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Pipeline run

f5d39ec3-8e2f-4f12-9201-c9834fd60ec9

Pipeline LLM cost (USD)
API 1: $0.0085 API 2: $0.0004 API 3: $0.0000 Total: $0.0089

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD
Nature of work · GenAI application engineering
Build and ship enterprise GenAI features in Python/FastAPI, wiring ChatGPT/Claude Sonnet through LangChain/Vercel AI SDK, plus retrieval pipelines, MCP servers, and function-calling tools.
"Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers."
Tech stack maturity
Modern Cloud Native
The skill set centers on current GenAI application patterns like function-calling, LangChain, and pgvector, which are characteristic of modern cloud-native AI engineering.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
4.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
Builds AI products
"Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers."
vocab breakdown (legacy)
Assistants (×1): ChatGPT, Claude, Cursor, Windsurf
Frameworks (×2): LangChain, Hugging Face, pgvector, LangGraph, Vercel AI SDK, Pydantic AI, Replicate, Modal, Groq, LanceDB
Models / concepts (×3): Claude Sonnet, Mistral, Anthropic, OpenAI, agentic workflow, agentic, function calling, tool use, MCP, AI, GenAI
Evidence — skills matched in JD (22)
ChatGPT Claude Sonnet LangChain Vercel AI SDK Hugging Face Hub pgvector LanceDB Windsurf Cursor MCP Function Calling LangGraph Pydantic AI OpenAI Anthropic Mistral Groq Modal Replicate Python FastAPI Docker
Skill cluster (4 dimension groups, role-scoped)
Containerization and Image Builds
Docker
Python Programming
Python
Web Application Frameworks
FastAPI
Cross-cutting / unaligned
ChatGPT Claude Sonnet LangChain Vercel AI SDK Hugging Face Hub pgvector LanceDB Windsurf Cursor MCP Function Calling LangGraph Pydantic AI OpenAI Anthropic Mistral Groq Modal Replicate
Show KRA description ↓
- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers. - Use Lang Chain and Vercel AI SDK for orchestration and tool use. - Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB. - Use Windsurf and Cursor for AI-assisted coding. - Implement MCP servers and function-calling tools for our agentic workflow. LangChain, LangGraph, Pydantic AI, OpenAI, Anthropic, Mistral, Groq, Modal, Replicate Python, FastAPI, Docker

Signals

Skill ai-engineer
0.27
Alias ai-engineer
1.00
KRA ai-engineer
0.51

Post-classification

Centroidupdated · n=19
Alias collision log
New-role queue
New skills captured12
New KRA capturedyes

Captured for admin review

ChatGPT primary LLM / GenAI Engineer pending
Claude Sonnet primary LLM / GenAI Engineer pending
Vercel AI SDK primary LLM / GenAI Engineer pending
Hugging Face Hub primary LLM / GenAI Engineer pending
LanceDB primary LLM / GenAI Engineer pending
Windsurf primary LLM / GenAI Engineer pending
MCP primary LLM / GenAI Engineer pending
Pydantic AI LLM / GenAI Engineer pending
Mistral LLM / GenAI Engineer pending
Groq LLM / GenAI Engineer pending
Modal LLM / GenAI Engineer pending
Replicate LLM / GenAI Engineer pending
R&R fragment (sim 0.00) LLM / GenAI Engineer pending

- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers. - Use Lang Chain and Vercel AI SDK for orchestration and tool use. - Build retrieval pipelines on Hugging-Face hub,…

Status: completed Created: 2026-06-04T16:33:30.455098Z Updated: 2026-06-04T16:34:06.957971Z API 3 duration: 5061 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

LLM / GenAI Engineer

domain · AI / ML CASE DOMAIN

slug: llm-genai-engineer · id: 151 · source: db

Domain=AI / ML; The JD is centered on shipping GenAI/agentic application features, orchestration, RAG retrieval pipelines, and function-calling/MCP tooling, which best matches an LLM/GenAI Engineer.

Matched skills

Chat GPTClaude SonnetLang ChainVercel AI SDKHugging-Face hubpgvectorLanceDBWindsurfCursorMCP serversfunction-callingLangChainLangGraphPydantic AIOpenAIAnthropicMistralGroqModalReplicate

Matched dimensions

GenAI application developmentLLM orchestration and tool useRetrieval-augmented generation pipelinesAgentic workflow implementationEnterprise AI feature delivery

Matched KRAs

Ship GenAI features powered by Chat GPT and Claude SonnetUse Lang Chain and Vercel AI SDK for orchestrationBuild retrieval pipelines on Hugging-Face hub, pgvector, and LanceDBImplement MCP servers and function-calling toolsSupport enterprise customers

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

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

AI Application Engineer

About:
We are an AI-first startup building agentic products.

Responsibilities:
- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.
- Use Lang Chain and Vercel AI SDK for orchestration and tool use.
- Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.
- Use Windsurf and Cursor for AI-assisted coding.
- Implement MCP servers and function-calling tools for our agentic workflow.

Required Skills:
LangChain, LangGraph, Pydantic AI, OpenAI, Anthropic, Mistral, Groq, Modal, Replicate
Python, FastAPI, Docker

Skills from this JD

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

ChatGPT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Claude Sonnet Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
LangChain Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangChain id=240 · langchain

Aliases — catalog

  • LangChain (CANONICAL) primary

Context tags (catalog)

API integration Hugging Face LLM LLMs OpenAI RAG agents callbacks chains data augmentation deployment document loaders embeddings fine-tuning memory prompt engineering prompt templates prompts retrieval retrievers state management streaming text splitters toolkits tools vector database vector stores

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Llm Application Framework
Vendor
Harrison Chase
License
mit
Year introduced
2022
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
146
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • LLM Operations and Orchestration Catalog dimension db id 49

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vercel AI SDK Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Web Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Hugging Face Hub Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hugging Face id=1189 · hugging-face

Aliases — catalog

  • Hugging Face (CANONICAL)

Context tags (catalog)

AutoModel BERT Datasets Fine-tuning GPT-2 Hugging Face Spaces Inference API Model Hub NLU Pipelines Pre-trained models T5 Tokenizers Trainer Transformers

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ai Model Hub Platform
Vendor
Hugging Face
License
apache_2
Year introduced
2016
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Hugging Face appears in many ML/LLM job descriptions and has strong GitHub adoption; its Transformers ecosystem is a common default for model sharing and fine-tuning.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
899
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
pgvector Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: pgvector id=1246 · pgvector

Aliases — catalog

  • pgvector (CANONICAL) primary

Context tags (catalog)

AI integration JSONB PostgreSQL data analytics data retrieval database extension embedding full-text search high-dimensional data indexing machine learning nearest neighbors query optimization similarity search vector search

Stored enrichment (catalog DB)

Category
Library
Sub-category
Database Extension Library
Vendor
ZomboDB
License
mit
Year introduced
2021
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in growing numbers of JDs for AI search/RAG roles, but remains a PostgreSQL extension rather than a universal database skill; GitHub adoption is rising yet still far below core DB tech.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
972
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Vector Databases Catalog dimension db id 198

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LanceDB Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Databases
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Windsurf Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Cursor Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Cursor id=1589 · cursor

Aliases — catalog

  • Cursor (CANONICAL)

Context tags (catalog)

AI suggestions cloud integration code completion code snippets custom themes debugging tools intelligent refactoring keyboard shortcuts language support plugin ecosystem project management real-time collaboration syntax highlighting user interface version control

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ai Code Editor
Vendor
Cursor
License
unknown
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Cursor is increasingly appearing in developer job posts and AI-tooling stacks, but it is not yet a universal hiring staple like VS Code or GitHub Copilot.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1195
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)
MCP Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
CREDENTIAL
Sub-category
general
Skill nature
CREDENTIAL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Function Calling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: function calling id=1278 · function-calling

Aliases — catalog

  • function calling (CANONICAL) primary

Context tags (catalog)

argument passing asynchronous callback closure event-driven first-class function function signature functional programming higher-order function lambda method invocation recursion return value scope variable arguments

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Function Calling Concept
Confidence
0.70
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears increasingly in LLM vendor docs and job postings for AI app roles, but is still far from universal compared with core backend skills.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
943
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Structured Output Integration Catalog dimension db id 204

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Structured Output Integration
structured-output-integration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LangGraph Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangGraph id=1253 · langgraph

Aliases — catalog

  • LangGraph (CANONICAL) primary

Context tags (catalog)

API integration agent-based contextual understanding data visualization dialog management entity extraction graph traversal intent recognition knowledge graph machine learning multi-turn dialogue natural language processing semantic web state management user intent

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Agent Framework
Vendor
LangGraph Team
License
mit
Year introduced
2023
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: LangGraph is increasingly appearing in AI/agent job descriptions and sits on top of the fast-growing LangChain ecosystem, but it is not yet a universal hiring staple.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
969
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Agentic Frameworks Catalog dimension db id 200

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Agentic Frameworks
agentic-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pydantic AI Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
OpenAI Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: OpenAI id=1186 · openai

Aliases — catalog

  • OpenAI (CANONICAL)

Context tags (catalog)

AI ethics API ChatGPT Codex DALL-E GPT-3 data augmentation fine-tuning machine learning model training natural language processing neural networks prompt engineering reinforcement learning transformer

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ai Platform
Vendor
OpenAI
License
other_open
Year introduced
2015
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: OpenAI appears in a growing number of job postings for LLM/app integration, but it is not yet a universal baseline skill like AWS or Python; market demand is rising alongside API adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
896
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)
Anthropic Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Anthropic id=1188 · anthropic

Aliases — catalog

  • Anthropic (CANONICAL)

Context tags (catalog)

AI safety Claude alignment data privacy ethical AI fine-tuning human-in-the-loop machine learning model interpretability multi-modal natural language processing prompt engineering reinforcement learning scalability transformers

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ai Model Platform
Vendor
Anthropic
License
unknown
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Anthropic/Claude is increasingly listed in AI engineer JDs and vendor docs, but it is not yet as universal as AWS/OpenAI; GitHub and job-market signals show rapid growth rather than saturation.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
898
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)
Mistral Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Groq Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Modal Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Replicate Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Python Secondary 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
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security 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, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
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)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
FastAPI Secondary 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)

  • Web Application Frameworks Catalog dimension db id 2

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer

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)
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=61 · docker

Aliases — catalog

  • Docker (CANONICAL) primary

Context tags (catalog)

CI/CD Compose DevOps Docker Compose Docker Swarm Dockerfile Kubernetes build pipeline container container lifecycle container orchestration container registry container security containerization containers image image registry images immutable infrastructure lightweight virtualization microservices networking orchestration port mapping registry scalability service discovery swarm volume volume management

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Containerization Tool
Vendor
Docker, Inc.
License
apache_2
Year introduced
2013
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Docker is a hiring-pipeline staple: it appears in many DevOps, backend, and platform JDs, and remains a standard containerization tool alongside Kubernetes in production stacks.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
63
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Containerization and Image Builds Catalog dimension db id 152

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Deployment and Cloud Platforms Catalog dimension db id 418

    Library dimension (catalog)

    Roles linked in library: Ruby Backend Developer

  • Deployment and Runtime Configuration Catalog dimension db id 13

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, PHP Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Runtime Configuration
deployment-and-runtime-configuration
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
LangChain in_db
LLM Operations and Orchestration
llm-operations-and-orchestration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hugging Face Hub new
ML Frameworks and Libraries
ml-frameworks-and-libraries
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Hugging Face Hub new
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
pgvector in_db
Vector Databases
vector-databases
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cursor in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Function Calling in_db
Structured Output Integration
structured-output-integration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
LangGraph in_db
Agentic Frameworks
agentic-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenAI in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Anthropic in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
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 & DSLs
programming-languages-dsls
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)
Python in_db
Python Programming
python-programming
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)
FastAPI in_db
Web Application Frameworks
web-application-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Containerization and Image Builds
containerization-and-image-builds
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Cloud Platforms
deployment-and-cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Deployment and Runtime Configuration
deployment-and-runtime-configuration
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ChatGPT | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Claude Sonnet | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Vercel AI SDK | type=Web Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed LanceDB | type=Databases subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Windsurf | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed MCP | type=CREDENTIAL subtype=general nature=CREDENTIAL lifespan=MULTI_YEAR
canonical_skill_proposed Pydantic AI | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Mistral | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Groq | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Modal | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
canonical_skill_proposed Replicate | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED
dimension_skill_link_proposed Hugging Face Hub ↔ ML Frameworks and Libraries
dimension_skill_link_proposed Hugging Face Hub ↔ React Frontend Development
nano JD Parser — gpt-4.1-nano click to toggle
RoleAI Application Engineer
DomainSoftware & SaaS Products
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "We are an AI-first startup",
      "last_5_words": "building agentic products."
    },
    "text": "We are an AI-first startup building agentic products.",
    "word_count": 10
  },
  "ai_kras": [
    "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
    "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
    "Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.",
    "Use Windsurf and Cursor for AI-assisted coding.",
    "Implement MCP servers and function-calling tools for our agentic workflow."
  ],
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "SaaS",
        "Product Companies"
      ],
      "domain": "Software \u0026 SaaS Products"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [],
  "role": "AI Application Engineer",
  "role_aliases": [
    "AI Engineer",
    "Machine Learning Engineer",
    "Software Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 5,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Ship GenAI features powered by",
        "last_5_words": "for our agentic workflow."
      },
      "text": "- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.\n- Use Lang Chain and Vercel AI SDK for orchestration and tool use.\n- Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.\n- Use Windsurf and Cursor for AI-assisted coding.\n- Implement MCP servers and function-calling tools for our agentic workflow.",
      "word_count": 47
    },
    {
      "bullet_count": 0,
      "heading": "Required Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "LangChain, LangGraph, Pydantic AI,",
        "last_5_words": "Python, FastAPI, Docker"
      },
      "text": "LangChain, LangGraph, Pydantic AI, OpenAI, Anthropic, Mistral, Groq, Modal, Replicate\nPython, FastAPI, Docker",
      "word_count": 22
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "ChatGPT"
    },
    {
      "is_primary": true,
      "skill_name": "Claude Sonnet"
    },
    {
      "is_primary": true,
      "skill_name": "LangChain"
    },
    {
      "is_primary": true,
      "skill_name": "Vercel AI SDK"
    },
    {
      "is_primary": true,
      "skill_name": "Hugging Face Hub"
    },
    {
      "is_primary": true,
      "skill_name": "pgvector"
    },
    {
      "is_primary": true,
      "skill_name": "LanceDB"
    },
    {
      "is_primary": true,
      "skill_name": "Windsurf"
    },
    {
      "is_primary": true,
      "skill_name": "Cursor"
    },
    {
      "is_primary": true,
      "skill_name": "MCP"
    },
    {
      "is_primary": true,
      "skill_name": "Function Calling"
    },
    {
      "is_primary": false,
      "skill_name": "LangGraph"
    },
    {
      "is_primary": false,
      "skill_name": "Pydantic AI"
    },
    {
      "is_primary": false,
      "skill_name": "OpenAI"
    },
    {
      "is_primary": false,
      "skill_name": "Anthropic"
    },
    {
      "is_primary": false,
      "skill_name": "Mistral"
    },
    {
      "is_primary": false,
      "skill_name": "Groq"
    },
    {
      "is_primary": false,
      "skill_name": "Modal"
    },
    {
      "is_primary": false,
      "skill_name": "Replicate"
    },
    {
      "is_primary": false,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "FastAPI"
    },
    {
      "is_primary": false,
      "skill_name": "Docker"
    }
  ],
  "jd_role": {
    "display_name": "AI Application Engineer",
    "rationale": null,
    "role_aliases": [
      "AI Engineer",
      "Machine Learning Engineer",
      "Software Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "We are an AI-first startup",
        "last_5_words": "building agentic products."
      },
      "text": "We are an AI-first startup building agentic products.",
      "word_count": 10
    },
    "ai_kras": [
      "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
      "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
      "Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.",
      "Use Windsurf and Cursor for AI-assisted coding.",
      "Implement MCP servers and function-calling tools for our agentic workflow."
    ],
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "SaaS",
          "Product Companies"
        ],
        "domain": "Software \u0026 SaaS Products"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": null,
      "raw": null
    },
    "job_locations": [],
    "role": "AI Application Engineer",
    "role_aliases": [
      "AI Engineer",
      "Machine Learning Engineer",
      "Software Engineer"
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 5,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Ship GenAI features powered by",
          "last_5_words": "for our agentic workflow."
        },
        "text": "- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.\n- Use Lang Chain and Vercel AI SDK for orchestration and tool use.\n- Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.\n- Use Windsurf and Cursor for AI-assisted coding.\n- Implement MCP servers and function-calling tools for our agentic workflow.",
        "word_count": 47
      },
      {
        "bullet_count": 0,
        "heading": "Required Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "LangChain, LangGraph, Pydantic AI,",
          "last_5_words": "Python, FastAPI, Docker"
        },
        "text": "LangChain, LangGraph, Pydantic AI, OpenAI, Anthropic, Mistral, Groq, Modal, Replicate\nPython, FastAPI, Docker",
        "word_count": 22
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "f5d39ec3-8e2f-4f12-9201-c9834fd60ec9",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "AI Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 1.0,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 1.0,
        "slug": "ml-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into AI-powered features by integrating large language models like GPT-4, Claude, or Gemini into application workflows via API.",
            "sentence": "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
            "similarity": 0.6036
          },
          {
            "kra_text": "Integrates AI model API responses with application business logic, database writes, event publishing, and downstream service orchestration.",
            "sentence": "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
            "similarity": 0.5037
          },
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
            "sentence": "Implement MCP servers and function-calling tools for our agentic workflow.",
            "similarity": 0.4076
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.505,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
            "similarity": 0.444
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Implement MCP servers and function-calling tools for our agentic workflow.",
            "similarity": 0.4142
          },
          {
            "kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
            "sentence": "Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.",
            "similarity": 0.3751
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.4111,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
            "sentence": "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
            "similarity": 0.4218
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB.",
            "similarity": 0.3998
          },
          {
            "kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
            "sentence": "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
            "similarity": 0.3812
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.4009,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Scala Backend Developer",
        "kra_matches": [
          {
            "kra_text": "backend workflow orchestration",
            "sentence": "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
            "similarity": 0.443
          },
          {
            "kra_text": "backend workflow orchestration",
            "sentence": "Implement MCP servers and function-calling tools for our agentic workflow.",
            "similarity": 0.4082
          },
          {
            "kra_text": "Server-side feature implementation",
            "sentence": "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
            "similarity": 0.3501
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 87,
        "score": 0.4004,
        "slug": "scala-backend-developer",
        "total_count": null
      },
      {
        "display_name": "AI Compliance Officer",
        "kra_matches": [
          {
            "kra_text": "Maps AI system behaviors and data processing activities to regulatory requirements including EU AI Act, GDPR, CCPA, and sector-specific compliance frameworks.",
            "sentence": "Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.",
            "similarity": 0.4127
          },
          {
            "kra_text": "Manages AI deployment approval workflows, periodic reassessment calendars, and conditional authorization records for production AI systems.",
            "sentence": "Implement MCP servers and function-calling tools for our agentic workflow.",
            "similarity": 0.3868
          },
          {
            "kra_text": "Manages AI deployment approval workflows, periodic reassessment calendars, and conditional authorization records for production AI systems.",
            "sentence": "Use Lang Chain and Vercel AI SDK for orchestration and tool use.",
            "similarity": 0.3861
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 12,
        "score": 0.3952,
        "slug": "ai-compliance-officer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "AI Engineer",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "LangChain",
          "function calling",
          "pgvector"
        ],
        "role_id": 13,
        "score": 0.2727,
        "slug": "ai-engineer",
        "total_count": 11
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "LangChain"
        ],
        "role_id": 3,
        "score": 0.0909,
        "slug": "ml-engineer",
        "total_count": 11
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "LangChain"
        ],
        "role_id": 16,
        "score": 0.0909,
        "slug": "ml-ops-engineer",
        "total_count": 11
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "LLM / GenAI Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 151,
      "score": 0.98,
      "slug": "llm-genai-engineer",
      "total_count": null
    },
    "confidence": 0.98,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "GenAI application development",
      "LLM orchestration and tool use",
      "Retrieval-augmented generation pipelines",
      "Agentic workflow implementation",
      "Enterprise AI feature delivery"
    ],
    "matched_kras": [
      "Ship GenAI features powered by Chat GPT and Claude Sonnet",
      "Use Lang Chain and Vercel AI SDK for orchestration",
      "Build retrieval pipelines on Hugging-Face hub, pgvector, and LanceDB",
      "Implement MCP servers and function-calling tools",
      "Support enterprise customers"
    ],
    "matched_skills": [
      "Chat GPT",
      "Claude Sonnet",
      "Lang Chain",
      "Vercel AI SDK",
      "Hugging-Face hub",
      "pgvector",
      "LanceDB",
      "Windsurf",
      "Cursor",
      "MCP servers",
      "function-calling",
      "LangChain",
      "LangGraph",
      "Pydantic AI",
      "OpenAI",
      "Anthropic",
      "Mistral",
      "Groq",
      "Modal",
      "Replicate"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=AI / ML; The JD is centered on shipping GenAI/agentic application features, orchestration, RAG retrieval pipelines, and function-calling/MCP tooling, which best matches an LLM/GenAI Engineer.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 19,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.0,
      "queue_id": 2024,
      "r_and_r_preview": "- Ship GenAI features powered by Chat GPT and Claude Sonnet to enterprise customers.\n- Use Lang Chain and Vercel AI SDK for orchestration and tool use.\n- Build retrieval pipelines on Hugging-Face hub,",
      "role_display_name": "LLM / GenAI Engineer",
      "role_slug": "llm-genai-engineer",
      "status": "pending"
    },
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 25814,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "ChatGPT",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25815,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Claude Sonnet",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25816,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Vercel AI SDK",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25817,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Hugging Face Hub",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25818,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "LanceDB",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25819,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Windsurf",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 25820,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "MCP",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 25821,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Pydantic AI",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 25822,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Mistral",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 25823,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Groq",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 25824,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Modal",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 25825,
        "role_display_name": "LLM / GenAI Engineer",
        "role_slug": "llm-genai-engineer",
        "skill_name": "Replicate",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
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      "alias_persisted": false,
      "existing_alias_id": 501,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "langchain",
        "sub_category_id": 146,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 1825,
      "existing_alias_text": "Hugging Face",
      "input_term": "Hugging Face Hub",
      "matched_canonical": {
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        "display_name": "Hugging Face",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "hugging-face",
        "sub_category_id": 899,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1882,
      "existing_alias_text": "pgvector",
      "input_term": "pgvector",
      "matched_canonical": {
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        "display_name": "pgvector",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LIBRARY",
        "slug": "pgvector",
        "sub_category_id": 972,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2535,
      "existing_alias_text": "Cursor",
      "input_term": "Cursor",
      "matched_canonical": {
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        "display_name": "Cursor",
        "id": 1589,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "sub_category_id": 1195,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1914,
      "existing_alias_text": "function calling",
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      "matched_canonical": {
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        "display_name": "function calling",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "sub_category_id": 943,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1889,
      "existing_alias_text": "LangGraph",
      "input_term": "LangGraph",
      "matched_canonical": {
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        "display_name": "LangGraph",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
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        "sub_category_id": 969,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1822,
      "existing_alias_text": "OpenAI",
      "input_term": "OpenAI",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "openai",
        "sub_category_id": 896,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1824,
      "existing_alias_text": "Anthropic",
      "input_term": "Anthropic",
      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
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        "sub_category_id": 898,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1837,
      "existing_alias_text": "FastAPI",
      "input_term": "FastAPI",
      "matched_canonical": {
        "category_id": 5,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "fastapi",
        "sub_category_id": 35,
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        "volatility": "STABLE"
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      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 198,
      "existing_alias_text": "Docker",
      "input_term": "Docker",
      "matched_canonical": {
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        "display_name": "Docker",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "docker",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
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      "matched_via": "alias"
    }
  ],
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      "slug": "ai-engineer",
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      "slug": "ml-engineer",
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    },
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      "slug": "ml-ops-engineer",
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    },
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      "display_name": "Cloud Security Engineer",
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      "slug": "cloud-security-engineer",
      "source": "db"
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      "id": 435,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "fullstack-developer",
      "source": "db"
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      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
    },
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      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
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      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
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      "display_name": "Data Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
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      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
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      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
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      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
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      "display_name": "PHP Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
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      "display_name": "DevOps Engineer",
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      "rationale": null,
      "role_archetype": null,
      "slug": "devops-engineer",
      "source": "db"
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      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
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      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
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      "source": "db"
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      "display_name": "Go Backend Developer",
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      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "go-backend-developer",
      "source": "db"
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  "chosen_role": {
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    "rationale": "Domain=AI / ML; The JD is centered on shipping GenAI/agentic application features, orchestration, RAG retrieval pipelines, and function-calling/MCP tooling, which best matches an LLM/GenAI Engineer.",
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    "slug": "llm-genai-engineer",
    "source": "db"
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      "dimension": {
        "difficulty_hint": "well_known",
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        "source": "db"
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      "input_skill": "LangChain",
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      "roles_from_db": [
        {
          "display_name": "AI Engineer",
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          "rationale": null,
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          "slug": "ml-ops-engineer",
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    },
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        "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.",
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      },
      "input_skill": "Hugging Face Hub",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Vector Databases",
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        "rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
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      },
      "input_skill": "pgvector",
      "llm_role": null,
      "roles_from_db": [
        {
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        "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": "Cursor",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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      "input_skill": "Function Calling",
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          "display_name": "AI Engineer",
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        "source": "db"
      },
      "input_skill": "LangGraph",
      "llm_role": null,
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        {
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        "slug": "d_init_01",
        "source": "db"
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      "input_skill": "OpenAI",
      "llm_role": null,
      "roles_from_db": []
    },
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      "input_skill": "Anthropic",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
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        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
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          "rationale": null,
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          "display_name": "Fullstack Developer",
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        "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
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        "source": "db"
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          "rationale": null,
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    },
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        "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.",
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      "input_skill": "Python",
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        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
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        "source": "db"
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      "input_skill": "Python",
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      "roles_from_db": [
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          "slug": "data-engineer",
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    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
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        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
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        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "ML Engineer",
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          "slug": "ml-ops-engineer",
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        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for XR",
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        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
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        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
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              "display_name": "Backend Developer",
              "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"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Docker",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ChatGPT",
    "Claude Sonnet",
    "Vercel AI SDK",
    "LanceDB",
    "Windsurf",
    "MCP",
    "Pydantic AI",
    "Mistral",
    "Groq",
    "Modal",
    "Replicate"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "LLM / GenAI Engineer",
    "id": 151,
    "rationale": "Domain=AI / ML; The JD is centered on shipping GenAI/agentic application features, orchestration, RAG retrieval pipelines, and function-calling/MCP tooling, which best matches an LLM/GenAI Engineer.",
    "role_archetype": null,
    "slug": "llm-genai-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "ChatGPT",
      "tag": "new"
    },
    {
      "skill": "Claude Sonnet",
      "tag": "new"
    },
    {
      "skill": "LangChain",
      "tag": "in_db"
    },
    {
      "skill": "Vercel AI SDK",
      "tag": "new"
    },
    {
      "skill": "Hugging Face Hub",
      "tag": "in_db"
    },
    {
      "skill": "pgvector",
      "tag": "in_db"
    },
    {
      "skill": "LanceDB",
      "tag": "new"
    },
    {
      "skill": "Windsurf",
      "tag": "new"
    },
    {
      "skill": "Cursor",
      "tag": "in_db"
    },
    {
      "skill": "MCP",
      "tag": "new"
    },
    {
      "skill": "Function Calling",
      "tag": "in_db"
    },
    {
      "skill": "LangGraph",
      "tag": "in_db"
    },
    {
      "skill": "Pydantic AI",
      "tag": "new"
    },
    {
      "skill": "OpenAI",
      "tag": "in_db"
    },
    {
      "skill": "Anthropic",
      "tag": "in_db"
    },
    {
      "skill": "Mistral",
      "tag": "new"
    },
    {
      "skill": "Groq",
      "tag": "new"
    },
    {
      "skill": "Modal",
      "tag": "new"
    },
    {
      "skill": "Replicate",
      "tag": "new"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "LLM Operations and Orchestration",
          "id": 49,
          "rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
          "slug": "llm-operations-and-orchestration",
          "source": "db"
        },
        "dimension_id": 49,
        "input_skill": "LangChain",
        "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": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 240,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ML Frameworks and Libraries",
          "id": 40,
          "rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
          "slug": "ml-frameworks-and-libraries",
          "source": "db"
        },
        "dimension_id": 40,
        "input_skill": "Hugging Face Hub",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 151,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "Hugging Face Hub",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Vector Databases",
          "id": 198,
          "rationale": "Specialized storage and indexing systems used to persist embeddings and support similarity search. This is a distinct vendor-family cluster because AI features often depend on a concrete vector store choice and its operational behavior.",
          "slug": "vector-databases",
          "source": "db"
        },
        "dimension_id": 198,
        "input_skill": "pgvector",
        "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": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1246,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "Cursor",
        "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": 1589,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Structured Output Integration",
          "id": 204,
          "rationale": "Turning model responses into reliable application data that downstream code can consume. This dimension covers schema-constrained generation, parsing, validation, and mapping outputs into product workflows.",
          "slug": "structured-output-integration",
          "source": "db"
        },
        "dimension_id": 204,
        "input_skill": "Function Calling",
        "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": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1278,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Agentic Frameworks",
          "id": 200,
          "rationale": "Frameworks for building tool-using, stateful, multi-step AI agents that plan and act across tasks. This is a separate cluster because agent behavior introduces control flow, memory, and safety concerns beyond simple prompt chaining.",
          "slug": "agentic-frameworks",
          "source": "db"
        },
        "dimension_id": 200,
        "input_skill": "LangGraph",
        "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": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1253,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "OpenAI",
        "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": 1186,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "Anthropic",
        "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": 1188,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "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": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "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 Developer",
            "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"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "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": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "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": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "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": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "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": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "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": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "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": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "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"
        },
        "dimension_id": 96,
        "input_skill": "FastAPI",
        "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": 1201,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
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