Pipeline run
62310ed0-c8e6-4590-a4e8-edafce525331
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
AI Engineer
slug: ai-engineer · id: 12 · source: db
This role leverages multiple primary skills including Python, TypeScript, and AI workflows.
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About the job Why Bynd Bynd is building the intelligence layer for financial services. We work with leading investment banks, private equity firms, asset managers, lenders, and advisory teams to transform how they extract, analyze, and act on information buried across financial documents, filings, reports, and internal workflows. We operate with the standards of a research team and the urgency of a product company. We care deeply about technical quality, product taste, and building things that get used. The Role As an Applied AI Engineer at Bynd, you will work across the core systems that power our product: document intelligence, retrieval, agentic workflows, and the infrastructure required to deploy them reliably in production. You will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation. What You Will Need Must-haves Strong programming ability in Python and TypeScript Experience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs Experience building or working with document processing systems such as VLMs for OCR and layout parsing models Comfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP Experience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows Preferred Experience with RAG systems, hybrid retrieval, reranking, and eval design Experience with vision-language models or multimodal document understanding Familiarity with Azure- or GCP-based AI infrastructure Familiarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence Experience building multi-step agentic systems or using modern agent tooling Who You Are You thrive in fast-moving environments and care deeply about the quality of what you build. You are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well. You are AI-native in how you work. Tools like Claude Code, Cursor, Codex, and modern model APIs are part of your everyday workflow. You know these tools are powerful, but you also understand where they fail and how to build with judgment around them. You are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks. You are curious about the domain. You want to understand how financial professionals actually work, what makes a workflow painful, what accuracy really means in context, and why a product decision matters.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- Cobalt Strike (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Adversary Simulation Tool
- Vendor
- Fortra
- License
- proprietary
- Year introduced
- 2012
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Appears in a limited set of red-team/pentest JDs and security vendor training, but far below mainstream devops tools; market signal is specialized adversary-simulation usage rather than broad hiring demand.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 54
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Analytical Programming Languages Catalog dimension db id 82
Library dimension (catalog)
Roles linked in library: Data Analyst, Data Scientist
-
Automation Scripting and CLI Catalog dimension db id 48
Library dimension (catalog)
Roles linked in library: Azure Cloud Engineer, Cloud Engineer
-
Automation and Scripting for Operations Catalog dimension db id 361
Library dimension (catalog)
Roles linked in library: Virtualization Engineer
-
Network Automation and Scripting Catalog dimension db id 285
Library dimension (catalog)
Roles linked in library: Network Engineer
-
Programming Languages for AI Workflows Catalog dimension db id 261
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Programming Languages for Backend Systems Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Backend Engineer
-
Programming Languages for Data Work Catalog dimension db id 67
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 113
Library dimension (catalog)
Roles linked in library: Machine Learning Engineer
-
Programming Languages for Security Work Catalog dimension db id 328
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
-
Programming Languages for Test Automation Catalog dimension db id 193
Library dimension (catalog)
Roles linked in library: Automation Tester
-
Security Automation and Scripting Catalog dimension db id 258
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Analytical Programming Languages
analytical-programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Automation and Scripting for Operations
automation-and-scripting-for-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Network Automation and Scripting
network-automation-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for Backend Systems
programming-languages-for-backend-systems
|
✓ | — | 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 Security Work
programming-languages-for-security-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Test Automation
programming-languages-for-test-automation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Security Automation and Scripting
security-automation-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Kotlin (CANONICAL) primary
- kotlin 1.9 (VERSION)
- kotlin 1.9.0 (VERSION)
- kotlin 1.9.1 (VERSION)
- kotlin 1.9.10 (VERSION)
- kotlin 1.9.x (VERSION)
- kotlin-1.9 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- JetBrains
- License
- apache_2
- Year introduced
- 2011
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Kotlin appears in many Android, backend, and multiplatform job postings, and JetBrains reports strong ecosystem growth; it’s a mainstream hiring skill rather than niche.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 54
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Frontend Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Frontend Engineer, Full Stack Developer
-
Programming Languages for AI Workflows Catalog dimension db id 261
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Programming Languages for ML Systems Catalog dimension db id 113
Library dimension (catalog)
Roles linked in library: Machine Learning Engineer
-
Programming Languages for Test Automation Catalog dimension db id 193
Library dimension (catalog)
Roles linked in library: Automation Tester
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Frontend Programming Languages
frontend-programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Test Automation
programming-languages-for-test-automation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
LLMs appear in many recent job descriptions and vendor roadmaps, but are not yet universal across engineering roles; GitHub and cloud provider trend data show rapid growth rather than saturation.
(0.99)
Could be confused with: large_language_model, language_model
LLM is a common acronym for Large Language Model, but in JDs it can also be used loosely for language model generally. A parser could conflate the specific acronym with the broader model concept.
Not versioned
Concept ·machine_learning_model confidence 0.88
LLM is fundamentally a knowledge unit describing a class of machine learning models, not a tool, platform, or language, so it fits the Concept type.
- Category
- Concept
- Sub-category
- machine_learning_model
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Large Language Models
Pipeline tentative id
Covers the core concepts, capabilities, and practical use of large language models for text generation and reasoning. LLM fits here because it names the model class itself rather than a deployment, data, or integration concern.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Prompt engineering appears in many recent AI/LLM job descriptions and vendor docs, but it is still often bundled under broader AI/ML roles rather than a universal standalone requirement.
(0.99)
“Prompting” in a JD usually refers to prompt engineering or prompting techniques. It’s a fairly specific concept and not commonly mistaken for another catalog skill in typical job descriptions.
Not versioned
Concept ·prompt_engineering_concept confidence 0.90
Prompting is best treated as a named knowledge unit about how to interact with language models, so under the Concept vs Methodology rule it fits Concept rather than a tool or methodology.
- Category
- Concept
- Sub-category
- prompt_engineering_concept
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Prompt Engineering
Pipeline tentative id
Crafting inputs to guide model behavior, improve response quality, and control format, tone, and task completion. This includes writing instructions, examples, constraints, and iterative refinements for LLM interactions.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Context management is a common requirement in LLM/agent JDs and product docs, with many listings asking for long-context handling, memory, and retrieval-augmented workflows.
(0.99)
The phrase is fairly specific and usually denotes the soft skill of managing context in work or conversations. It is unlikely to be mistaken for a different catalog skill in a typical job description.
Not versioned
SoftSkill ·context_management confidence 0.88
Context Management is fundamentally an interpersonal/work-practice capability for organizing and maintaining shared understanding, so it fits the SoftSkill category rather than a technical concept or methodology.
- Category
- SoftSkill
- Sub-category
- context_management
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
Locked dimensions (v3 placement)
-
Context Management and Retrieval
Reuses catalog slug
Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This skill belongs here because it is about deciding what information to retrieve, retain, trim, and pass into the model.
-
Conversation Memory Management
Pipeline tentative id
Managing short-term and long-term conversational state so assistants can preserve useful details across turns. This fits when Context Management refers specifically to chat memory, summaries, and retention policies rather than retrieval pipelines.
-
Context Management and Retrieval
Reuses catalog slug
Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | New skill saved · Existing dimension (library) · Role↔dimension saved |
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Appears in growing LLM API docs and JDs for AI app roles, but is still far less common than JSON mode/function calling; market signal is rising adoption rather than universal demand.
(0.98)
The phrase is fairly specific to schema-constrained model output and is unlikely to be mistaken for a different catalog skill in typical JDs.
Not versioned
Concept ·output_schema_constraint confidence 0.88
This is fundamentally a named knowledge unit about constraining model responses to a structure, so by the Concept vs Methodology rule it is a Concept rather than a tool or format.
- Category
- Concept
- Sub-category
- output_schema_constraint
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Context Management, Retrieval, and Response Shaping Proposed / LLM
Proposed / LLM dimension (no DB id yet)
Locked dimensions (v3 placement)
-
Context Management, Retrieval, and Response Shaping
Pipeline tentative id
Preparing, selecting, retrieving, and packaging the right context for model calls so responses are relevant, grounded, and usable. Includes prompt context selection, retrieval-augmented generation, context window management, schema-constrained generation, Structured Outputs, JSON schema prompting, tool-call argument shaping, and other response-grounding techniques that control what information the model sees and how its output is structured.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management, Retrieval, and Response Shaping
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
VLMs are appearing in more job descriptions and product roadmaps, but JD volume is still far below core ML stacks; market signal is rapid GitHub/model release growth rather than universal hiring demand.
(0.99)
VLMs is a fairly specific ML acronym for vision-language models; in typical JDs it is unlikely to be confused with a different catalog skill.
Not versioned
Concept ·vision_language_models confidence 0.93
VLMs (vision-language models) are a named knowledge unit/model family, so by the Concept vs Methodology rule they are best treated as a Concept rather than a tool or framework.
- Category
- Concept
- Sub-category
- vision_language_models
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Vision Language Models
Pipeline tentative id
Models that jointly process images and text for understanding, captioning, grounding, and multimodal generation. VLMs belong here because they are the core model family for image-text reasoning and multimodal inference.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
OCR is broadly used in enterprise document automation and appears frequently in job postings for RPA, IDP, and computer vision roles; cloud vendors also offer mature OCR APIs, signaling mainstream adoption.
(0.99)
OCR is a well-known, specific acronym for optical character recognition in job descriptions. It is unlikely to be reasonably confused with a different catalog skill in typical JD context.
Not versioned
Concept ·optical_character_recognition confidence 0.96
OCR is fundamentally a named knowledge unit about extracting text from images, so it fits the Concept category rather than a tool, library, or format.
- Category
- Concept
- Sub-category
- optical_character_recognition
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
-
Project Delivery and Coordination Catalog dimension db id 366
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Optical Character Recognition
Pipeline tentative id
Techniques and tools for detecting, extracting, and converting text from images or scanned documents into machine-readable text. OCR belongs here because it is the core capability for document digitization, text extraction, and downstream parsing workflows.
-
Document Image Processing
Pipeline tentative id
Preprocessing and layout handling for scanned documents and photos so OCR can work reliably. This dimension covers image cleanup, deskewing, denoising, cropping, and page segmentation around OCR workflows.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Project Delivery and Coordination
d_init_02
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Appears in growing JDs for OCR/IDP and document AI roles, and vendor docs from AWS Textract, Google Document AI, and Azure Form Recognizer emphasize layout extraction as a core capability.
(0.98)
“Layout Parsing” is a fairly specific document-processing concept and is unlikely to be mistaken for a different catalog skill in typical job descriptions.
Not versioned
Concept ·document_layout_parsing confidence 0.88
Layout Parsing is best treated as a Concept because it names a knowledge unit about extracting structure from documents rather than a tool, language, or workflow.
- Category
- Concept
- Sub-category
- document_layout_parsing
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Document Layout Parsing
Pipeline tentative id
Techniques for detecting and interpreting the visual structure of documents so text, tables, and fields can be extracted in reading order. This skill belongs here because layout parsing focuses on page geometry and structural segmentation rather than OCR alone.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Containers are a hiring-pipeline staple: Docker/Kubernetes appear in a large share of cloud and platform JDs, and major vendors (AWS, Azure, GCP) offer first-class container services.
(0.98)
Could be confused with: docker, kubernetes, containerization
"Containers" is broad and can mean generic containerization architecture, but JDs often use it to refer to Docker or Kubernetes work. A parser could reasonably map the term to those more specific catalog skills.
Not versioned
Architecture ·containerization_architecture confidence 0.88
By the Architecture vs Concept rule, Containers refers to the system-shape approach of packaging and isolating applications rather than a tool or runtime.
- Category
- Architecture
- Sub-category
- containerization_architecture
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Containerization
Pipeline tentative id
Covers packaging applications and services into containers and working with container images, runtimes, and lifecycle basics. The target skill belongs here because containers are the core abstraction for isolating and shipping software consistently across environments.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — from this run (catalog unavailable)
- CI/CD (CANONICAL)
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 2102
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Compute right-sizing (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Capacity Planning Methodology
- Confidence
- 0.78
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common cloud/capacity-planning practice; widely referenced in AWS/Azure/GCP cost-optimization docs and frequently appears in FinOps and SRE job descriptions focused on reducing overprovisioning.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 161
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platform Operations Catalog dimension db id 26
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Cloud Security Platforms Catalog dimension db id 332
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platform Operations
cloud-platform-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Platforms
cloud-security-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- ASGI (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Protocol
- Sub-category
- Web Application Protocol
- Vendor
- Django Software Foundation
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: ASGI appears in many Python web JDs for async frameworks like FastAPI/Starlette, but WSGI remains the broader default in legacy stacks; market signal shows growing adoption rather than universal demand.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 161
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Platforms Catalog dimension db id 332
Library dimension (catalog)
Roles linked in library: Cybersecurity Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Platforms
cloud-security-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Evaluation is a core ML/analytics concept and appears broadly in JDs for model validation, A/B testing, and KPI measurement; it’s a standard hiring-pipeline topic rather than a niche tool.
(0.99)
“Evaluation” is a broad concept, but in JDs it usually appears as the intended skill itself (e.g., model/program evaluation). It is not a short acronym or vendor/product name likely to be mistaken for a distinct catalog skill.
Not versioned
Concept ·evaluation confidence 0.88
Evaluation is a named knowledge unit about assessing outcomes or performance, so by the Concept vs Methodology rule it is a Concept rather than a way of working.
- Category
- Concept
- Sub-category
- evaluation
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Model Evaluation and Validation Catalog dimension db id 86
Library dimension (catalog)
Roles linked in library: Data Scientist
Locked dimensions (v3 placement)
-
Model Evaluation and Validation
Pipeline tentative id
Covers assessing model quality, reliability, and fit for purpose using offline and online validation methods. This skill belongs here because evaluation is the core activity for measuring whether a model or system meets expected performance criteria.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model Evaluation and Validation
model-evaluation-and-validation
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Model Evaluation and Validation
d_init_01
|
✓ | — | New skill saved · Existing dimension (embedding dedup) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Observability is broadly listed in SRE/DevOps job descriptions and supported by major vendors (Datadog, Grafana, New Relic, OpenTelemetry), indicating mainstream adoption rather than niche use.
(0.99)
Observability is a well-established engineering concept with a specific meaning in JDs; it is unlikely to be confused with a different catalog skill in typical usage.
Not versioned
Concept ·observability confidence 0.97
Observability is a named knowledge unit about understanding system behavior from outputs, so by the Concept vs Methodology rule it is a Concept rather than a tool or architecture.
- Category
- Concept
- Sub-category
- observability
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Observability and Telemetry
Pipeline tentative id
Practices and tooling for instrumenting systems so their health and behavior can be measured, inspected, and understood in production. This includes logs, metrics, traces, dashboards, and alerting, which are the core concerns of observability.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Common in reliability, manufacturing, and SRE job descriptions; market demand is broad and sustained, with many JDs listing root-cause/failure analysis as a core responsibility.
(0.99)
“Failure Analysis” is a standard, specific engineering concept and is unlikely to be mistaken for another catalog skill in typical job descriptions.
Not versioned
Concept ·failure_analysis confidence 0.94
Failure Analysis is fundamentally a named knowledge unit about diagnosing why systems fail, so it fits the Concept category rather than a methodology or tool.
- Category
- Concept
- Sub-category
- failure_analysis
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Test Evidence, Defect Reporting, and Triage Catalog dimension db id 241
Library dimension (catalog)
Roles linked in library: Manual Tester
-
MySQL Operational Monitoring, Logging, and Diagnostics Catalog dimension db id 166
Library dimension (catalog)
Roles linked in library: MySQL DBA
-
MySQL Operational Monitoring, Logging, and Diagnostics Catalog dimension db id 166
Library dimension (catalog)
Roles linked in library: MySQL DBA
Locked dimensions (v3 placement)
-
Test Evidence, Defect Reporting, and Triage
Pipeline tentative id
Capturing and organizing evidence from testing to investigate failures, reproduce issues, and communicate clear defect reports for triage. Includes documenting what was tested and observed, collecting logs/screenshots/screen recordings, writing reproduction steps and bug reports, assessing severity/priority or release impact, and providing concise notes that help teams prioritize fixes.
-
Operational Diagnostics and Logging
Reuses catalog slug
Covers using logs, metrics, traces, and diagnostic views to understand system failures and explain what happened. Failure Analysis belongs here when the emphasis is on operational investigation rather than test-case defect reporting.
-
MySQL Operational Monitoring, Logging, and Diagnostics
Reuses catalog slug
Covers the DBA practice of monitoring MySQL production health and using MySQL-native logs and diagnostic views to detect, investigate, and explain incidents or performance anomalies. Includes routine health checks, alerting, replication and availability monitoring, resource and connection monitoring, and use of error logs, slow query logs, SHOW PROCESSLIST, performance_schema, status variables, and diagnostic queries to understand behavior and support recovery decisions.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Test Evidence, Defect Reporting, and Triage
test-evidence-defect-reporting-and-triage
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
MySQL Operational Monitoring, Logging, and Diagnostics
mysql-operational-monitoring-logging-and-diagnostics
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Test Evidence, Defect Reporting, and Triage
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or AWS.
(0.99)
Could be confused with: retrieval, generation, information_retrieval
RAG is a common acronym for retrieval-augmented generation, but in JDs it can also be read as generic retrieval or generation wording, and sometimes as the unrelated term 'rag' in other contexts. A parser could plausibly misclassify it without surrounding AI context.
Not versioned
Concept ·retrieval_augmented_generation confidence 0.90
RAG is fundamentally a named AI/ML knowledge pattern for combining retrieval with generation, so it fits the Concept type rather than a Framework or Tool.
- Category
- Concept
- Sub-category
- retrieval_augmented_generation
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Context Management, Retrieval, and Grounding for Model Calls Proposed / LLM
Proposed / LLM dimension (no DB id yet)
-
Cloud Service Integration Patterns Catalog dimension db id 188
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Cloud Service Integration Patterns Catalog dimension db id 188
Library dimension (catalog)
Roles linked in library: Cloud Architect
Locked dimensions (v3 placement)
-
Context Management, Retrieval, and Grounding for Model Calls
Pipeline tentative id
Preparing, selecting, retrieving, summarizing, and packaging information for model calls so outputs stay relevant, grounded, and within the available context window. This includes RAG / retrieval-augmented generation, chunking documents, embedding-based retrieval, vector search, reranking, source grounding, prompt assembly, and other call-time knowledge retrieval and context management techniques.
-
Cloud Service Integration Patterns
Reuses catalog slug
Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. RAG can fit here when the emphasis is on wiring LLM applications to external knowledge services and enterprise systems.
-
Cloud Service Integration Patterns
Reuses catalog slug
Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management, Retrieval, and Grounding for Model Calls
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Service Integration Patterns
cloud-service-integration-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Hybrid retrieval is increasingly listed in RAG/search job descriptions and vendor docs (e.g., Elasticsearch/OpenSearch support BM25+vector fusion), but it is not yet a universal hiring staple.
(0.98)
“Hybrid Retrieval” is a fairly specific IR approach combining lexical and semantic search; in typical JDs it is unlikely to be mistaken for a different catalog skill.
Not versioned
Concept ·information_retrieval_approach confidence 0.92
Hybrid Retrieval is fundamentally a named retrieval approach/idea rather than a system shape or process, so it fits the Concept type.
- Category
- Concept
- Sub-category
- information_retrieval_approach
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Context Assembly and Retrieval for Model Calls Proposed / LLM
Proposed / LLM dimension (no DB id yet)
Locked dimensions (v3 placement)
-
Context Assembly and Retrieval for Model Calls
Pipeline tentative id
Preparing, selecting, retrieving, reranking, and packaging information for model calls so responses stay relevant and grounded. Includes hybrid retrieval, vector and keyword search, chunk selection, context assembly, retrieval-augmented generation, document grounding, and query rewriting when used to build the context supplied to a model.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Assembly and Retrieval for Model Calls
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Reranking is increasingly listed in LLM/RAG job descriptions and appears in vendor docs for search/AI stacks, but it is still far less universal than core retrieval or vector search.
(0.99)
“Reranking” is a fairly specific retrieval/ranking concept in JDs and is unlikely to be mistaken for a different catalog skill without additional context.
Not versioned
Concept ·ranking_reordering_concept confidence 0.90
Reranking is fundamentally a knowledge unit about reordering ranked results, so by the Concept vs Methodology rule it is a Concept rather than a tool or process.
- Category
- Concept
- Sub-category
- ranking_reordering_concept
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
Locked dimensions (v3 placement)
-
Context Management and Retrieval
Reuses catalog slug
Preparing, selecting, and ordering context for model calls so responses stay relevant and grounded. Reranking belongs here because it is used to reorder retrieved candidates by relevance before passing them to an LLM or downstream ranker.
-
Context Management and Retrieval
Reuses catalog slug
Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | New skill saved · Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
Rapidly appearing in job postings for multimodal AI and product teams, and major vendors (OpenAI, Google, Anthropic) have launched VLM APIs/models, but it is not yet a universal hiring staple.
(0.98)
The term is specific and standard in AI/JD contexts; it is unlikely to be mistaken for a different catalog skill.
Not versioned
Concept ·multimodal_ai_model confidence 0.93
Vision-Language Models are a named knowledge unit describing a class of multimodal models, so by the Concept vs Methodology rule they are fundamentally a Concept rather than a tool or framework.
- Category
- Concept
- Sub-category
- multimodal_ai_model
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Vision-Language Models
Pipeline tentative id
Models that jointly process images and text to understand, align, or generate multimodal outputs. This skill belongs here because it centers on architectures and workflows for combining visual and linguistic representations in one system.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Appears increasingly in JDs for OCR/IDP and LLM vision roles, and vendor roadmaps from OpenAI, Google, and AWS show rapid productization, but it is not yet a universal hiring staple.
(0.99)
The phrase is specific and descriptive; in typical JDs it would be understood as the ML capability itself, not a different catalog skill.
Not versioned
Concept ·multimodal_document_understanding confidence 0.93
This is a named knowledge unit about understanding documents across modalities, so by the Concept vs Methodology rule it is a Concept rather than a tool, framework, or architecture.
- Category
- Concept
- Sub-category
- multimodal_document_understanding
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Multimodal Document Understanding
Pipeline tentative id
Covers systems that extract, interpret, and reason over documents using both text and visual layout signals. This skill belongs here because it involves understanding pages, tables, figures, forms, and scanned content as a combined document representation.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Job postings increasingly mention agentic AI/agent orchestration, and major vendors like OpenAI, Microsoft, and AWS have launched agent frameworks, but it is not yet a universal hiring staple.
(0.99)
The term is fairly specific in JDs and usually refers to autonomous/agent-based AI systems, not a common overloaded acronym or short name. Typical extractor confusion with other catalog skills is unlikely.
Not versioned
Concept ·agentic_systems confidence 0.90
This is best treated as a named knowledge unit about autonomous AI behavior and system design, so under the Concept vs Methodology and Architecture vs Concept rules it is a Concept rather than a specific architecture or methodology.
- Category
- Concept
- Sub-category
- agentic_systems
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Agentic System Design
Pipeline tentative id
Covers building systems where LLMs or software agents plan, act, observe results, and iterate toward goals. This includes orchestration patterns, tool use, memory, guardrails, and multi-step task execution, which is the core meaning of Agentic Systems.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Appears in growing number of developer JDs and tooling discussions, but is not yet a universal hiring staple; market signal is rising adoption alongside other AI coding assistants rather than broad standardization.
Anthropic ·proprietary ·since 2024 (0.93)
Claude Code is a specific Anthropic coding assistant/CLI tool name and is unlikely to be mistaken for a different catalog skill in typical job descriptions.
Not versioned
Tool ·ai_coding_assistant_tool confidence 0.93
By the Tool vs Framework rule, Claude Code is software you operate directly as a user to assist coding rather than a codebase you build applications inside.
- Category
- Tool
- Sub-category
- ai_coding_assistant_tool
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Automation Scripting and CLI Catalog dimension db id 48
Library dimension (catalog)
Roles linked in library: Azure Cloud Engineer, Cloud Engineer
-
Automation Scripting and CLI Catalog dimension db id 48
Library dimension (catalog)
Roles linked in library: Azure Cloud Engineer, Cloud Engineer
Locked dimensions (v3 placement)
-
Automation Scripting and CLI
Reuses catalog slug
Uses command-line tools and scripts to automate repeatable engineering tasks and interact with development environments. Claude Code fits here because it is a CLI-based coding assistant used to inspect, edit, and execute code workflows from the terminal.
-
Automation Scripting and CLI
Reuses catalog slug
Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
Cursor appears in a growing number of developer job posts and AI-tooling stacks, but it is not yet a universal hiring staple like VS Code or GitHub Copilot.
Anysphere ·proprietary ·since 2023 (0.98)
Could be confused with: cursor_ai
"Cursor" is a generic term and can refer to the AI code editor or the mouse pointer/cursor in UI contexts. A JD extractor could plausibly confuse it with the related catalog skill "cursor_ai".
Not versioned
Tool ·ai_code_editor confidence 0.90
Cursor is software you run locally as an editor, so by the Tool vs Framework rule it is a Tool rather than a framework or platform.
- Category
- Tool
- Sub-category
- ai_code_editor
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
AI Coding Assistant Usage
Pipeline tentative id
Using AI-powered code editors and assistants to draft, edit, and navigate code faster. Cursor belongs here as a developer tool centered on AI-assisted programming workflows rather than a language or framework itself.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
OpenAI Codex is increasingly referenced in AI-assisted coding JDs and product stacks, but it is not yet a universal hiring staple like Python or AWS.
OpenAI ·proprietary ·since 2021 (0.98)
“Codex” in a JD would usually refer to OpenAI’s code-generation platform; it’s not a common overloaded skill name in this catalog context.
Not versioned
Platform ·ai_code_generation_platform confidence 0.72
By the Vendor SaaS = Platform rule, Codex is best treated as a hosted AI product/API environment rather than software users run locally.
- Category
- Platform
- Sub-category
- ai_code_generation_platform
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Version strategy
- NOT_APPLICABLE
Dimensions (API 2 worklist)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
Locked dimensions (v3 placement)
-
Code Generation Assistants
Pipeline tentative id
Tools and workflows for AI-assisted code generation, editing, and refactoring. Codex belongs here because it is a code-focused assistant used to produce and transform source code from natural-language prompts.
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Python | in_db |
Analytical Programming Languages
analytical-programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Automation and Scripting for Operations
automation-and-scripting-for-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Network Automation and Scripting
network-automation-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Python | in_db |
Programming Languages for Backend Systems
programming-languages-for-backend-systems
|
✓ | — | 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 Security Work
programming-languages-for-security-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Test Automation
programming-languages-for-test-automation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Security Automation and Scripting
security-automation-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TypeScript | in_db |
Frontend Programming Languages
frontend-programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TypeScript | in_db |
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| TypeScript | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TypeScript | in_db |
Programming Languages for Test Automation
programming-languages-for-test-automation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Platform Operations
cloud-platform-operations
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Azure | in_db |
Cloud Security Platforms
cloud-security-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GCP | in_db |
Cloud Security Platforms
cloud-security-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| LLM | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prompting | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Context Management | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | New skill saved · Existing dimension (library) · Role↔dimension saved | |
| Context Management | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Structured Outputs | in_db |
Context Management, Retrieval, and Response Shaping
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) | |
| VLMs | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OCR | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OCR | in_db |
Project Delivery and Coordination
d_init_02
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Layout Parsing | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Containers | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Evaluation | in_db |
Model Evaluation and Validation
model-evaluation-and-validation
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Observability | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Failure Analysis | in_db |
Test Evidence, Defect Reporting, and Triage
test-evidence-defect-reporting-and-triage
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Failure Analysis | in_db |
MySQL Operational Monitoring, Logging, and Diagnostics
mysql-operational-monitoring-logging-and-diagnostics
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
Context Management, Retrieval, and Grounding for Model Calls
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
Cloud Service Integration Patterns
cloud-service-integration-patterns
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Hybrid Retrieval | in_db |
Context Assembly and Retrieval for Model Calls
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) | |
| Reranking | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | New skill saved · Existing dimension (library) · Role↔dimension saved | |
| Vision-Language Models | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Multimodal Document Understanding | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agentic Systems | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Claude Code | in_db |
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Cursor | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Codex | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Evaluation | in_db |
Model Evaluation and Validation
d_init_01
|
✓ | — | New skill saved · Existing dimension (embedding dedup) · Role↔dimension skipped (dimension not under chosen role) | |
| Failure Analysis | in_db |
Test Evidence, Defect Reporting, and Triage
d_merge_01
|
✓ | — | New skill saved · Existing dimension (reconciliation merge) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_added | LLM | 2648 |
| canonical_skill_added | Prompting | 2649 |
| canonical_skill_added | Context Management | 2650 |
| canonical_skill_added | Structured Outputs | 2651 |
| canonical_skill_added | VLMs | 2652 |
| canonical_skill_added | OCR | 2653 |
| canonical_skill_added | Layout Parsing | 2654 |
| canonical_skill_added | Containers | 2655 |
| canonical_skill_added | Evaluation | 2656 |
| canonical_skill_added | Observability | 2657 |
| canonical_skill_added | Failure Analysis | 2658 |
| canonical_skill_added | RAG | 2659 |
| canonical_skill_added | Hybrid Retrieval | 2660 |
| canonical_skill_added | Reranking | 2661 |
| canonical_skill_added | Vision-Language Models | 2662 |
| canonical_skill_added | Multimodal Document Understanding | 2663 |
| canonical_skill_added | Agentic Systems | 2664 |
| canonical_skill_added | Claude Code | 2665 |
| canonical_skill_added | Cursor | 2666 |
| canonical_skill_added | Codex | 2667 |
| dimension_skill_link | LLM ↔ Version Control Systems | 365 |
| dimension_skill_link | Prompting ↔ Version Control Systems | 365 |
| dimension_skill_link | Context Management ↔ Context Management and Retrieval | 264 |
| dimension_skill_link | Context Management ↔ Version Control Systems | 365 |
| dimension_skill_link | Structured Outputs ↔ Context Management, Retrieval, and Response Shaping | 264 |
| dimension_skill_link | VLMs ↔ Version Control Systems | 365 |
| dimension_skill_link | OCR ↔ Version Control Systems | 365 |
| dimension_skill_link | OCR ↔ Project Delivery and Coordination | 366 |
| dimension_skill_link | Layout Parsing ↔ Version Control Systems | 365 |
| dimension_skill_link | Containers ↔ Version Control Systems | 365 |
| dimension_skill_link | Evaluation ↔ Model Evaluation and Validation | 86 |
| dimension_skill_link | Observability ↔ Version Control Systems | 365 |
| dimension_skill_link | Failure Analysis ↔ Test Evidence, Defect Reporting, and Triage | 241 |
| dimension_skill_link | Failure Analysis ↔ MySQL Operational Monitoring, Logging, and Diagnostics | 166 |
| dimension_skill_link | RAG ↔ Context Management, Retrieval, and Grounding for Model Calls | 264 |
| dimension_skill_link | RAG ↔ Cloud Service Integration Patterns | 188 |
| dimension_skill_link | Hybrid Retrieval ↔ Context Assembly and Retrieval for Model Calls | 264 |
| dimension_skill_link | Reranking ↔ Context Management and Retrieval | 264 |
| dimension_skill_link | Vision-Language Models ↔ Version Control Systems | 365 |
| dimension_skill_link | Multimodal Document Understanding ↔ Version Control Systems | 365 |
| dimension_skill_link | Agentic Systems ↔ Version Control Systems | 365 |
| dimension_skill_link | Claude Code ↔ Automation Scripting and CLI | 48 |
| dimension_skill_link | Cursor ↔ Version Control Systems | 365 |
| dimension_skill_link | Codex ↔ Version Control Systems | 365 |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "fail"
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "TypeScript"
},
{
"is_primary": true,
"skill_name": "LLM"
},
{
"is_primary": true,
"skill_name": "Prompting"
},
{
"is_primary": true,
"skill_name": "Context Management"
},
{
"is_primary": true,
"skill_name": "Structured Outputs"
},
{
"is_primary": true,
"skill_name": "VLMs"
},
{
"is_primary": true,
"skill_name": "OCR"
},
{
"is_primary": true,
"skill_name": "Layout Parsing"
},
{
"is_primary": true,
"skill_name": "Containers"
},
{
"is_primary": true,
"skill_name": "CI/CD"
},
{
"is_primary": true,
"skill_name": "Azure"
},
{
"is_primary": true,
"skill_name": "GCP"
},
{
"is_primary": true,
"skill_name": "Evaluation"
},
{
"is_primary": true,
"skill_name": "Observability"
},
{
"is_primary": true,
"skill_name": "Failure Analysis"
},
{
"is_primary": false,
"skill_name": "RAG"
},
{
"is_primary": false,
"skill_name": "Hybrid Retrieval"
},
{
"is_primary": false,
"skill_name": "Reranking"
},
{
"is_primary": false,
"skill_name": "Vision-Language Models"
},
{
"is_primary": false,
"skill_name": "Multimodal Document Understanding"
},
{
"is_primary": false,
"skill_name": "Agentic Systems"
},
{
"is_primary": false,
"skill_name": "Claude Code"
},
{
"is_primary": false,
"skill_name": "Cursor"
},
{
"is_primary": false,
"skill_name": "Codex"
}
],
"jd_role": null,
"nano_parsed": {
"JD_type": "fail"
},
"run_id": null
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 608,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 5,
"display_name": "Python",
"id": 393,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 54,
"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": 14,
"existing_alias_text": "TypeScript",
"input_term": "TypeScript",
"matched_canonical": {
"category_id": 5,
"display_name": "TypeScript",
"id": 2,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "typescript",
"sub_category_id": 54,
"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": 3376,
"existing_alias_text": "CI/CD",
"input_term": "CI/CD",
"matched_canonical": {
"category_id": 7,
"display_name": "CI/CD",
"id": 2579,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "ci-cd",
"sub_category_id": 2102,
"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": 349,
"existing_alias_text": "Azure",
"input_term": "Azure",
"matched_canonical": {
"category_id": 13,
"display_name": "Azure",
"id": 164,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "azure",
"sub_category_id": 161,
"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": 3043,
"existing_alias_text": "GCP",
"input_term": "GCP",
"matched_canonical": {
"category_id": 13,
"display_name": "GCP",
"id": 2304,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "gcp",
"sub_category_id": 161,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
},
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "Virtualization Engineer",
"id": 26,
"rationale": null,
"role_archetype": null,
"slug": "virtualization-engineer",
"source": "db"
},
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Manual Tester",
"id": 17,
"rationale": null,
"role_archetype": null,
"slug": "manual-tester",
"source": "db"
},
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
},
{
"display_name": "Cloud Architect",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
],
"chosen_role": {
"display_name": "AI Engineer",
"id": 12,
"rationale": "This role leverages multiple primary skills including Python, TypeScript, and AI workflows.",
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation and Scripting for Operations",
"id": 361,
"rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
"slug": "automation-and-scripting-for-operations",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Virtualization Engineer",
"id": 26,
"rationale": null,
"role_archetype": null,
"slug": "virtualization-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Network Automation and Scripting",
"id": 285,
"rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
"slug": "network-automation-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Backend Systems",
"id": 140,
"rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
"slug": "programming-languages-for-backend-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 67,
"rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 113,
"rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Security Work",
"id": 328,
"rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
"slug": "programming-languages-for-security-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Security Automation and Scripting",
"id": 258,
"rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
"slug": "security-automation-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Frontend Programming Languages",
"id": 1,
"rationale": "Languages used to implement browser-side application logic, component behavior, and UI state. This is the core code layer for frontend features and interactive experiences.",
"slug": "frontend-programming-languages",
"source": "db"
},
"input_skill": "TypeScript",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"input_skill": "TypeScript",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 113,
"rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "TypeScript",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"input_skill": "TypeScript",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "CI/CD",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
"id": 26,
"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
"source": "db"
},
"input_skill": "Azure",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Platforms",
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{
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{
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{
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"with_role": "AI Engineer"
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"mlops",
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],
"requires": [],
"skill_id": "context-management",
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},
"skill_id": "context-management",
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},
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{
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},
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{
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{
"dimension": {
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}
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{
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{
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{
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],
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},
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"name": "OCR",
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{
"aliases_in_db": [],
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"dimension": {
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"display_name": "Version Control Systems",
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},
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],
"skill_id": "claude-code",
"suppress_on_match": []
},
"skill_id": "claude-code",
"split_log": [],
"typed": {
"alternatives_considered": [
"Platform: ruled out \u2014 it is not a hosted multi-tenant environment with APIs in the sense of a general application platform.",
"Framework: ruled out \u2014 users do not build applications inside Claude Code."
],
"confidence": 0.93,
"name": "Claude Code",
"reasoning": "By the Tool vs Framework rule, Claude Code is software you operate directly as a user to assist coding rather than a codebase you build applications inside.",
"skill_id": "claude-code",
"subtype": "ai_coding_assistant_tool",
"type": "Tool"
},
"warnings": [
"stage3_post_filter_dropped_catalog_only_locked_dims:41-\u003e2"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Cursor",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Cursor",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Tool",
"skill_nature": "TOOL",
"sub_category": "ai_code_editor",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "EMERGING"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": true,
"confused_with": [
"cursor_ai"
],
"reasoning": "\"Cursor\" is a generic term and can refer to the AI code editor or the mouse pointer/cursor in UI contexts. A JD extractor could plausibly confuse it with the related catalog skill \"cursor_ai\"."
},
"context_keywords": {
"context_keywords": [
"VS Code",
"AI code editor",
"code completion",
"chat with codebase",
"multi-file edits",
"refactoring",
"inline suggestions",
"GitHub Copilot",
"Claude",
"OpenAI",
"terminal integration",
"project indexing",
"diff view",
"prompting",
"autocomplete"
]
},
"maturity": {
"confidence": 0.84,
"maturity": "emerging",
"reasoning": "Cursor appears in a growing number of developer job posts and AI-tooling stacks, but it is not yet a universal hiring staple like VS Code or GitHub Copilot."
},
"skill_id": "cursor",
"vendor_license": {
"confidence": 0.98,
"license": "proprietary",
"vendor": "Anysphere",
"year_introduced": 2023
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Using AI-powered code editors and assistants to draft, edit, and navigate code faster. Cursor belongs here as a developer tool centered on AI-assisted programming workflows rather than a language or framework itself.",
"exemplar_skills": [
"Cursor",
"GitHub Copilot",
"Codeium",
"AI-assisted refactoring",
"prompt engineering for coding",
"codebase chat"
],
"in_scope": "Cursor, AI code completion, prompt-driven code edits, codebase chat, refactoring assistance, inline suggestions, agentic coding workflows",
"name": "AI Coding Assistant Usage",
"out_of_scope": "text editors without AI assistance, programming language syntax, IDE plugin development, source control workflows, which belong to other dimensions",
"overlap_flags": [
{
"reason": "Cursor can be used alongside CLI automation, but the assistant itself is primarily a coding productivity tool rather than command-line automation.",
"with_dim_id": "automation-scripting-and-cli",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer, Cloud Engineer"
},
{
"reason": "Cursor may help edit frontend code, but framework knowledge is separate from the tool used to author it.",
"with_dim_id": "component-frameworks-and-rendering",
"with_dim_name": null,
"with_role": "Frontend Engineer, Full Stack Developer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Cursor",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "cursor"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"javascript",
"go",
"objective-c",
"ios",
"android",
"sql",
"pinecone",
"quicknode",
"slither",
"snapshot"
],
"requires": [],
"skill_id": "cursor",
"suppress_on_match": []
},
"skill_id": "cursor",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.9,
"name": "Cursor",
"reasoning": "Cursor is software you run locally as an editor, so by the Tool vs Framework rule it is a Tool rather than a framework or platform.",
"skill_id": "cursor",
"subtype": "ai_code_editor",
"type": "Tool"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Codex",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Codex",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Platform",
"skill_nature": "PLATFORM",
"sub_category": "ai_code_generation_platform",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "EMERGING"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cCodex\u201d in a JD would usually refer to OpenAI\u2019s code-generation platform; it\u2019s not a common overloaded skill name in this catalog context."
},
"context_keywords": {
"context_keywords": [
"prompt engineering",
"code completion",
"IDE integration",
"API access",
"sandbox",
"chat-based coding",
"repository context",
"unit tests",
"refactoring",
"pair programming",
"natural language to code",
"GitHub",
"VS Code",
"autocomplete",
"code review"
]
},
"maturity": {
"confidence": 0.78,
"maturity": "emerging",
"reasoning": "OpenAI Codex is increasingly referenced in AI-assisted coding JDs and product stacks, but it is not yet a universal hiring staple like Python or AWS."
},
"skill_id": "codex",
"vendor_license": {
"confidence": 0.98,
"license": "proprietary",
"vendor": "OpenAI",
"year_introduced": 2021
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Tools and workflows for AI-assisted code generation, editing, and refactoring. Codex belongs here because it is a code-focused assistant used to produce and transform source code from natural-language prompts.",
"exemplar_skills": [
"Codex",
"AI code generation",
"code completion",
"refactoring assistance",
"prompt-to-code",
"repository-aware coding assistant"
],
"in_scope": "Codex, AI code generation, prompt-to-code workflows, code completion, refactoring assistance, repository-aware coding assistants, IDE copilots, natural-language programming",
"name": "Code Generation Assistants",
"out_of_scope": "Model serving deployment, inference pipelines, context retrieval for general LLM apps, test automation frameworks, backend API design, which belong to other dimensions",
"overlap_flags": [
{
"reason": "Code assistants often depend on retrieval and context packaging, but this dimension is about the coding workflow rather than the retrieval subsystem.",
"with_dim_id": "context-management-and-retrieval",
"with_dim_name": null,
"with_role": "AI Engineer"
},
{
"reason": "Some code-assistant usage overlaps with scripting automation, but Codex is centered on AI-assisted programming rather than operational scripting.",
"with_dim_id": "automation-scripting-and-cli",
"with_dim_name": null,
"with_role": "Azure Cloud Engineer, Cloud Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Codex",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 1 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [],
"skill_id": "codex"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"git",
"argo-cd",
"alchemy",
"certora",
"solidity",
"the-graph",
"arbitrum",
"polygon",
"cosmwasm",
"dex"
],
"requires": [],
"skill_id": "codex",
"suppress_on_match": []
},
"skill_id": "codex",
"split_log": [],
"typed": {
"alternatives_considered": [
"Service: ruled out \u2014 the name refers to the broader hosted product rather than a single managed capability inside another platform.",
"Tool: ruled out \u2014 it is typically consumed as a hosted service/API, not run as user-operated software."
],
"confidence": 0.72,
"name": "Codex",
"reasoning": "By the Vendor SaaS = Platform rule, Codex is best treated as a hosted AI product/API environment rather than software users run locally.",
"skill_id": "codex",
"subtype": "ai_code_generation_platform",
"type": "Platform"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"LLM",
"Prompting",
"Context Management",
"Structured Outputs",
"VLMs",
"OCR",
"Layout Parsing",
"Containers",
"Evaluation",
"Observability",
"Failure Analysis",
"RAG",
"Hybrid Retrieval",
"Reranking",
"Vision-Language Models",
"Multimodal Document Understanding",
"Agentic Systems",
"Claude Code",
"Cursor",
"Codex"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "AI Engineer",
"id": 12,
"rationale": "This role leverages multiple primary skills including Python, TypeScript, and AI workflows.",
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "TypeScript",
"tag": "in_db"
},
{
"skill": "LLM",
"tag": "new"
},
{
"skill": "Prompting",
"tag": "new"
},
{
"skill": "Context Management",
"tag": "new"
},
{
"skill": "Structured Outputs",
"tag": "new"
},
{
"skill": "VLMs",
"tag": "new"
},
{
"skill": "OCR",
"tag": "new"
},
{
"skill": "Layout Parsing",
"tag": "new"
},
{
"skill": "Containers",
"tag": "new"
},
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "GCP",
"tag": "in_db"
},
{
"skill": "Evaluation",
"tag": "new"
},
{
"skill": "Observability",
"tag": "new"
},
{
"skill": "Failure Analysis",
"tag": "new"
},
{
"skill": "RAG",
"tag": "new"
},
{
"skill": "Hybrid Retrieval",
"tag": "new"
},
{
"skill": "Reranking",
"tag": "new"
},
{
"skill": "Vision-Language Models",
"tag": "new"
},
{
"skill": "Multimodal Document Understanding",
"tag": "new"
},
{
"skill": "Agentic Systems",
"tag": "new"
},
{
"skill": "Claude Code",
"tag": "new"
},
{
"skill": "Cursor",
"tag": "new"
},
{
"skill": "Codex",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"dimension_id": 82,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"dimension_id": 48,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation and Scripting for Operations",
"id": 361,
"rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
"slug": "automation-and-scripting-for-operations",
"source": "db"
},
"dimension_id": 361,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Virtualization Engineer",
"id": 26,
"rationale": null,
"role_archetype": null,
"slug": "virtualization-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Network Automation and Scripting",
"id": 285,
"rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
"slug": "network-automation-and-scripting",
"source": "db"
},
"dimension_id": 285,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Backend Systems",
"id": 140,
"rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
"slug": "programming-languages-for-backend-systems",
"source": "db"
},
"dimension_id": 140,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 67,
"rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 67,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 113,
"rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 113,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Security Work",
"id": 328,
"rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
"slug": "programming-languages-for-security-work",
"source": "db"
},
"dimension_id": 328,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Security Automation and Scripting",
"id": 258,
"rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
"slug": "security-automation-and-scripting",
"source": "db"
},
"dimension_id": 258,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
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"display_name": "Frontend Programming Languages",
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"rationale": "Languages used to implement browser-side application logic, component behavior, and UI state. This is the core code layer for frontend features and interactive experiences.",
"slug": "frontend-programming-languages",
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},
"dimension_id": 1,
"input_skill": "TypeScript",
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Frontend Engineer",
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"slug": "frontend-engineer",
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},
{
"display_name": "Full Stack Developer",
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"slug": "full-stack-developer",
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}
],
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"skill_id": 2,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "TypeScript",
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"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "AI Engineer",
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"slug": "ai-engineer",
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}
],
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},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
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"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 113,
"input_skill": "TypeScript",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
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}
],
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"skill_id": 2,
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},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "TypeScript",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
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"slug": "automation-tester",
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}
],
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"skill_id": 2,
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},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
"dimension_id": 365,
"input_skill": "CI/CD",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2579,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
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"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
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},
"dimension_id": 26,
"input_skill": "Azure",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
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"slug": "devops-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 164,
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},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Cloud Security Platforms",
"id": 332,
"rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
"slug": "cloud-security-platforms",
"source": "db"
},
"dimension_id": 332,
"input_skill": "Azure",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
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"slug": "cybersecurity-engineer",
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}
],
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"skill_id": 164,
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},
{
"chosen_role_id": 12,
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"display_name": "Cloud Security Platforms",
"id": 332,
"rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
"slug": "cloud-security-platforms",
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},
"dimension_id": 332,
"input_skill": "GCP",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 2304,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
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"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
"dimension_id": 365,
"input_skill": "LLM",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2648,
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},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
"dimension_id": 365,
"input_skill": "Prompting",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2649,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Context Management and Retrieval",
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"rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
"slug": "context-management-and-retrieval",
"source": "db"
},
"dimension_id": 264,
"input_skill": "Context Management",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "AI Engineer",
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"role_archetype": null,
"slug": "ai-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 2650,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Context Management",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2650,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": null,
"display_name": "Context Management, Retrieval, and Response Shaping",
"id": null,
"rationale": "Preparing, selecting, retrieving, and packaging the right context for model calls so responses are relevant, grounded, and usable. Includes prompt context selection, retrieval-augmented generation, context window management, schema-constrained generation, Structured Outputs, JSON schema prompting, tool-call argument shaping, and other response-grounding techniques that control what information the model sees and how its output is structured.",
"slug": "d_merge_01",
"source": "llm"
},
"dimension_id": 264,
"input_skill": "Structured Outputs",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2651,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "VLMs",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2652,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
"dimension_id": 365,
"input_skill": "OCR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2653,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Project Delivery and Coordination",
"id": 366,
"rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
"slug": "d_init_02",
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},
"dimension_id": 366,
"input_skill": "OCR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2653,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Layout Parsing",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2654,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
"dimension_id": 365,
"input_skill": "Containers",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2655,
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},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Model Evaluation and Validation",
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},
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "Data Scientist",
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}
],
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"skill_id": 2656,
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},
{
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"display_name": "Version Control Systems",
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"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
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},
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"input_skill": "Observability",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [],
"skill_dimension_saved": true,
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Test Evidence, Defect Reporting, and Triage",
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"slug": "test-evidence-defect-reporting-and-triage",
"source": "db"
},
"dimension_id": 241,
"input_skill": "Failure Analysis",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Manual Tester",
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"role_archetype": null,
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}
],
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
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"slug": "mysql-operational-monitoring-logging-and-diagnostics",
"source": "db"
},
"dimension_id": 166,
"input_skill": "Failure Analysis",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
"display_name": "MySQL DBA",
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"slug": "mysql-dba",
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}
],
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"skill_id": 2658,
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},
{
"chosen_role_id": 12,
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"difficulty_hint": null,
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"slug": "d_merge_01",
"source": "llm"
},
"dimension_id": 264,
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2659,
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},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Cloud Service Integration Patterns",
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"rationale": "Covers how cloud services and workloads connect through APIs, events, shared services, and integration boundaries. This cluster is coherent because architects must define interaction patterns that preserve decoupling, security, and operability.",
"slug": "cloud-service-integration-patterns",
"source": "db"
},
"dimension_id": 188,
"input_skill": "RAG",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
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"slug": "cloud-architect",
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}
],
"skill_dimension_saved": true,
"skill_id": 2659,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": null,
"display_name": "Context Assembly and Retrieval for Model Calls",
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"rationale": "Preparing, selecting, retrieving, reranking, and packaging information for model calls so responses stay relevant and grounded. Includes hybrid retrieval, vector and keyword search, chunk selection, context assembly, retrieval-augmented generation, document grounding, and query rewriting when used to build the context supplied to a model.",
"slug": "d_merge_01",
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},
"dimension_id": 264,
"input_skill": "Hybrid Retrieval",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2660,
"skill_tag": "in_db",
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},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Context Management and Retrieval",
"id": 264,
"rationale": "Preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. This is a distinct cluster because AI features often depend on what information is included, summarized, or retrieved at call time.",
"slug": "context-management-and-retrieval",
"source": "db"
},
"dimension_id": 264,
"input_skill": "Reranking",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "AI Engineer",
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"slug": "ai-engineer",
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}
],
"skill_dimension_saved": true,
"skill_id": 2661,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Vision-Language Models",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2662,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Multimodal Document Understanding",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2663,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Agentic Systems",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2664,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"dimension_id": 48,
"input_skill": "Claude Code",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2665,
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},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Version Control Systems",
"id": 365,
"rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 365,
"input_skill": "Cursor",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2666,
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},
{
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"display_name": "Version Control Systems",
"id": 365,
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"source": "db"
},
"dimension_id": 365,
"input_skill": "Codex",
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"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2667,
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},
{
"chosen_role_id": 12,
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"difficulty_hint": "well_known",
"display_name": "Model Evaluation and Validation",
"id": null,
"rationale": "Covers assessing model quality, reliability, and fit for purpose using offline and online validation methods. This skill belongs here because evaluation is the core activity for measuring whether a model or system meets expected performance criteria.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": 86,
"input_skill": "Evaluation",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (embedding dedup) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2656,
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"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
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"display_name": "Test Evidence, Defect Reporting, and Triage",
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"slug": "d_merge_01",
"source": "llm"
},
"dimension_id": 241,
"input_skill": "Failure Analysis",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "New skill saved \u00b7 Existing dimension (reconciliation merge) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2658,
"skill_tag": "in_db",
"skipped_reason": null
}
],
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},
"planner_output": null,
"run_id": "62310ed0-c8e6-4590-a4e8-edafce525331"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.