Pipeline run
3ebebd1c-ed76-46b5-9b1a-4670b8e8d377
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
The primary skills revolve around AI programming and deployment, which aligns with the role of an AI Engineer.
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) |
Aliases — catalog
- Nebular (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Ui Component Framework
- Vendor
- Nebular Team
- License
- mit
- Year introduced
- 2017
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Nebular appears in relatively few job postings compared with mainstream Angular UI libraries, and its usage is concentrated in Angular admin/dashboard projects rather than broad frontend hiring.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2181
- 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 — from this run (catalog unavailable)
- Prompting (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2162
- 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 — from this run (catalog unavailable)
- Context Management (CANONICAL)
Skill profile (library / DB)
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 2163
- Extractable
- True
- Also category
- False
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)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — from this run (catalog unavailable)
- Structured Outputs (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2164
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — from this run (catalog unavailable)
- Document Processing (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 16
- Sub-category id
- 2187
- 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 — from this run (catalog unavailable)
- VLMs (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2165
- 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 — from this run (catalog unavailable)
- OCR (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2166
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Project Delivery and Coordination Catalog dimension db id 366
Library dimension (catalog)
-
Version Control Systems Catalog dimension db id 365
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Project Delivery and Coordination
d_init_02
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- observables (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Streams
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Observables are widely used in RxJS/Angular and appear in many frontend and backend JDs for reactive programming and async event streams, indicating broad hiring-pipeline familiarity.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2167
- 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
- Subjects (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Subject
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Reactive Subjects appear in RxJS/ReactiveX JDs far less often than core React or Python; market demand is mostly in specialized event-streaming codebases, not broad hiring pipelines.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 2168
- 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 — 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) |
Aliases — catalog
- switchMap (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Operator
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common RxJS/ReactiveX operator in many frontend and backend JDs; widely documented in Angular/RxJS job listings and used as a standard higher-order mapping pattern.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2169
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Model Evaluation and Validation Catalog dimension db id 86
Library dimension (catalog)
Roles linked in library: Data Scientist
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model Evaluation and Validation
model-evaluation-and-validation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- mergeMap (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Operator
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common RxJS operator in Angular/TypeScript JDs and tutorials; widely used for async stream flattening, with strong GitHub/docs presence and no sunset or replacement signal.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2170
- 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
- combineLatest (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Operator
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: combineLatest is a standard Rx operator widely used in RxJS/RxJava job descriptions and docs; it’s a core reactive primitive rather than a niche library feature.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2171
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
MySQL Operational Monitoring, Logging, and Diagnostics Catalog dimension db id 166
Library dimension (catalog)
Roles linked in library: MySQL DBA
-
Test Evidence, Defect Reporting, and Triage Catalog dimension db id 241
Library dimension (catalog)
Roles linked in library: Manual Tester
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
MySQL Operational Monitoring, Logging, and Diagnostics
mysql-operational-monitoring-logging-and-diagnostics
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Test Evidence, Defect Reporting, and Triage
test-evidence-defect-reporting-and-triage
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- debounceTime (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Reactive Operator
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common RxJS operator widely used in Angular/TypeScript JDs for search/input throttling; appears in many tutorials and codebases, with no vendor sunset or replacement trend.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2172
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Service Integration Patterns Catalog dimension db id 188
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Service Integration Patterns
cloud-service-integration-patterns
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — from this run (catalog unavailable)
- Hybrid Retrieval (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2173
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- SCSS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Stylesheet Language
- Vendor
- Hasslein Studios
- License
- mit
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: SCSS is widely listed in front-end job descriptions and remains a common Sass syntax in production stacks; it’s not sunset and is still supported by the Sass ecosystem.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2174
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Context Management and Retrieval Catalog dimension db id 264
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
Appears increasingly in AI/ML job descriptions and vendor docs for LLM evaluation, but there is no universal hiring-pipeline standard yet; market demand is growing alongside eval tooling adoption.
(0.99)
“Eval Design” is a fairly specific shorthand for evaluation design methodology in ML/analytics contexts. It’s unlikely to be reasonably confused with a different catalog skill in typical job descriptions.
Not versioned
Methodology ·evaluation_design_methodology confidence 0.88
Eval Design is best treated as a methodology because it describes a way of working for creating and running evaluations, not a software artifact or system shape.
- Category
- Methodology
- Sub-category
- evaluation_design_methodology
- Skill nature
- METHODOLOGY
- 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)
-
Evaluation Design for AI Systems
Pipeline tentative id
Designing evaluation setups for AI features and model outputs, including what to measure, how to sample cases, and how to structure rubrics or test sets. This fits Eval Design because the skill is about defining reliable assessment methods before analysis or deployment.
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)
- Vision-Language Models (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2175
- 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 — from this run (catalog unavailable)
- Multimodal Document Understanding (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2176
- 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 — from this run (catalog unavailable)
- Agentic Systems (CANONICAL)
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 2177
- 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) |
Skill enrichment (orchestrator / LLM)
Appears increasingly in JDs for AI/LLM engineer roles and vendor docs, but market usage is still fragmented across frameworks like LangChain, OpenAI tools, and custom orchestration rather than universal hiring demand.
(0.98)
The phrase is fairly specific and usually refers to tooling for AI agents. In typical JDs it is unlikely to be mistaken for a different catalog skill.
Not versioned
Tool ·agent_tooling confidence 0.88
By the Tool vs Framework rule, Agent Tooling is software you operate to support agents rather than a structured codebase you build applications inside.
- Category
- Tool
- Sub-category
- agent_tooling
- Skill nature
- TOOL
- 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
-
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
Covers preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. Agent tooling belongs here when it manages prompts, memory, retrieval, and tool outputs as inputs to an LLM agent loop.
-
Agent Orchestration Tooling
Pipeline tentative id
Covers the software used to build, coordinate, and observe LLM agents that can call tools, plan steps, and execute workflows. This fits Agent Tooling when the emphasis is on agent loops, tool invocation, state tracking, and multi-step execution behavior.
-
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) |
Aliases — catalog
- theming (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Ui Theming
- Confidence
- 0.88
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: UI theming is broadly expected in frontend JDs and design-system roles; market signals include widespread support in React/Angular/Vue ecosystems and vendor docs for CSS variables, Tailwind, and component libraries.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 2178
- Extractable
- True
- Also category
- False
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
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — from this run (catalog unavailable)
- Cursor (CANONICAL)
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 11
- Sub-category id
- 2179
- 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
- Angular Material (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Ui Component Framework
- Vendor
- License
- apache_2
- Year introduced
- 2014
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in Angular front-end job descriptions and widely used in enterprise apps; Angular’s official Material component library remains actively maintained, with no vendor sunset signal.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 2180
- 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) |
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) | |
| LLMs | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prompting | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Context Management | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Context Management | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Structured Outputs | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Document Processing | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| VLMs | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OCR | in_db |
Project Delivery and Coordination
d_init_02
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| OCR | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Layout Parsing | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Containers | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| 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) | |
| Evaluation | in_db |
Model Evaluation and Validation
model-evaluation-and-validation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Observability | in_db |
Version Control Systems
d_init_01
|
✓ | — | 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
|
✓ | — | 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
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
Cloud Service Integration Patterns
cloud-service-integration-patterns
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Hybrid Retrieval | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Reranking | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Vision-Language Models | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Multimodal Document Understanding | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agentic Systems | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Claude Code | in_db |
Automation Scripting and CLI
automation-scripting-and-cli
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Cursor | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Codex | in_db |
Version Control Systems
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Eval Design | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agent Tooling | in_db |
Context Management and Retrieval
context-management-and-retrieval
|
✓ | ✓ | New skill saved · Existing dimension (library) · Role↔dimension saved | |
| Agent Tooling | in_db |
Version Control Systems
d_init_01
|
✓ | — | New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_added | Eval Design | 2680 |
| canonical_skill_added | Agent Tooling | 2681 |
| dimension_skill_link | Eval Design ↔ Version Control Systems | 365 |
| dimension_skill_link | Agent Tooling ↔ Context Management and Retrieval | 264 |
| dimension_skill_link | Agent Tooling ↔ Version Control Systems | 365 |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Bynd is building the intelligence",
"last_5_words": "and building things that get used."
},
"text": "Bynd is building the intelligence layer for financial services.\n\nWe 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.\n\nWe 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.",
"word_count": 66
},
"certifications": [],
"company_name": "Bynd",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Finance",
"Investment Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [],
"role": "Applied AI Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "The Role",
"heading_was_present": true,
"source_marker": {
"first_5_words": "As an Applied AI Engineer",
"last_5_words": "extraction, analysis, and workflow automation."
},
"text": "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.\n\nYou will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation.",
"word_count": 45
},
{
"bullet_count": 14,
"heading": "What You Will Need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Must-haves\n\nStrong programming ability",
"last_5_words": "or using modern agent tooling"
},
"text": "Must-haves\n\nStrong programming ability in Python and TypeScript\nExperience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs\nExperience building or working with document processing systems such as VLMs for OCR and layout parsing models\nComfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP\nExperience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows\n\nPreferred\n\nExperience with RAG systems, hybrid retrieval, reranking, and eval design\nExperience with vision-language models or multimodal document understanding\nFamiliarity with Azure- or GCP-based AI infrastructure\nFamiliarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence\nExperience building multi-step agentic systems or using modern agent tooling",
"word_count": 174
},
{
"bullet_count": 0,
"heading": "Who You Are",
"heading_was_present": true,
"source_marker": {
"first_5_words": "You thrive in fast-moving environments",
"last_5_words": "accuracy really means in context, and why"
},
"text": "You thrive in fast-moving environments and care deeply about the quality of what you build.\nYou are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well.\nYou 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.\nYou are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.\nYou 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.",
"word_count": 104
}
],
"urls": []
}
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": "LLMs"
},
{
"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": "Document Processing"
},
{
"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": "Eval Design"
},
{
"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": "Agent Tooling"
},
{
"is_primary": false,
"skill_name": "Claude Code"
},
{
"is_primary": false,
"skill_name": "Cursor"
},
{
"is_primary": false,
"skill_name": "Codex"
}
],
"jd_role": {
"display_name": "Applied AI Engineer",
"rationale": null,
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Bynd is building the intelligence",
"last_5_words": "and building things that get used."
},
"text": "Bynd is building the intelligence layer for financial services.\n\nWe 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.\n\nWe 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.",
"word_count": 66
},
"certifications": [],
"company_name": "Bynd",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Finance",
"Investment Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [],
"role": "Applied AI Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "The Role",
"heading_was_present": true,
"source_marker": {
"first_5_words": "As an Applied AI Engineer",
"last_5_words": "extraction, analysis, and workflow automation."
},
"text": "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.\n\nYou will build systems that financial institutions depend on for high-accuracy extraction, analysis, and workflow automation.",
"word_count": 45
},
{
"bullet_count": 14,
"heading": "What You Will Need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Must-haves\n\nStrong programming ability",
"last_5_words": "or using modern agent tooling"
},
"text": "Must-haves\n\nStrong programming ability in Python and TypeScript\nExperience integrating LLMs into production systems, including prompting, context management, structured outputs, and cost-performance tradeoffs\nExperience building or working with document processing systems such as VLMs for OCR and layout parsing models\nComfort with cloud deployment and production systems, including containers, CI/CD, and Azure or GCP\nExperience thinking carefully about system quality, including evaluation, observability, or failure analysis for complex AI workflows\n\nPreferred\n\nExperience with RAG systems, hybrid retrieval, reranking, and eval design\nExperience with vision-language models or multimodal document understanding\nFamiliarity with Azure- or GCP-based AI infrastructure\nFamiliarity with financial services workflows such as investment banking, private equity, equity research, credit, or diligence\nExperience building multi-step agentic systems or using modern agent tooling",
"word_count": 174
},
{
"bullet_count": 0,
"heading": "Who You Are",
"heading_was_present": true,
"source_marker": {
"first_5_words": "You thrive in fast-moving environments",
"last_5_words": "accuracy really means in context, and why"
},
"text": "You thrive in fast-moving environments and care deeply about the quality of what you build.\nYou are ambitious and energized by difficult problems. You like working on things that are technically hard, operationally messy, and valuable when solved well.\nYou 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.\nYou are an owner. You are autonomous, self-directed, and comfortable with ambiguity. You take responsibility for outcomes, not just tasks.\nYou 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.",
"word_count": 104
}
],
"urls": []
},
"run_id": null
}
API 2 — extract-details
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 3616,
"existing_alias_text": "RAG",
"input_term": "RAG",
"matched_canonical": {
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"display_name": "RAG",
"id": 2659,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
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"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"existing_alias_text": "Hybrid Retrieval",
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"matched_canonical": {
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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},
{
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"existing_alias_text": "Claude Code",
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],
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"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": "LLMs",
"llm_role": null,
"roles_from_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": "Prompting",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"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"
},
"input_skill": "Context Management",
"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": "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": "Context Management",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"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"
},
"input_skill": "Structured Outputs",
"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": "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": "Document Processing",
"llm_role": null,
"roles_from_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": "VLMs",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Project Delivery and Coordination",
"id": 366,
"rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
"slug": "d_init_02",
"source": "db"
},
"input_skill": "OCR",
"llm_role": null,
"roles_from_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": "OCR",
"llm_role": null,
"roles_from_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": "Layout Parsing",
"llm_role": null,
"roles_from_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": "Containers",
"llm_role": null,
"roles_from_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",
"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"
},
"input_skill": "Azure",
"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": "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"
},
"input_skill": "GCP",
"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": "Model Evaluation and Validation",
"id": 86,
"rationale": "Techniques for assessing model quality, robustness, and uncertainty before recommendations are made. This includes choosing metrics, validating generalization, and understanding error tradeoffs.",
"slug": "model-evaluation-and-validation",
"source": "db"
},
"input_skill": "Evaluation",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"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": "Observability",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
"id": 166,
"rationale": "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.",
"slug": "mysql-operational-monitoring-logging-and-diagnostics",
"source": "db"
},
"input_skill": "Failure Analysis",
"llm_role": null,
"roles_from_db": [
{
"display_name": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Test Evidence, Defect Reporting, and Triage",
"id": 241,
"rationale": "Capturing and organizing clear evidence from manual testing, including what was tested, what was observed, and how to reproduce issues, then communicating defect severity, impact, and triage notes so teams can prioritize fixes and make release decisions. This includes test notes, screenshots, screen recordings, execution logs, reproduction steps, bug reports, severity/priority assessment, and concise status or coverage summaries.",
"slug": "test-evidence-defect-reporting-and-triage",
"source": "db"
},
"input_skill": "Failure Analysis",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Manual Tester",
"id": 17,
"rationale": null,
"role_archetype": null,
"slug": "manual-tester",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Service Integration Patterns",
"id": 188,
"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"
},
"input_skill": "RAG",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"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"
},
"input_skill": "RAG",
"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": "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"
},
"input_skill": "Hybrid Retrieval",
"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": "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"
},
"input_skill": "Reranking",
"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": "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": "Vision-Language Models",
"llm_role": null,
"roles_from_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": "Multimodal Document Understanding",
"llm_role": null,
"roles_from_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": "Agentic Systems",
"llm_role": null,
"roles_from_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": "Claude Code",
"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": "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": []
},
{
"dimension": {
"difficulty_hint": "well_known",
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"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"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"
},
"input_skill": "Reranking",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "Reranking",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "Eval Design",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Eval Design",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Methodology",
"skill_nature": "METHODOLOGY",
"sub_category": "evaluation_design_methodology",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "EMERGING"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cEval Design\u201d is a fairly specific shorthand for evaluation design methodology in ML/analytics contexts. It\u2019s unlikely to be reasonably confused with a different catalog skill in typical job descriptions."
},
"context_keywords": {
"context_keywords": [
"rubric",
"benchmarking",
"gold standard",
"inter-rater reliability",
"A/B testing",
"control group",
"experimental design",
"validity",
"reliability",
"precision",
"recall",
"confusion matrix",
"ground truth",
"sampling",
"statistical significance"
]
},
"maturity": {
"confidence": 0.78,
"maturity": "emerging",
"reasoning": "Appears increasingly in AI/ML job descriptions and vendor docs for LLM evaluation, but there is no universal hiring-pipeline standard yet; market demand is growing alongside eval tooling adoption."
},
"skill_id": "eval-design",
"vendor_license": {
"confidence": 0.99,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Designing evaluation setups for AI features and model outputs, including what to measure, how to sample cases, and how to structure rubrics or test sets. This fits Eval Design because the skill is about defining reliable assessment methods before analysis or deployment.",
"exemplar_skills": [
"Eval Design",
"evaluation rubric design",
"benchmark design",
"golden set creation",
"offline model evaluation",
"human evaluation protocol",
"error analysis taxonomy"
],
"in_scope": "Eval Design, evaluation criteria, rubric design, test set construction, golden datasets, human review protocols, offline evaluation, online evaluation, benchmark selection, scoring guidelines, error taxonomy, slice-based evaluation",
"name": "Evaluation Design for AI Systems",
"out_of_scope": "Experiment Design and Analysis for causal A/B tests and treatment comparison, test reporting and quality metrics for summarizing results, model serving architecture for deployment/runtime concerns, context management and retrieval for prompt/context assembly",
"overlap_flags": [
{
"reason": "Both involve defining measurement plans, but this dimension focuses on AI/model evaluation rather than causal experimentation.",
"with_dim_id": "experiment-design-and-analysis",
"with_dim_name": null,
"with_role": "Data Scientist"
},
{
"reason": "Evaluation outputs may be reported as metrics, but that dimension owns summarization and dashboards rather than evaluation design itself.",
"with_dim_id": "test-reporting-and-quality-metrics",
"with_dim_name": null,
"with_role": "Automation Tester"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Eval Design",
"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": "eval-design"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"evaluation",
"failure-analysis",
"code-review",
"agile",
"devops",
"scrum",
"context-management",
"agentic-systems",
"prompting"
],
"requires": [],
"skill_id": "eval-design",
"suppress_on_match": []
},
"skill_id": "eval-design",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Eval Design",
"reasoning": "Eval Design is best treated as a methodology because it describes a way of working for creating and running evaluations, not a software artifact or system shape.",
"skill_id": "eval-design",
"subtype": "evaluation_design_methodology",
"type": "Methodology"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Vision-Language Models",
"alias_type": "CANONICAL",
"id": 3619,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Vision-Language Models",
"id": 2662,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "vision-language-models",
"sub_category_id": 2175,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "Vision-Language Models",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Vision-Language Models",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Multimodal Document Understanding",
"alias_type": "CANONICAL",
"id": 3620,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Multimodal Document Understanding",
"id": 2663,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "multimodal-document-understanding",
"sub_category_id": 2176,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "Multimodal Document Understanding",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Multimodal Document Understanding",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Agentic Systems",
"alias_type": "CANONICAL",
"id": 3621,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Agentic Systems",
"id": 2664,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "agentic-systems",
"sub_category_id": 2177,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "Agentic Systems",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Agentic Systems",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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"
},
"input_skill": "Agent Tooling",
"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": "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": "Agent Tooling",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"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"
},
"input_skill": "Agent Tooling",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "Agent Tooling",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Tool",
"skill_nature": "TOOL",
"sub_category": "agent_tooling",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "EMERGING"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "The phrase is fairly specific and usually refers to tooling for AI agents. In typical JDs it is unlikely to be mistaken for a different catalog skill."
},
"context_keywords": {
"context_keywords": [
"function calling",
"tool use",
"tool orchestration",
"retrieval-augmented generation",
"RAG",
"memory store",
"vector database",
"prompt chaining",
"planner-executor",
"workflow graph",
"LangChain",
"LlamaIndex",
"OpenAI Assistants",
"MCP",
"semantic search"
]
},
"maturity": {
"confidence": 0.78,
"maturity": "emerging",
"reasoning": "Appears increasingly in JDs for AI/LLM engineer roles and vendor docs, but market usage is still fragmented across frameworks like LangChain, OpenAI tools, and custom orchestration rather than universal hiring demand."
},
"skill_id": "agent-tooling",
"vendor_license": {
"confidence": 0.98,
"license": null,
"vendor": null,
"year_introduced": null
},
"versioning": {
"current_version": null,
"version_aliases": {},
"versioned": false
}
},
"keep_log": [
{
"a_dim_id": "context-management-and-retrieval",
"a_name": "Context Management and Retrieval",
"a_role": "__skill_focal__",
"b_dim_id": "context-management-and-retrieval",
"b_name": "Context Management and Retrieval",
"b_role": "AI Engineer",
"pair_kind": "cross_role",
"reasoning": "Dim A is agent-loop implementation: it covers prompt assembly, conversation memory, retrieval-augmented context selection, tool result formatting, and grounding snippets for LLM agents. Dim B is AI-engineering context prep at call time, describing what information is included/summarized/retrieved for AI features. Same wording, but A is narrower and tooling-heavy; B is broader and feature-level. Cross-role overlap is lexical, not a single shared cluster.",
"similarity": 0.84058036370886
}
],
"locked_dimensions": [
{
"description": "Covers preparing, selecting, and packaging context for model calls so responses stay relevant and grounded. Agent tooling belongs here when it manages prompts, memory, retrieval, and tool outputs as inputs to an LLM agent loop.",
"exemplar_skills": [
"Agent Tooling",
"prompt engineering",
"context window management",
"retrieval-augmented generation",
"conversation memory",
"tool result formatting"
],
"in_scope": "Agent Tooling, prompt assembly, conversation memory, retrieval-augmented context selection, tool result formatting, grounding snippets, token budgeting, context window management, reranking retrieved passages",
"name": "Context Management and Retrieval",
"out_of_scope": "Model training and fine-tuning, serving infrastructure, UI component logic, database indexing for analytics, general automation scripts not used to build agent context",
"overlap_flags": [
{
"reason": "Agent tooling often sits on top of model serving, but this dimension is about orchestrating inputs and outputs rather than hosting inference.",
"with_dim_id": "model-serving-architecture",
"with_dim_name": null,
"with_role": "Machine Learning Engineer"
},
{
"reason": "Some agents use event streams or async jobs, but that dimension owns the transport and decoupling patterns, not agent context assembly.",
"with_dim_id": "messaging-and-event-streaming",
"with_dim_name": null,
"with_role": "Backend Engineer"
}
],
"tentative_id": "context-management-and-retrieval"
},
{
"description": "Covers the software used to build, coordinate, and observe LLM agents that can call tools, plan steps, and execute workflows. This fits Agent Tooling when the emphasis is on agent loops, tool invocation, state tracking, and multi-step execution behavior.",
"exemplar_skills": [
"Agent Tooling",
"tool calling",
"agent orchestration",
"function calling",
"agent memory",
"multi-step task execution",
"tool registry"
],
"in_scope": "Agent Tooling, tool calling, function invocation, agent loops, planning and replanning, stateful task execution, multi-step workflows, tool registries, action schemas, agent memory stores, trace inspection",
"name": "Agent Orchestration Tooling",
"out_of_scope": "Model hosting and scaling, prompt-only chat applications, general API integration without agent behavior, data pipelines for training or scoring, workflow engines used outside LLM agents",
"overlap_flags": [
{
"reason": "Both can coordinate steps, but this dimension is specific to LLM-driven agents and tool invocation rather than business-process workflows.",
"with_dim_id": "workflow-automation-and-approvals",
"with_dim_name": null,
"with_role": "ServiceNOW Developer"
},
{
"reason": "Agents frequently call APIs, but that dimension owns ordinary service integration while this one owns agent-specific control flow and tool use.",
"with_dim_id": "api-integration-and-data-fetching",
"with_dim_name": null,
"with_role": "Frontend Engineer, Full Stack Developer"
}
],
"tentative_id": "d_init_01"
},
{
"description": "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.",
"exemplar_skills": [
"Context Management and Retrieval"
],
"in_scope": "Skills, tools, and practices that belong under Context Management and Retrieval for the target role, including items implied by the dimension rationale.",
"name": "Context Management and Retrieval",
"out_of_scope": "Adjacent clusters explicitly not owned by Context Management and Retrieval, including unrelated platforms, roles, and skill families per library policy.",
"overlap_flags": [],
"tentative_id": "context-management-and-retrieval"
}
],
"merge_log": [],
"placed": {
"name": "Agent Tooling",
"placement_confidence": 0.92,
"primary_dimension": "context-management-and-retrieval",
"reasoning": "Deterministic JD placement: locked_dimensions has 3 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [
"d_init_01"
],
"skill_id": "agent-tooling"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"workflow-automation",
"agentic-systems",
"ai-ml",
"devops",
"mlops",
"ai",
"context-management",
"agentic-workflows",
"ci-cd",
"document-processing"
],
"requires": [],
"skill_id": "agent-tooling",
"suppress_on_match": []
},
"skill_id": "agent-tooling",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.88,
"name": "Agent Tooling",
"reasoning": "By the Tool vs Framework rule, Agent Tooling is software you operate to support agents rather than a structured codebase you build applications inside.",
"skill_id": "agent-tooling",
"subtype": "agent_tooling",
"type": "Tool"
},
"warnings": [
"stage3_post_filter_dropped_catalog_only_locked_dims:42-\u003e3"
]
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Claude Code",
"alias_type": "CANONICAL",
"id": 3622,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Claude Code",
"id": 2665,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "claude-code",
"sub_category_id": 2178,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"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": "Claude Code",
"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"
}
]
}
],
"input_skill": "Claude Code",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Cursor",
"alias_type": "CANONICAL",
"id": 3623,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 11,
"display_name": "Cursor",
"id": 2666,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "cursor",
"sub_category_id": 2179,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Codex",
"alias_type": "CANONICAL",
"id": 3624,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Codex",
"id": 2667,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "codex",
"sub_category_id": 2180,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Eval Design",
"Agent Tooling"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "AI Engineer",
"id": 12,
"rationale": "The primary skills revolve around AI programming and deployment, which aligns with the role of an AI Engineer.",
"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": "LLMs",
"tag": "in_db"
},
{
"skill": "Prompting",
"tag": "in_db"
},
{
"skill": "Context Management",
"tag": "in_db"
},
{
"skill": "Structured Outputs",
"tag": "in_db"
},
{
"skill": "Document Processing",
"tag": "in_db"
},
{
"skill": "VLMs",
"tag": "in_db"
},
{
"skill": "OCR",
"tag": "in_db"
},
{
"skill": "Layout Parsing",
"tag": "in_db"
},
{
"skill": "Containers",
"tag": "in_db"
},
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "Azure",
"tag": "in_db"
},
{
"skill": "GCP",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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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",
"source": "db"
},
"dimension_id": 365,
"input_skill": "OCR",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2653,
"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": "Layout Parsing",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2654,
"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": "Containers",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2655,
"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": "CI/CD",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2579,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platform Operations",
"id": 26,
"rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
"slug": "cloud-platform-operations",
"source": "db"
},
"dimension_id": 26,
"input_skill": "Azure",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 164,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Platforms",
"id": 332,
"rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
"slug": "cloud-security-platforms",
"source": "db"
},
"dimension_id": 332,
"input_skill": "Azure",
"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": 164,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Platforms",
"id": 332,
"rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
"slug": "cloud-security-platforms",
"source": "db"
},
"dimension_id": 332,
"input_skill": "GCP",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2304,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Evaluation and Validation",
"id": 86,
"rationale": "Techniques for assessing model quality, robustness, and uncertainty before recommendations are made. This includes choosing metrics, validating generalization, and understanding error tradeoffs.",
"slug": "model-evaluation-and-validation",
"source": "db"
},
"dimension_id": 86,
"input_skill": "Evaluation",
"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 Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2656,
"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": "Observability",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2657,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "MySQL Operational Monitoring, Logging, and Diagnostics",
"id": 166,
"rationale": "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.",
"slug": "mysql-operational-monitoring-logging-and-diagnostics",
"source": "db"
},
"dimension_id": 166,
"input_skill": "Failure Analysis",
"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": "MySQL DBA",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "mysql-dba",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2658,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Test Evidence, Defect Reporting, and Triage",
"id": 241,
"rationale": "Capturing and organizing clear evidence from manual testing, including what was tested, what was observed, and how to reproduce issues, then communicating defect severity, impact, and triage notes so teams can prioritize fixes and make release decisions. This includes test notes, screenshots, screen recordings, execution logs, reproduction steps, bug reports, severity/priority assessment, and concise status or coverage summaries.",
"slug": "test-evidence-defect-reporting-and-triage",
"source": "db"
},
"dimension_id": 241,
"input_skill": "Failure Analysis",
"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": "Manual Tester",
"id": 17,
"rationale": null,
"role_archetype": null,
"slug": "manual-tester",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2658,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Service Integration Patterns",
"id": 188,
"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": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 11,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2659,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"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": "RAG",
"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": 2659,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"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": "Hybrid Retrieval",
"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": 2660,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"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": "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": 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": "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": "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,
"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": "Agentic Systems",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 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": "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,
"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": "Cursor",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2666,
"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": "Codex",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 2667,
"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": "Eval Design",
"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": 2680,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 12,
"dimension": {
"difficulty_hint": "well_known",
"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": "Agent Tooling",
"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",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 2681,
"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": "Agent Tooling",
"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": 2681,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 2,
"role_dimension_saved": 0,
"skill_dimension_saved": 3,
"skipped": 0
},
"planner_output": null,
"run_id": "3ebebd1c-ed76-46b5-9b1a-4670b8e8d377"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.