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Pipeline run

0d8aa303-16b2-49fc-8ad5-c85cd52aa5f6

Pipeline LLM cost (USD)
API 1: $0.0193 API 2: $0.0003 API 3: $0.0000 Total: $0.0196

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · API and service implementation
Owns ambiguous product/engineering problems end-to-end, turning them into shipped backend systems, APIs, and data platforms while using Claude Code/Codex, evals, prompt/model-routing work, and MCP/plugin building to improve the AI-native workflow week over week.
""Design and ship backend systems, APIs, and data platforms that hold up at scale.""
Tech stack maturity
Modern Cloud Native cache hit
The skill set centers on Python FastAPI, PostgreSQL, data platforms, model routing, plugins, and LLM orchestration tools like LangGraph and Codex, which strongly indicates a modern cloud-native backend stack with AI integration.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1): Claude, Gemini
Frameworks (×2): Vertex AI, Milvus, LangGraph, Langfuse
Models / concepts (×3): Anthropic, OpenAI, agentic, MCP, AI
Evidence — skills matched in JD (18)
Claude Code Codex Python FastAPI LangGraph PostgreSQL APIs Model Routing Prompt Design Tokenomics MCP Plugins Backend Systems Data Platforms Open-Weights Models AI Tooling Evals Test Harness
Skill cluster (4 dimension groups, role-scoped)
Programming Languages
Python
Relational Database Design
PostgreSQL
Web Application Frameworks
FastAPI
Cross-cutting / unaligned
Claude Code Codex LangGraph APIs Model Routing Prompt Design Tokenomics MCP Plugins Backend Systems Data Platforms Open-Weights Models AI Tooling Evals Test Harness
Show KRA description ↓
Youll own engineering work end-to-end across our products and platform from ambiguous problem to shipped feature. Translate fuzzy product and engineering asks into shipped systems; you dont wait for a manager to break the work down for you. Build with Claude Code, Codex, and the broader agentic stack as your default workflow not novelty. Design and ship backend systems, APIs, and data platforms that hold up at scale. Partner directly with product owners and stakeholders. Product instinct matters as much as code. We write evals, not just tests. We engineer the harness: model routing, context and prompt design, tokenomics first-class craft, not afterthoughts. We author skills and MCP plugins to specialize our toolchain we dont just consume it. Open-weights models get explored when they unlock cost, sovereignty, or capability that frontier APIs cant. We treat the engineering loop itself as something to instrument and improve, week over week. Closer to a Forward-Deployed Engineer than a heads-down coder. Twice-daily written updates are the default, not an ask. Decisions, trade-offs, and approaches live where everyone can see them silence stays comfortable because the writing is already flowing. We respond fast on chat and email, and treat documents as how we think and align. 36 years building real software. Stack is secondary; strong fundamentals and learning agility are not. Youve already rewired how you work in the last 6-12 months around AI tooling. You can tell us specifically what changed, and why. High agency. You spot the problem, frame it, and ship the fix without waiting to be asked. Communicates in writing by default. Surfaces progress, decisions, and blockers fast not weekly, not when asked. No cognitive surrender. You think with AI, not through it you spot slop, push back on the model, and ship higher-quality work because of the loop, not despite it. Sharp judgment on trade-offs, prioritization, and when to push back. Comfort with ambiguity and a bias to action. Python, FastAPI, LangGraph, and Postgres are common in our stack but wed rather hire a generalist who thinks AI-native than a specialist who doesnt.

Signals

Skill fullstack-developer
0.21
Alias
KRA flutter-developer
0.55

Post-classification

Centroidupdated · n=1802
Alias collision log
New-role queue
New skills captured8
New KRA capturedyes

Captured for admin review

Prompt Design primary Backend Developer pending
Tokenomics primary Backend Developer pending
MCP primary Backend Developer pending
Open-Weights Models Backend Developer pending
AI Tooling Backend Developer pending
Evals Backend Developer pending
Test Harness Backend Developer pending
Backend Systems primary Backend Developer pending
R&R fragment (sim 0.00) Backend Developer pending

Youll own engineering work end-to-end across our products and platform from ambiguous problem to shipped feature. Translate fuzzy product and engineering asks into shipped systems; you dont wait for a…

Status: completed Created: 2026-07-28T16:56:53.786051Z Updated: 2026-07-30T21:29:10.628262Z API 3 duration: 8063 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Backend Developer

CASE A

slug: backend-engineer · id: 1 · source: db

Title 'Staff Software Engineer AI-native, high agency' not a catalog hit; nature-aware resolver mapped to same-nature role 'Backend Developer': This is a build-nature backend engineering role; Backend Developer is the correct generalization and more faithful than AI Engineer or other language-specific variants.

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
1
Skipped

Job description

Staff Software Engineer AI-native, high agency
ZoomRx Technology Team Chennai / Pune /Gurugram (hybrid)


The bet
The last 12 months redefined what seniorengineer means. Were hiring the people who already operate the new way.
If youve spent this year rewiring how youship turning ambiguous PRDs into shipped features with Claude Code and Codexin the loop, replacing rote work with agents, treating AI as leverage insteadof assistance read on.
About ZoomRx
We work on hard problems at the intersectionof data, healthcare, and technology, for the worlds largest biopharmacompanies. Our products Ferma.AI, PERxCEPT, HCP-Pt Conversations shape howthey understand markets and make decisions. Were flat; engineers own outcomes,not tickets.
The Role
Youll own engineering work end-to-end acrossour products and platform from ambiguous problem to shipped feature.
Translate fuzzy product and engineering asks intoshipped systems; you dont wait for a manager to break the work down for you.
Build with Claude Code, Codex, and the broader agenticstack as your default workflow not novelty.
Design and ship backend systems, APIs, and dataplatforms that hold up at scale.
Partner directly with product owners and stakeholders.Product instinct matters as much as code.
How We Engineer
We write evals, not just tests. We engineerthe harness: model routing, context and prompt design, tokenomics first-classcraft, not afterthoughts. We author skills and MCP plugins to specialize ourtoolchain we dont just consume it. Open-weights models get explored whenthey unlock cost, sovereignty, or capability that frontier APIs cant. We treatthe engineering loop itself as something to instrument and improve, week overweek.
How We Operate
Closer to a Forward-Deployed Engineer than aheads-down coder. Twice-daily written updates are the default, not an ask.Decisions, trade-offs, and approaches live where everyone can see them silence stays comfortable because the writing is already flowing. We respondfast on chat and email, and treat documents as how we think and align.
What we look for
36 years building real software. Stack is secondary;strong fundamentals and learning agility are not.
Youve already rewired how you work in the last 612months around AI tooling. You can tell us specifically what changed, and why.
High agency. You spot the problem, frame it, and shipthe fix without waiting to be asked.
Communicates in writing by default. Surfaces progress,decisions, and blockers fast not weekly, not when asked.
No cognitive surrender. You think with AI, not through it you spot slop, push back on the model, and ship higher-quality workbecause of the loop, not despite it.
Sharp judgment on trade-offs, prioritization, and whento push back.
Comfort with ambiguity and a bias to action.
Python, FastAPI, LangGraph, and Postgres arecommon in our stack but wed rather hire a generalist who thinks AI-nativethan a specialist who doesnt.
What to Expect
A short pre-R1 note from you on how your workhas evolved in the last 612 months, then a deep R1 with the VP of Tech,followed by a focused technical loop. Well spend most of our time on whatyouve actually built and how you think.
Why Now
Were not retrofitting AI onto how we used towork. Were rebuilding the tech function around it operating model, tooling,hiring bar. Engineers joining now shape what that looks like.
If what changed in the last six months ofhow you work is a question you have a real answer to, wed love to talk.
Whats in our stack today
Tooling our engineers run on every day: Claude Code, Codex, MCP servers, and thebroader agentic toolchain.
Product stack : Python, FastAPI, LangGraph, Temporal,Anthropic Claude, OpenAI, Google Gemini (Vertex AI), Milvus, PostgreSQL, Redis,Kubernetes (GKE) on GCP, LangFuse, SigNoz, Sentry.
We dont expect everyone to have touched allof these most of our engineers picked up half on the job.


Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Role: Data Platform Engineer
Industry Type: Analytics / KPO / Research
Department: Engineering - Software & QA
Employment Type: Full Time, Permanent
Role Category: Software Development
Education
UG: Any Graduate
PG: Any Postgraduate

Skills from this JD

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

Claude Code Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Claude Code id=1588 · claude-code

Aliases — catalog

  • Claude Code (CANONICAL)

Context tags (catalog)

AI assistant API integration automated testing code completion code generation code review debugging integrated development environment machine learning natural language processing programming languages real-time collaboration software development syntax highlighting version control

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ai Coding Assistant Tool
Vendor
Anthropic
License
unknown
Year introduced
2023
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in a growing number of engineering JDs and vendor docs as an AI coding assistant, but adoption is still far from universal compared with established IDE tools.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1194
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)
Codex Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Codex id=1590 · codex

Aliases — catalog

  • Codex (CANONICAL)

Context tags (catalog)

AI assistant API integration GPT-3 OpenAI chatbot development code generation data analysis fine-tuning machine learning model training natural language processing prompt engineering software automation user interface version control

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ai Assistant Tool
Vendor
OpenAI
License
proprietary
Year introduced
2021
Confidence
0.78
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in growing JD/tooling mentions for AI coding assistants, but market is still fragmented and not yet a universal hiring staple like GitHub Copilot or AWS.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1196
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)
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
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, WordPress Dev

  • 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: Distributed Systems Engineer, 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 saved
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 Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: FastAPI id=1201 · fastapi

Aliases — catalog

  • FastAPI (CANONICAL) primary

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • 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 saved
LangGraph Primary 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)
PostgreSQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PostgreSQL id=16 · postgresql

Aliases — catalog

  • PostgreSQL (CANONICAL) primary
  • PG 13 (VERSION)
  • PG 14 (VERSION)
  • PG 15 (VERSION)
  • PG 16 (VERSION)
  • PostgreSQL 13 (VERSION)
  • PostgreSQL 14 (VERSION)
  • PostgreSQL 15 (VERSION)
  • PostgreSQL 16 (VERSION)
  • Postgres 13 (VERSION)
  • Postgres 14 (VERSION)
  • Postgres 15 (VERSION)
  • Postgres 16 (VERSION)
  • pg10 (VERSION)
  • pg11 (VERSION)
  • pg12 (VERSION)
  • pg13 (VERSION)
  • pg14 (VERSION)
  • pg15 (VERSION)
  • pg16 (VERSION)
  • postgres (VERSION)
  • postgresql 10 (VERSION)
  • postgresql 11 (VERSION)
  • postgresql 12 (VERSION)
  • postgresql 13 (VERSION)
  • postgresql 14 (VERSION)
  • postgresql 15 (VERSION)
  • postgresql 16 (VERSION)
  • postgresql-16 (VERSION)
  • postgresql10 (VERSION)
  • postgresql11 (VERSION)
  • postgresql12 (VERSION)
  • postgresql13 (VERSION)
  • postgresql14 (VERSION)
  • postgresql15 (VERSION)
  • postgresql16 (VERSION)

Context tags (catalog)

ACID EXPLAIN JSONB PL/pgSQL PostGIS SQL VACUUM backup data integrity database migration extensions indexes indexing joins migration partitioning performance tuning pgAdmin query optimization replication schema stored procedures table partitioning transaction transactions triggers views

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Relational Database
Vendor
PostgreSQL Global Development Group
License
other_open
Year introduced
1996
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PostgreSQL appears in a large share of backend/data engineering job postings and is a default managed option across AWS RDS, GCP Cloud SQL, and Azure Database, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
29
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 216

    Library dimension (catalog)

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

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

  • Relational Database Usage Catalog dimension db id 371

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension saved
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
APIs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: APIs id=1192 · apis

Aliases — catalog

  • APIs (CANONICAL)

Context tags (catalog)

API Gateway Endpoint GraphQL JSON JWT Microservices OAuth Postman REST Rate Limiting SOAP Swagger Throttling Webhooks XML

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Application Programming Interfaces
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: APIs are a hiring-pipeline staple across backend, mobile, and platform JDs; REST/GraphQL/API design appears in large volumes of job postings and vendor docs, indicating broad adoption.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
10
Sub-category id
902
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)
Model Routing Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: model routing id=1286 · model-routing

Aliases — catalog

  • model routing (CANONICAL) primary

Context tags (catalog)

API gateway circuit breaker data flow dynamic routing failover latency optimization load balancing microservices network topology pathfinding request handling routing algorithms service discovery service mesh traffic management

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Model Routing Concept
Confidence
0.82
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears increasingly in AI/LLM job descriptions and vendor docs for multi-model orchestration, but there’s no universal standard or broad hiring-pipeline staple yet.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
955
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Latency and Cost Optimization Catalog dimension db id 205

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Latency and Cost Optimization
latency-and-cost-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Prompt Design 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
CONCEPT
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Tokenomics 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
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
Certifications
Sub-category
general
Skill nature
CREDENTIAL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Plugins Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: plugins id=3661 · plugins

Aliases — catalog

  • plugins (CANONICAL) primary
  • Plugins (CANONICAL)

Context tags (catalog)

API API integration add-ons callback functions configuration customization dependency injection dynamic loading event-driven extension hooks integration interoperability middleware modular plugin architecture plugin ecosystem plugin manager service-oriented software architecture versioning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Plugin Pattern
Confidence
0.84
Version strategy
NOT_APPLICABLE

Maturity reasoning: Plugin architectures are a common hiring signal across IDEs, CMSs, browsers, and SaaS platforms; many JDs mention extensibility/plugin systems, and major ecosystems like VS Code and WordPress rely on them.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3807
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Magento Module Development Catalog dimension db id 482

    Library dimension (catalog)

    Roles linked in library: Magento Dev

  • PHP and Magento Extension Points Catalog dimension db id 391

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Magento Module Development
magento-module-development
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PHP and Magento Extension Points
php-and-magento-extension-points
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Open-Weights Models 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
CONCEPT
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
AI Tooling Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agent Tooling id=1587 · agent-tooling

Aliases — catalog

  • Agent Tooling (CANONICAL)

Context tags (catalog)

AI agents API integration automation customization data pipelines debugging deployment integration monitoring orchestration performance tuning scripting toolchain user interface version control workflow

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Agent Tooling
Vendor
OpenAI
License
mit
Year introduced
2021
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Agent tooling is appearing in more job descriptions and vendor roadmaps, but it is not yet a universal hiring staple; market signal shows rapid GitHub/project growth around agent frameworks and orchestration tools.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1193
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
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Evals 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
PRACTICE
Volatility
FAST
Typical lifespan
SHORT_LIVED
Version strategy
VERSIONED
Test Harness 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
Testing Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Backend Systems 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
Architectural Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Platforms Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: data platforms id=1470 · data-platforms

Aliases — catalog

  • data platforms (CANONICAL)

Context tags (catalog)

Azure Data Lake BigQuery ETL Redshift Snowflake business intelligence cloud storage data governance data integration data lakes data modeling data orchestration data pipelines data warehousing real-time analytics

Stored enrichment (catalog DB)

Category
Domain
Sub-category
Data Platforms
Confidence
0.91
Version strategy
NOT_APPLICABLE

Maturity reasoning: Broadly listed in data/analytics JDs across Snowflake, Databricks, BigQuery, and Redshift ecosystems; strong vendor and hiring-market demand signals make it a staple domain.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
37
Sub-category id
1109
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    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)
Systems Programming
d_init_02
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
Claude Code in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Codex 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 saved
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 saved
LangGraph in_db
Agentic Frameworks
agentic-frameworks
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PostgreSQL in_db
Relational Database Design
relational-database-design
Existing dimension (library) · Role↔dimension saved
PostgreSQL in_db
Relational Database Usage
relational-database-usage
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
APIs in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Model Routing in_db
Latency and Cost Optimization
latency-and-cost-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Plugins in_db
Magento Module Development
magento-module-development
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Plugins in_db
PHP and Magento Extension Points
php-and-magento-extension-points
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI Tooling new
React Frontend Development
d_init_01
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Platforms in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Platforms in_db
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Prompt Design | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
canonical_skill_proposed Tokenomics | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed MCP | type=Certifications subtype=general nature=CREDENTIAL lifespan=MULTI_YEAR
canonical_skill_proposed Open-Weights Models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=SHORT_LIVED
canonical_skill_proposed Evals | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=SHORT_LIVED
canonical_skill_proposed Test Harness | type=Testing Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Backend Systems | type=Architectural Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed AI Tooling ↔ React Frontend Development
nano JD Parser — gpt-4.1-nano click to toggle
RoleStaff Software Engineer AI-native, high agency
CompanyZoomRx
Experience6-12 months of experience around AI tooling
DomainHealthcare
Location Chennai, India (hybrid)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "We work on hard problems",
      "last_5_words": "not tickets."
    },
    "text": "We work on hard problems at the intersection of data, healthcare, and technology, for the worlds largest biopharma companies. Our products Ferma.AI, PERxCEPT, HCP-Pt Conversations shape how they understand markets and make decisions. Were flat; engineers own outcomes, not tickets.",
    "word_count": 50
  },
  "ai_kras": [
    "Youve already rewired how you work in the last 6-12 months around AI tooling.",
    "You think with AI, not through it you spot slop, push back on the model, and ship higher-quality work because of the loop, not despite it."
  ],
  "certifications": [],
  "company_name": "ZoomRx",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Biopharma",
        "HealthTech"
      ],
      "domain": "Healthcare"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Any Graduate",
      "raw": "UG: Any Graduate",
      "requirement": "required"
    },
    {
      "level": "Master\u0027s",
      "qualification": "Any Postgraduate",
      "raw": "PG: Any Postgraduate",
      "requirement": "preferred"
    }
  ],
  "experience": {
    "max": 6,
    "min": 3,
    "raw": "6-12 months of experience around AI tooling"
  },
  "job_locations": [
    {
      "aliases": [
        "Madras"
      ],
      "city": "Chennai",
      "country": "India",
      "state": null,
      "work_mode": "hybrid"
    },
    {
      "aliases": [],
      "city": "Pune",
      "country": "India",
      "state": null,
      "work_mode": "hybrid"
    },
    {
      "aliases": [
        "Gurgaon"
      ],
      "city": "Gurugram",
      "country": "India",
      "state": "Haryana",
      "work_mode": "hybrid"
    }
  ],
  "role": "Staff Software Engineer AI-native, high agency",
  "role_aliases": [
    {
      "name": "Software Engineer",
      "reasoning": "generalized form of the picked role",
      "relation": "synonym"
    },
    {
      "name": "Data Platform Engineer",
      "reasoning": "JD mentions Data Platform Engineer as a role",
      "relation": "adjacent"
    },
    {
      "name": "Backend Engineer",
      "reasoning": "role involves backend systems and APIs",
      "relation": "adjacent"
    }
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "The Role",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Youll own engineering work end-to-end",
        "last_5_words": "as much as code."
      },
      "text": "Youll own engineering work end-to-end across our products and platform from ambiguous problem to shipped feature.\nTranslate fuzzy product and engineering asks into shipped systems; you dont wait for a manager to break the work down for you.\nBuild with Claude Code, Codex, and the broader agentic stack as your default workflow not novelty.\nDesign and ship backend systems, APIs, and data platforms that hold up at scale.\nPartner directly with product owners and stakeholders. Product instinct matters as much as code.",
      "word_count": 83
    },
    {
      "bullet_count": 0,
      "heading": "How We Engineer",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "We write evals, not just",
        "last_5_words": "instrument and improve, week over week."
      },
      "text": "We write evals, not just tests. We engineer the harness: model routing, context and prompt design, tokenomics first-class craft, not afterthoughts. We author skills and MCP plugins to specialize our toolchain we dont just consume it. Open-weights models get explored when they unlock cost, sovereignty, or capability that frontier APIs cant. We treat the engineering loop itself as something to instrument and improve, week over week.",
      "word_count": 66
    },
    {
      "bullet_count": 0,
      "heading": "How We Operate",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Closer to a Forward-Deployed Engineer",
        "last_5_words": "how we think and align."
      },
      "text": "Closer to a Forward-Deployed Engineer than a heads-down coder. Twice-daily written updates are the default, not an ask. Decisions, trade-offs, and approaches live where everyone can see them silence stays comfortable because the writing is already flowing. We respond fast on chat and email, and treat documents as how we think and align.",
      "word_count": 66
    },
    {
      "bullet_count": 0,
      "heading": "What we look for",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "36 years building real software.",
        "last_5_words": "who thinks AI-native than a specialist."
      },
      "text": "36 years building real software. Stack is secondary; strong fundamentals and learning agility are not.\nYouve already rewired how you work in the last 6-12 months around AI tooling. You can tell us specifically what changed, and why.\nHigh agency. You spot the problem, frame it, and ship the fix without waiting to be asked.\nCommunicates in writing by default. Surfaces progress, decisions, and blockers fast not weekly, not when asked.\nNo cognitive surrender. You think with AI, not through it you spot slop, push back on the model, and ship higher-quality work because of the loop, not despite it.\nSharp judgment on trade-offs, prioritization, and when to push back.\nComfort with ambiguity and a bias to action.\nPython, FastAPI, LangGraph, and Postgres are common in our stack but wed rather hire a generalist who thinks AI-native than a specialist who doesnt.",
      "word_count": 139
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is mentioned in a responsibility with a verb, indicating its importance in the workflow.",
      "skill_name": "Claude Code"
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      "dimension": null,
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      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is mentioned in a responsibility with a verb, indicating its importance in the workflow.",
      "skill_name": "Codex"
    },
    {
      "dimension": {
        "display_name": "Programming Languages",
        "id": 1,
        "slug": "programming-languages"
      },
      "is_primary": true,
      "layer": "L0",
      "layer_source": "baseline",
      "rationale": "Anchor dimension: Programming Languages",
      "skill_name": "Python"
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    {
      "dimension": {
        "display_name": "Web Application Frameworks",
        "id": 2,
        "slug": "web-application-frameworks"
      },
      "is_primary": true,
      "layer": "L1",
      "layer_source": "baseline",
      "rationale": null,
      "skill_name": "FastAPI"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is implied as part of the technology stack mentioned in the responsibilities.",
      "skill_name": "LangGraph"
    },
    {
      "dimension": {
        "display_name": "Relational Database Design",
        "id": 4,
        "slug": "relational-database-design"
      },
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": null,
      "skill_name": "PostgreSQL"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L1",
      "layer_source": "promoted",
      "rationale": "The skill is mentioned in a responsibility leading to the design and shipping of backend systems, making it a primary focus.",
      "skill_name": "APIs"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is implied as part of the engineering responsibilities described in the JD.",
      "skill_name": "Model Routing"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "llm",
      "rationale": "The skill is implied as part of the engineering responsibilities described in the JD.",
      "skill_name": "Prompt Design"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "llm",
      "rationale": "The skill is implied as part of the engineering responsibilities described in the JD.",
      "skill_name": "Tokenomics"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "llm",
      "rationale": "The skill is implied as part of the engineering responsibilities described in the JD.",
      "skill_name": "MCP"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is mentioned in a responsibility with a verb, indicating its importance in the workflow.",
      "skill_name": "Plugins"
    },
    {
      "dimension": null,
      "is_primary": false,
      "layer": null,
      "layer_source": null,
      "rationale": null,
      "skill_name": "Open-Weights Models"
    },
    {
      "dimension": null,
      "is_primary": false,
      "layer": null,
      "layer_source": null,
      "rationale": null,
      "skill_name": "AI Tooling"
    },
    {
      "dimension": null,
      "is_primary": false,
      "layer": null,
      "layer_source": null,
      "rationale": null,
      "skill_name": "Evals"
    },
    {
      "dimension": null,
      "is_primary": false,
      "layer": null,
      "layer_source": null,
      "rationale": null,
      "skill_name": "Test Harness"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "llm",
      "rationale": "The skill is implied as part of the design and shipping responsibilities described in the JD.",
      "skill_name": "Backend Systems"
    },
    {
      "dimension": null,
      "is_primary": true,
      "layer": "L2",
      "layer_source": "baseline",
      "rationale": "The skill is already at baseline and is mentioned in a responsibility, maintaining its layer.",
      "skill_name": "Data Platforms"
    }
  ],
  "jd_parameters": {
    "certifications": [],
    "clientDetails": null,
    "company": "ZoomRx",
    "companySize": null,
    "ctc": {
      "currency": null,
      "max": null,
      "min": null,
      "period": null,
      "raw": null
    },
    "educationRequirements": [
      "Any Graduate",
      "Any Postgraduate"
    ],
    "experience": {
      "max": 6,
      "min": 3,
      "raw": "6-12 months of experience around AI tooling"
    },
    "industryDomain": "Healthcare",
    "knockouts": {
      "certifications": [],
      "ctc": {
        "currency": null,
        "max": null,
        "min": null
      },
      "educationRequirements": [
        "Any Graduate",
        "Any Postgraduate"
      ],
      "experience": {
        "max": 6,
        "min": 3
      },
      "location": [
        "Chennai, India",
        "Hybrid",
        "Pune, India",
        "Gurugram, India"
      ],
      "noticePeriod": null
    },
    "locations": [
      "Chennai, India",
      "Hybrid",
      "Pune, India",
      "Gurugram, India"
    ],
    "noticePeriod": null,
    "openToRelocate": false,
    "role": "Staff Software Engineer AI-native, high agency",
    "roleSynonyms": [
      "Software Engineer"
    ]
  },
  "jd_role": {
    "display_name": "Staff Software Engineer AI-native, high agency",
    "rationale": null,
    "role_aliases": [
      "Software Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "We work on hard problems",
        "last_5_words": "not tickets."
      },
      "text": "We work on hard problems at the intersection of data, healthcare, and technology, for the worlds largest biopharma companies. Our products Ferma.AI, PERxCEPT, HCP-Pt Conversations shape how they understand markets and make decisions. Were flat; engineers own outcomes, not tickets.",
      "word_count": 50
    },
    "ai_kras": [
      "Youve already rewired how you work in the last 6-12 months around AI tooling.",
      "You think with AI, not through it you spot slop, push back on the model, and ship higher-quality work because of the loop, not despite it."
    ],
    "certifications": [],
    "company_name": "ZoomRx",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Biopharma",
          "HealthTech"
        ],
        "domain": "Healthcare"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "Any Graduate",
        "raw": "UG: Any Graduate",
        "requirement": "required"
      },
      {
        "level": "Master\u0027s",
        "qualification": "Any Postgraduate",
        "raw": "PG: Any Postgraduate",
        "requirement": "preferred"
      }
    ],
    "experience": {
      "max": 6,
      "min": 3,
      "raw": "6-12 months of experience around AI tooling"
    },
    "job_locations": [
      {
        "aliases": [
          "Madras"
        ],
        "city": "Chennai",
        "country": "India",
        "state": null,
        "work_mode": "hybrid"
      },
      {
        "aliases": [],
        "city": "Pune",
        "country": "India",
        "state": null,
        "work_mode": "hybrid"
      },
      {
        "aliases": [
          "Gurgaon"
        ],
        "city": "Gurugram",
        "country": "India",
        "state": "Haryana",
        "work_mode": "hybrid"
      }
    ],
    "role": "Staff Software Engineer AI-native, high agency",
    "role_aliases": [
      {
        "name": "Software Engineer",
        "reasoning": "generalized form of the picked role",
        "relation": "synonym"
      },
      {
        "name": "Data Platform Engineer",
        "reasoning": "JD mentions Data Platform Engineer as a role",
        "relation": "adjacent"
      },
      {
        "name": "Backend Engineer",
        "reasoning": "role involves backend systems and APIs",
        "relation": "adjacent"
      }
    ],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "The Role",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Youll own engineering work end-to-end",
          "last_5_words": "as much as code."
        },
        "text": "Youll own engineering work end-to-end across our products and platform from ambiguous problem to shipped feature.\nTranslate fuzzy product and engineering asks into shipped systems; you dont wait for a manager to break the work down for you.\nBuild with Claude Code, Codex, and the broader agentic stack as your default workflow not novelty.\nDesign and ship backend systems, APIs, and data platforms that hold up at scale.\nPartner directly with product owners and stakeholders. Product instinct matters as much as code.",
        "word_count": 83
      },
      {
        "bullet_count": 0,
        "heading": "How We Engineer",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "We write evals, not just",
          "last_5_words": "instrument and improve, week over week."
        },
        "text": "We write evals, not just tests. We engineer the harness: model routing, context and prompt design, tokenomics first-class craft, not afterthoughts. We author skills and MCP plugins to specialize our toolchain we dont just consume it. Open-weights models get explored when they unlock cost, sovereignty, or capability that frontier APIs cant. We treat the engineering loop itself as something to instrument and improve, week over week.",
        "word_count": 66
      },
      {
        "bullet_count": 0,
        "heading": "How We Operate",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Closer to a Forward-Deployed Engineer",
          "last_5_words": "how we think and align."
        },
        "text": "Closer to a Forward-Deployed Engineer than a heads-down coder. Twice-daily written updates are the default, not an ask. Decisions, trade-offs, and approaches live where everyone can see them silence stays comfortable because the writing is already flowing. We respond fast on chat and email, and treat documents as how we think and align.",
        "word_count": 66
      },
      {
        "bullet_count": 0,
        "heading": "What we look for",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "36 years building real software.",
          "last_5_words": "who thinks AI-native than a specialist."
        },
        "text": "36 years building real software. Stack is secondary; strong fundamentals and learning agility are not.\nYouve already rewired how you work in the last 6-12 months around AI tooling. You can tell us specifically what changed, and why.\nHigh agency. You spot the problem, frame it, and ship the fix without waiting to be asked.\nCommunicates in writing by default. Surfaces progress, decisions, and blockers fast not weekly, not when asked.\nNo cognitive surrender. You think with AI, not through it you spot slop, push back on the model, and ship higher-quality work because of the loop, not despite it.\nSharp judgment on trade-offs, prioritization, and when to push back.\nComfort with ambiguity and a bias to action.\nPython, FastAPI, LangGraph, and Postgres are common in our stack but wed rather hire a generalist who thinks AI-native than a specialist who doesnt.",
        "word_count": 139
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "role": {
    "anchor_type": "LANGUAGE",
    "display_name": "Backend Developer",
    "id": 1,
    "resolution": "in_db",
    "slug": "backend-engineer"
  },
  "run_id": "0d8aa303-16b2-49fc-8ad5-c85cd52aa5f6",
  "secondary_skills": [
    "Open-Weights Models",
    "AI Tooling",
    "Evals",
    "Test Harness"
  ],
  "skill_layers": [
    {
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            "similarity": 0.4789
          },
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          "LangGraph",
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    "new_role_domain": null,
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    "queued": false,
    "reasoning": "Title \u0027Staff Software Engineer AI-native, high agency\u0027 not a catalog hit; nature-aware resolver mapped to same-nature role \u0027Backend Developer\u0027: This is a build-nature backend engineering role; Backend Developer is the correct generalization and more faithful than AI Engineer or other language-specific variants.",
    "role_aliases": [],
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  },
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        "role_slug": "backend-engineer",
        "skill_name": "Prompt Design",
        "status": "pending"
      },
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        "is_primary": true,
        "queue_id": 26292,
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        "status": "pending"
      },
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        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 26294,
        "role_display_name": "Backend Developer",
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        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 26295,
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      },
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      },
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}
API 2 — extract-details
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      "alias_persisted": false,
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "claude-code",
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        "volatility": "EMERGING"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2536,
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
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        "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",
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        "is_also_category": false,
        "is_extractable": true,
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1889,
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
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      "existing_alias_text": "PostgreSQL",
      "input_term": "PostgreSQL",
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
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        "typical_lifespan": "EVERGREEN",
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      "matched_via": "alias"
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    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 1828,
      "existing_alias_text": "APIs",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
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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": 1922,
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        "skill_nature": "CONCEPT",
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        "volatility": "EMERGING"
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      "matched_via": "alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 5278,
      "existing_alias_text": "Plugins",
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      "matched_canonical": {
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        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
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        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 2533,
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      "matched_via": "embedding_alias"
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      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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        "is_extractable": true,
        "skill_nature": "CONCEPT",
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      "matched_via": "alias"
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        "category_id": 3,
        "display_name": "PostgreSQL",
        "id": 16,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "postgresql",
        "sub_category_id": 29,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Data Modeling",
            "id": 216,
            "rationale": "Modeling and tuning relational persistence for backend features. PHP backend developers need this to shape schemas, indexes, transactions, and query-aware data structures that support application behavior.",
            "slug": "relational-data-modeling",
            "source": "db"
          },
          "input_skill": "PostgreSQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "PostgreSQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "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": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Usage",
            "id": 371,
            "rationale": "Working effectively with operational relational databases from Go backend services. This includes schema-aware querying, indexing awareness, transactions, and understanding how service code interacts with PostgreSQL or similar systems.",
            "slug": "relational-database-usage",
            "source": "db"
          },
          "input_skill": "PostgreSQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "PostgreSQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "APIs",
          "alias_type": "CANONICAL",
          "id": 1828,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 10,
        "display_name": "APIs",
        "id": 1192,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PROTOCOL",
        "slug": "apis",
        "sub_category_id": 902,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "APIs",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "APIs",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "model routing",
          "alias_type": "CANONICAL",
          "id": 1922,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "model routing",
        "id": 1286,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "model-routing",
        "sub_category_id": 955,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Latency and Cost Optimization",
            "id": 205,
            "rationale": "Techniques for making AI features fast and affordable enough for production use. This cluster is coherent because model choice, prompt length, caching, batching, and routing directly affect user experience and unit economics.",
            "slug": "latency-and-cost-optimization",
            "source": "db"
          },
          "input_skill": "Model Routing",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Model Routing",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Prompt Design",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "prompt-design",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Tokenomics",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "tokenomics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "MCP",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Certifications",
          "skill_nature": "CREDENTIAL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "mcp",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "plugins",
          "alias_type": "CANONICAL",
          "id": 6901,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Plugins",
          "alias_type": "CANONICAL",
          "id": 5278,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "plugins",
        "id": 3661,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "plugins",
        "sub_category_id": 3807,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Magento Module Development",
            "id": 482,
            "rationale": "Custom Magento module work for implementing business rules, observers, plugins, and service contracts. This is the core extension surface for adding storefront and admin behavior without modifying vendor code.",
            "slug": "magento-module-development",
            "source": "db"
          },
          "input_skill": "Plugins",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Magento Dev",
              "id": 231,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "magento-dev",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "PHP and Magento Extension Points",
            "id": 391,
            "rationale": "Core implementation surface for Magento customizations, including module code, event-driven hooks, and platform conventions. This is the primary language-and-framework cluster for building upgrade-safe storefront behavior.",
            "slug": "php-and-magento-extension-points",
            "source": "db"
          },
          "input_skill": "Plugins",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Plugins",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Open-Weights Models",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "open-weights-models",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Agent Tooling",
          "alias_type": "CANONICAL",
          "id": 2533,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Agent Tooling",
        "id": 1587,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "agent-tooling",
        "sub_category_id": 1193,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "AI Tooling",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "AI Tooling",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Evals",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "SHORT_LIVED",
          "version_strategy": "VERSIONED",
          "volatility": "FAST"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "evals",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Test Harness",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Testing Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "test-harness",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Backend Systems",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Architectural Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "backend-systems",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "data platforms",
          "alias_type": "CANONICAL",
          "id": 2365,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 37,
        "display_name": "data platforms",
        "id": 1470,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "data-platforms",
        "sub_category_id": 1109,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Data Platforms",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Systems Programming",
            "id": 166,
            "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
            "slug": "d_init_02",
            "source": "db"
          },
          "input_skill": "Data Platforms",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Data Platforms",
      "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": [
    "Prompt Design",
    "Tokenomics",
    "MCP",
    "Open-Weights Models",
    "Evals",
    "Test Harness",
    "Backend Systems"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Backend Developer",
    "id": 1,
    "rationale": "Title \u0027Staff Software Engineer AI-native, high agency\u0027 not a catalog hit; nature-aware resolver mapped to same-nature role \u0027Backend Developer\u0027: This is a build-nature backend engineering role; Backend Developer is the correct generalization and more faithful than AI Engineer or other language-specific variants.",
    "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"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Claude Code",
      "tag": "in_db"
    },
    {
      "skill": "Codex",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "FastAPI",
      "tag": "in_db"
    },
    {
      "skill": "LangGraph",
      "tag": "in_db"
    },
    {
      "skill": "PostgreSQL",
      "tag": "in_db"
    },
    {
      "skill": "APIs",
      "tag": "in_db"
    },
    {
      "skill": "Model Routing",
      "tag": "in_db"
    },
    {
      "skill": "Prompt Design",
      "tag": "new"
    },
    {
      "skill": "Tokenomics",
      "tag": "new"
    },
    {
      "skill": "MCP",
      "tag": "new"
    },
    {
      "skill": "Plugins",
      "tag": "in_db"
    },
    {
      "skill": "Open-Weights Models",
      "tag": "new"
    },
    {
      "skill": "AI Tooling",
      "tag": "in_db"
    },
    {
      "skill": "Evals",
      "tag": "new"
    },
    {
      "skill": "Test Harness",
      "tag": "new"
    },
    {
      "skill": "Backend Systems",
      "tag": "new"
    },
    {
      "skill": "Data Platforms",
      "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": 1,
        "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": "Claude Code",
        "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": 1588,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 1,
        "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",
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        "skipped_reason": null
      },
      {
        "chosen_role_id": 1,
        "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": "AI Tooling",
        "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": 1,
        "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": "Data Platforms",
        "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": 1470,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 1,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Systems Programming",
          "id": 166,
          "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 166,
        "input_skill": "Data Platforms",
        "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": 1470,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "0d8aa303-16b2-49fc-8ad5-c85cd52aa5f6"
}

LLM Calls

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

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