftSkill
Sub-category
Code Review
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Code review is a standard hiring-pipeline requirement in engineering JDs and is built into major platforms like GitHub/GitLab pull-request workflows, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
58
Sub-category id
364
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
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
Next.js in_db
Meta-Frameworks & SSR
meta-frameworks-ssr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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 XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
REST in_db
API Design and Contracts
api-design-and-contracts
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microservices in_db
Microservices and Distributed Systems
microservices-and-distributed-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Code Review in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flask in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Databases in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL in_db
NoSQL Databases
nosql-databases
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI in_db
React Frontend Development
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL in_db
NoSQL Databases
d_init_01
New skill saved · Existing dimension (embedding dedup) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_added Flask 1344
canonical_skill_added Relational Databases 1345
canonical_skill_added NoSQL 1346
canonical_skill_added AI 1347
dimension_skill_link Flask ↔ React Frontend Development 96
dimension_skill_link Relational Databases ↔ React Frontend Development 96
dimension_skill_link NoSQL ↔ NoSQL Databases 19
dimension_skill_link AI ↔ React Frontend Development 96
nano JD Parser — gpt-4.1-nano click to toggle
RoleFull Stack Engineer
CompanyInception
DomainIT Services & Consulting
Location India (remote)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "Inception, a G42 company, is",
      "last_5_words": "creating positive societal impact."
    },
    "text": "Inception, a G42 company, is the region\u2019s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception functions as the core intelligence layer \u2013 transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception is committed to creating positive societal impact.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "Inception",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO",
        "Tech Consulting"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
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    "min": null,
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}
API 1 — extract-from-jd click to toggle
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      "skill_name": "Relational Databases"
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    {
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    "llm2_reasoning": "The full stack web responsibilities (Next.js front-end and Python back-end) don\u2019t align with AR/VR, and Android development at least shares front-end app development aspects, making android-engineer the closer match.",
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}
API 2 — extract-details
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        "sub_category_id": 48,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "API Design and Contracts",
            "id": 3,
            "rationale": "Designing and maintaining service interfaces that other systems consume. Covers endpoint shape, payload modeling, versioning, pagination, idempotency, and compatibility rules.",
            "slug": "api-design-and-contracts",
            "source": "db"
          },
          "input_skill": "REST",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "REST",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Relational Databases",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Relational Databases",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Domain",
          "skill_nature": "CONCEPT",
          "sub_category": "relational_database_management",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cRelational Databases\u201d is a clear domain term (SQL/relational DBs) and is unlikely to be confused with other distinct catalog skills."
          },
          "context_keywords": {
            "context_keywords": [
              "SQL",
              "PostgreSQL",
              "MySQL",
              "Oracle",
              "ACID",
              "Normalization",
              "Joins",
              "Indexes",
              "Transactions",
              "Stored Procedures",
              "Data Modeling",
              "Entity-Relationship",
              "Schema Design",
              "Database Migration",
              "Query Optimization"
            ]
          },
          "maturity": {
            "confidence": 0.98,
            "maturity": "well_known",
            "reasoning": "Relational databases remain a hiring staple across most backend/data JDs, with PostgreSQL, MySQL, and SQL Server appearing routinely; cloud vendors also center managed RDBMS offerings, signaling broad adoption."
          },
          "skill_id": "relational-databases",
          "vendor_license": {
            "confidence": 0.9,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Relational database systems used to store, query, and manage structured data with tables, schemas, keys, and SQL. This skill belongs here because it covers the core database technology and operational concepts behind application data storage.",
            "exemplar_skills": [
              "Relational Databases",
              "SQL",
              "PostgreSQL",
              "MySQL",
              "SQLite",
              "Database indexing",
              "Database transactions",
              "Schema design"
            ],
            "in_scope": "Relational Databases, SQL, tables, schemas, primary keys, foreign keys, indexes, transactions, normalization, PostgreSQL, MySQL, SQL Server, SQLite",
            "name": "Relational Database Systems",
            "out_of_scope": "NoSQL document stores, key-value caches, message queues, and search engines; those belong to separate data storage or infrastructure dimensions.",
            "overlap_flags": [
              {
                "reason": "Both can involve storing application data, but this dimension is about on-device persistence rather than server-side relational databases.",
                "with_dim_id": "local-persistence-and-offline-behavior",
                "with_dim_name": null,
                "with_role": "Android Engineer, Hybrid Mobile Developer, Ios engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Relational Databases",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "relational-databases"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "managed-databases",
            "sqlite",
            "vector-db",
            "firebase-firestore",
            "repository-pattern",
            "apis",
            "rag",
            "hybrid-search"
          ],
          "requires": [],
          "skill_id": "relational-databases",
          "suppress_on_match": []
        },
        "skill_id": "relational-databases",
        "split_log": [],
        "typed": {
          "alternatives_considered": [
            "Datastore: ruled out \u2014 the skill name is a category of databases, not one specific system that persists data.",
            "Concept: ruled out \u2014 it is broader than a single knowledge unit and denotes an entire problem-space/domain."
          ],
          "confidence": 0.9,
          "name": "Relational Databases",
          "reasoning": "Relational Databases is a vertical body of knowledge about the relational data model and database systems, so it fits the Domain type rather than a specific datastore product.",
          "skill_id": "relational-databases",
          "subtype": "relational_database_management",
          "type": "Domain"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "NoSQL Databases",
            "id": 19,
            "rationale": "Models and manages data using non-relational database systems.",
            "slug": "nosql-databases",
            "source": "db"
          },
          "input_skill": "NoSQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "NoSQL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "database_paradigm",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": false,
            "confused_with": [],
            "reasoning": "\u201cNoSQL\u201d is a widely used, specific database paradigm term; typical JDs won\u2019t confuse it with other distinct skills in the catalog."
          },
          "context_keywords": {
            "context_keywords": [
              "MongoDB",
              "Cassandra",
              "Redis",
              "DynamoDB",
              "document store",
              "key-value store",
              "column-family",
              "graph database",
              "eventual consistency",
              "sharding",
              "horizontal scaling",
              "schema-less",
              "CAP theorem",
              "data modeling",
              "query language"
            ]
          },
          "maturity": {
            "confidence": 0.96,
            "maturity": "well_known",
            "reasoning": "NoSQL is broadly listed in job descriptions across backend/data roles, with MongoDB, DynamoDB, and Cassandra appearing as common market signals; it remains a hiring-pipeline staple rather than a niche or sunset tech."
          },
          "skill_id": "nosql",
          "vendor_license": {
            "confidence": 0.95,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Non-relational database systems used for flexible schemas, horizontal scaling, and workload-specific data access patterns. NoSQL belongs here because it refers to the database family rather than a single product or query language.",
            "exemplar_skills": [
              "NoSQL",
              "MongoDB",
              "Cassandra",
              "DynamoDB",
              "Redis",
              "Couchbase",
              "document database design",
              "wide-column modeling"
            ],
            "in_scope": "NoSQL, document databases, key-value stores, wide-column databases, graph databases, MongoDB, Cassandra, DynamoDB, Redis persistence, Couchbase, schema-less modeling, partitioning, replication, eventual consistency",
            "name": "NoSQL Databases",
            "out_of_scope": "SQL relational databases, ORM usage, data warehousing, stream processing, in-memory caching without persistence, which belong to other database or data platform dimensions",
            "overlap_flags": [
              {
                "reason": "Managed NoSQL services are often delivered through cloud platforms, but the database model itself is the core skill here.",
                "with_dim_id": "cloud-platforms",
                "with_dim_name": null,
                "with_role": "Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, ML Engineer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "NoSQL",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "nosql"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "vector-db",
            "firebase-firestore",
            "sqlite",
            "managed-databases",
            "rds",
            "databricks",
            "kubernetes",
            "microsoft-azure"
          ],
          "requires": [],
          "skill_id": "nosql",
          "suppress_on_match": []
        },
        "skill_id": "nosql",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.93,
          "name": "NoSQL",
          "reasoning": "NoSQL is fundamentally a knowledge unit describing a class of non-relational database approaches, so it fits the Concept type rather than a specific datastore or tool.",
          "skill_id": "nosql",
          "subtype": "database_paradigm",
          "type": "Concept"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "microservices",
          "alias_type": "CANONICAL",
          "id": 178,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "microservices",
        "id": 41,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "microservices",
        "sub_category_id": 1,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Microservices and Distributed Systems",
            "id": 9,
            "rationale": "Architectural patterns for decomposed backend systems and the operational concerns they introduce. Covers service boundaries, consistency tradeoffs, retries, circuit breakers, and distributed coordination.",
            "slug": "microservices-and-distributed-systems",
            "source": "db"
          },
          "input_skill": "Microservices",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Microservices",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "AI",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "AI",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "artificial_intelligence",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": true,
            "confused_with": [
              "ml"
            ],
            "reasoning": "\u201cAI\u201d in JDs is often used interchangeably with \u201cML\u201d (machine learning), so an extractor may map it to the ML skill."
          },
          "context_keywords": {
            "context_keywords": [
              "machine learning",
              "neural networks",
              "deep learning",
              "natural language processing",
              "computer vision",
              "reinforcement learning",
              "TensorFlow",
              "PyTorch",
              "data preprocessing",
              "model training",
              "predictive analytics",
              "AI ethics",
              "algorithm optimization",
              "feature engineering",
              "supervised learning"
            ]
          },
          "maturity": {
            "confidence": 0.93,
            "maturity": "well_known",
            "reasoning": "AI appears in a large and growing share of job descriptions across software, data, and product roles; major vendors like Microsoft, Google, and AWS have broad AI offerings and hiring demand reflects mainstream adoption."
          },
          "skill_id": "ai",
          "vendor_license": {
            "confidence": 1.0,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [],
        "locked_dimensions": [
          {
            "description": "Core concepts, capabilities, and terminology for building or using AI systems. The skill AI is too broad to map cleanly to a narrower catalog entry, so this dimension captures the general AI knowledge base.",
            "exemplar_skills": [
              "AI",
              "machine learning",
              "generative AI",
              "model inference",
              "prompt engineering"
            ],
            "in_scope": "AI, machine learning basics, model capabilities and limits, supervised and unsupervised learning, inference concepts, generative AI concepts, prompt-based interaction, evaluation basics",
            "name": "Artificial Intelligence Concepts",
            "out_of_scope": "Specific ML frameworks and libraries, distributed training infrastructure, MLOps pipelines, AI governance/compliance, cloud platform services",
            "overlap_flags": [
              {
                "reason": "Practical implementation of AI often overlaps with model-building libraries and training workflows.",
                "with_dim_id": "ml-frameworks-and-libraries",
                "with_dim_name": null,
                "with_role": "ML Engineer"
              },
              {
                "reason": "Operationalizing AI models can involve automated training, testing, and deployment pipelines.",
                "with_dim_id": "ci-cd-for-machine-learning",
                "with_dim_name": null,
                "with_role": "ML Engineer"
              },
              {
                "reason": "AI solutions may require governance review depending on the use case and risk profile.",
                "with_dim_id": "ai-use-case-compliance-review",
                "with_dim_name": null,
                "with_role": "AI Compliance Officer"
              }
            ],
            "tentative_id": "d_init_01"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "AI",
          "placement_confidence": 0.92,
          "primary_dimension": "d_init_01",
          "reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [],
          "skill_id": "ai"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "intelligent-automation",
            "algorithms",
            "chatbots",
            "virtual-assistants",
            "openai",
            "anthropic",
            "faiss",
            "crewai"
          ],
          "requires": [],
          "skill_id": "ai",
          "suppress_on_match": []
        },
        "skill_id": "ai",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.98,
          "name": "AI",
          "reasoning": "AI is fundamentally a named knowledge unit about intelligent systems, so it fits the Concept category rather than a tool, platform, or methodology.",
          "skill_id": "ai",
          "subtype": "artificial_intelligence",
          "type": "Concept"
        },
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Code Review",
          "alias_type": "CANONICAL",
          "id": 864,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 58,
        "display_name": "Code Review",
        "id": 516,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PRACTICE",
        "slug": "code-review",
        "sub_category_id": 364,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Code Review",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Code Review",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Flask",
    "Relational Databases",
    "NoSQL",
    "AI"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Full Stack Engineer",
    "id": 15,
    "rationale": "The primary skills involve both frontend and backend technologies, making Full Stack Engineer the best fit.",
    "role_archetype": "Engineering role focused on both frontend and backend development.",
    "slug": "full-stack-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Next.js",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "Flask",
      "tag": "new"
    },
    {
      "skill": "REST",
      "tag": "in_db"
    },
    {
      "skill": "Relational Databases",
      "tag": "new"
    },
    {
      "skill": "NoSQL",
      "tag": "new"
    },
    {
      "skill": "Microservices",
      "tag": "in_db"
    },
    {
      "skill": "AI",
      "tag": "new"
    },
    {
      "skill": "Code Review",
      "tag": "in_db"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 15,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Meta-Frameworks \u0026 SSR",
          "id": 130,
          "rationale": "Frameworks that build on UI libraries to provide routing, server-side rendering, and static site generation.",
          "slug": "meta-frameworks-ssr",
          "source": "db"
        },
        "dimension_id": 130,
        "input_skill": "Next.js",
        "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": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "frontend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 705,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 15,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Core server-side languages used to implement backend business logic, integrations, and service internals. This is the primary coding surface for the role across application layers.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "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": "Backend Engineer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 15,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "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": 5,
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}

LLM Calls

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

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