Pipeline run
6a632e4a-e40e-4495-8433-0802372c6faf
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionNature of work
—
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
✓ Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
—
Frameworks (×2):
—
Models / concepts (×3):
LLMs, AI agent, AI
Evidence — skills matched in JD (0)
Skill cluster (0 dimension groups, role-scoped)
Status:
completed
Created: 2026-05-08T12:12:50.268701Z
Updated: 2026-05-08T12:14:33.627876Z
API 3 duration: 1000 ms
Flow
Current 3-step pipeline
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Role
Chosen role & resolution
Software Engineer
slug: software-engineer · id: — · source: llm
The skills split across frontend, backend, cloud, data, and AI dimensions, making the JD broadest fit a software engineering umbrella.
0
New skills
0
Skill↔dim saved
0
Role↔dim saved
16
Skipped
Job description
About the job Position: Software Engineer Type: Contractor Compensation: $50 - $120/hour Location: Remote Role Responsibilities Design and develop scalable backend and frontend features using Python, Node.js, React, and GoLang. Architect, deploy, and maintain cloud infrastructure on AWS to support high-performance AI agent applications. Collaborate closely with cross-functional stakeholders to deliver robust, production-ready software solutions. Integrate and optimize workflows for large language models (LLMs) as part of advanced AI agent training initiatives. Conduct comprehensive code reviews and provide mentorship to team members for continuous improvement. Troubleshoot and resolve complex technical challenges efficiently in a distributed environment. Requirements Have strong relevant experience in Python, Node.js, React, GoLang, and working with large language models (LLMs). Have a proven track record deploying and managing applications on AWS cloud infrastructure. Demonstrate exceptional written and verbal communication skills, showcasing clarity and attention to detail. Have a demonstrated success in delivering scalable, maintainable software in collaborative, remote teams. Possess a strong grasp of software design principles, version control (e.g., Git), and modern development methodologies. Application Process Easy Apply on LinkedIn Check email for next steps Participate in resume evaluation & interview stage
history_view bundle (older API). Showing raw API payloads below.
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 |
|---|---|---|---|---|---|---|
| Argo Workflows | in_db |
Workflow Orchestration Systems
workflow-orchestration-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| React | in_db |
Component Frameworks and Rendering
component-frameworks-and-rendering
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Analytical Programming Languages
analytical-programming-languages
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Automation Scripting and CLI
automation-scripting-and-cli
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Network Automation and Scripting
network-automation-and-scripting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Backend Systems
programming-languages-for-backend-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Programming Languages for Test Automation
programming-languages-for-test-automation
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| Python | in_db |
Security Automation and Scripting
security-automation-and-scripting
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| AWS | in_db |
Cloud Platform Operations
cloud-platform-operations
|
— | — | — | TODO: REMOVE AFTER TESTING — api3_writes_enabled=False (writes disabled) |
| GoLang | new |
Go Programming Language
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| LLMs | new |
Large Language Model Applications
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
| LLMs | new |
LLM Safety and Guardrails
d_init_02
|
— | — | — | skill_not_in_db_v3_proposed |
| Node.js | new |
Node.js Runtime Development
d_init_01
|
— | — | — | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | GoLang | type=Language subtype=programming_language nature=LANGUAGE lifespan=EVERGREEN | |
| canonical_skill_proposed | LLMs | type=Concept subtype=large_language_models nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Node.js | type=Runtime subtype=javascript_runtime nature=RUNTIME lifespan=EVERGREEN | |
| dimension_proposed | Go Programming Language | |
| dimension_skill_link_proposed | GoLang ↔ Go Programming Language | |
| dimension_proposed | Large Language Model Applications | |
| dimension_skill_link_proposed | LLMs ↔ Large Language Model Applications | |
| dimension_proposed | LLM Safety and Guardrails | |
| dimension_skill_link_proposed | LLMs ↔ LLM Safety and Guardrails | |
| dimension_proposed | Node.js Runtime Development | |
| dimension_skill_link_proposed | Node.js ↔ Node.js Runtime Development |
API 1 — extract-from-jd click to toggle
{
"filtered_unknown_words": [
"AI",
"Application",
"Architect",
"Compensation",
"Conduct",
"Contractor",
"Design",
"Easy",
"Engineer",
"GoLang",
"LLMs",
"LinkedIn",
"Location",
"Node.js",
"Position",
"Process",
"Remote",
"Requirements",
"Responsibilities",
"Role",
"Software",
"Type",
"agent",
"applications",
"attention",
"challenges",
"clarity",
"cloud",
"code",
"communication",
"control",
"design",
"detail",
"development",
"email",
"environment",
"evaluation",
"experience",
"features",
"frontend",
"grasp",
"hour",
"improvement",
"infrastructure",
"initiatives",
"interview",
"job",
"language",
"managing",
"members",
"methodologies",
"models",
"performance",
"principles",
"production",
"record",
"resume",
"reviews",
"skills",
"software",
"solutions",
"stage",
"stakeholders",
"steps",
"success",
"teams",
"training",
"version"
],
"final_non_skills": [
"AI",
"Application",
"Architect",
"Compensation",
"Conduct",
"Contractor",
"Design",
"Easy",
"Engineer",
"LinkedIn",
"Location",
"Position",
"Process",
"Remote",
"Requirements",
"Responsibilities",
"Role",
"Software",
"Type",
"agent",
"applications",
"attention",
"challenges",
"clarity",
"cloud",
"code",
"communication",
"control",
"detail",
"development",
"email",
"environment",
"evaluation",
"experience",
"features",
"frontend",
"grasp",
"hour",
"improvement",
"infrastructure",
"initiatives",
"interview",
"job",
"language",
"managing",
"members",
"methodologies",
"models",
"performance",
"principles",
"production",
"record",
"resume",
"reviews",
"skills",
"solutions",
"stage",
"stakeholders",
"steps",
"success",
"teams",
"training",
"version"
],
"final_skills": [
"Argo Workflows",
"React",
"Python",
"AWS",
"GoLang",
"LLMs",
"Node.js"
],
"initial_skills": [
"Argo Workflows",
"React",
"Python",
"AWS"
],
"jd_role_hint": {
"display_name": "Software Engineer",
"rationale": "The excerpt centers on Python/Node.js/React/GoLang development, AWS infrastructure, and production software delivery.",
"role_archetype": "Builds and maintains application software, cloud infrastructure, and AI-enabled features in a collaborative product team.",
"slug": "software-engineer"
},
"llm_non_skills": [
"AI",
"Application",
"Architect",
"Compensation",
"Conduct",
"Contractor",
"Design",
"Easy",
"Engineer",
"LinkedIn",
"Location",
"Position",
"Process",
"Remote",
"Requirements",
"Responsibilities",
"Role",
"Software",
"Type",
"agent",
"applications",
"attention",
"challenges",
"clarity",
"cloud",
"code",
"communication",
"control",
"detail",
"development",
"email",
"environment",
"evaluation",
"experience",
"features",
"frontend",
"grasp",
"hour",
"improvement",
"infrastructure",
"initiatives",
"interview",
"job",
"language",
"managing",
"members",
"methodologies",
"models",
"performance",
"principles",
"production",
"record",
"resume",
"reviews",
"skills",
"solutions",
"stage",
"stakeholders",
"steps",
"success",
"teams",
"training",
"version"
],
"llm_skills": [
"GoLang",
"LLMs",
"Node.js"
],
"run_id": null,
"unknown_words": [
"AI",
"Application",
"Architect",
"Compensation",
"Conduct",
"Contractor",
"Design",
"Easy",
"Engineer",
"GoLang",
"LLMs",
"LinkedIn",
"Location",
"Node.js",
"Position",
"Process",
"Remote",
"Requirements",
"Responsibilities",
"Role",
"Software",
"Type",
"agent",
"applications",
"attention",
"challenges",
"clarity",
"cloud",
"code",
"communication",
"control",
"design",
"detail",
"development",
"email",
"environment",
"evaluation",
"experience",
"features",
"frontend",
"grasp",
"hour",
"improvement",
"infrastructure",
"initiatives",
"interview",
"job",
"language",
"managing",
"members",
"methodologies",
"models",
"performance",
"principles",
"production",
"record",
"resume",
"reviews",
"skills",
"software",
"solutions",
"stage",
"stakeholders",
"steps",
"success",
"teams",
"training",
"version"
]
}
API 2 — extract-details
{
"alias_matches": [],
"candidate_roles": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
},
{
"display_name": "Frontend Engineer",
"id": 3,
"rationale": null,
"role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
"slug": "frontend-engineer",
"source": "db"
},
{
"display_name": "Full Stack Developer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-developer",
"source": "db"
},
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
},
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
},
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
},
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 1,
"rationale": null,
"role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
"slug": "devops-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "Software Engineer",
"id": null,
"rationale": "The skills split across frontend, backend, cloud, data, and AI dimensions, making the JD broadest fit a software engineering umbrella.",
"role_archetype": "Builds application and backend software with some cloud and AI-adjacent tooling. Often works across product, infrastructure, and integration layers rather than in a narrowly specialized role.",
"slug": "software-engineer",
"source": "llm"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration Systems",
"id": 64,
"rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
"slug": "workflow-orchestration-systems",
"source": "db"
},
"input_skill": "Argo Workflows",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Component Frameworks and Rendering",
"id": 2,
"rationale": "Frameworks and rendering models used to build reusable UI components and page composition. This covers how frontend applications structure views, manage rendering, and organize feature code.",
"slug": "component-frameworks-and-rendering",
"source": "db"
},
"input_skill": "React",
"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": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Azure Cloud Engineer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "azure-cloud-engineer",
"source": "db"
},
{
"display_name": "Cloud Engineer",
"id": 18,
"rationale": null,
"role_archetype": null,
"slug": "cloud-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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 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": "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": "AWS",
"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": null,
"display_name": "Go Programming Language",
"id": null,
"rationale": "Core language knowledge for writing software in Go, including syntax, types, concurrency primitives, packages, and idiomatic code structure. This skill belongs here because it is the language itself rather than a framework or deployment practice.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "GoLang",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
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"name": "Go Programming Language",
"out_of_scope": "Framework-specific backend architecture, container orchestration, API design patterns, database administration, frontend development, these belong to other dimensions",
"overlap_flags": [
{
"reason": "Go is often used to build services, but service decomposition and integration patterns are a separate dimension from the language itself.",
"with_dim_id": "service-architecture-and-integration",
"with_dim_name": null,
"with_role": "Backend Engineer"
},
{
"reason": "If Go is used inside AI product code, that dimension covers the workflow context, while this one covers the language fundamentals.",
"with_dim_id": "programming-languages-for-ai-workflows",
"with_dim_name": null,
"with_role": "AI Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "GoLang",
"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": "golang"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"java",
"kotlin",
"javascript",
"typescript",
"c",
"r"
],
"requires": [],
"skill_id": "golang",
"suppress_on_match": []
},
"skill_id": "golang",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.99,
"name": "GoLang",
"reasoning": "GoLang is fundamentally a programming language, not a library or framework, because it is the language developers write code in.",
"skill_id": "golang",
"subtype": "programming_language",
"type": "Language"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Large Language Model Applications",
"id": null,
"rationale": "Covers building product features and workflows that use large language models for generation, extraction, reasoning, and conversational interactions. This skill belongs here because LLMs are the core model capability being integrated into software systems.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "LLMs",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": null,
"display_name": "LLM Safety and Guardrails",
"id": null,
"rationale": "Covers controls that constrain, validate, and recover from unsafe or low-confidence LLM behavior. This skill can also fit here when the emphasis is on preventing harmful outputs, handling policy violations, and adding fallback logic around LLM responses.",
"slug": "d_init_02",
"source": "llm"
},
"input_skill": "LLMs",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "LLMs",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "large_language_models",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "\u201cLLMs\u201d is a standard, specific abbreviation for large language models in JDs; it\u2019s unlikely to be confused with a different catalog skill in typical hiring context."
},
"context_keywords": {
"context_keywords": [
"prompt engineering",
"RAG",
"fine-tuning",
"inference",
"tokenization",
"embeddings",
"vector database",
"transformers",
"context window",
"few-shot learning",
"chain-of-thought",
"function calling",
"guardrails",
"alignment",
"LLMOps"
]
},
"maturity": {
"confidence": 0.93,
"maturity": "well_known",
"reasoning": "LLMs now appear in a large share of AI/ML and product-engineering job postings, and major vendors (OpenAI, Anthropic, Google, Microsoft) have made them a standard platform capability."
},
"skill_id": "llms",
"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": "Covers building product features and workflows that use large language models for generation, extraction, reasoning, and conversational interactions. This skill belongs here because LLMs are the core model capability being integrated into software systems.",
"exemplar_skills": [
"LLMs",
"prompt engineering",
"retrieval-augmented generation",
"function calling",
"structured output generation",
"few-shot prompting"
],
"in_scope": "LLMs, prompt design, prompt chaining, few-shot prompting, function calling, structured outputs, retrieval-augmented generation, chatbots, text generation, summarization, classification with LLMs, extraction with LLMs",
"name": "Large Language Model Applications",
"out_of_scope": "Model training from scratch, low-level GPU optimization, generic backend API design, model deployment packaging, safety policy enforcement at runtime",
"overlap_flags": [
{
"reason": "LLM features are often embedded into broader AI service topologies and may be split across handlers, workers, or gateways.",
"with_dim_id": "ai-service-architecture-patterns",
"with_dim_name": null,
"with_role": "AI Engineer"
},
{
"reason": "Production LLM apps often need refusal handling, confidence thresholds, and safe fallback behavior.",
"with_dim_id": "fallback-and-guardrail-handling",
"with_dim_name": null,
"with_role": "Machine Learning Engineer"
}
],
"tentative_id": "d_init_01"
},
{
"description": "Covers controls that constrain, validate, and recover from unsafe or low-confidence LLM behavior. This skill can also fit here when the emphasis is on preventing harmful outputs, handling policy violations, and adding fallback logic around LLM responses.",
"exemplar_skills": [
"LLMs",
"prompt injection defense",
"hallucination mitigation",
"output validation",
"refusal handling",
"content filtering"
],
"in_scope": "LLMs, content filtering, prompt injection defenses, refusal handling, output validation, policy enforcement, hallucination mitigation, safe fallback routing, confidence thresholds",
"name": "LLM Safety and Guardrails",
"out_of_scope": "General prompt crafting for feature behavior, model serving infrastructure, data labeling, training or fine-tuning workflows",
"overlap_flags": [
{
"reason": "Both dimensions address degraded or unsafe model responses, but this one is specific to LLM safety controls.",
"with_dim_id": "fallback-and-guardrail-handling",
"with_dim_name": null,
"with_role": "Machine Learning Engineer"
},
{
"reason": "Some LLM guardrails overlap with broader security policy enforcement, though the focus here is model behavior rather than cloud posture.",
"with_dim_id": "cloud-security-guardrails",
"with_dim_name": null,
"with_role": "Cloud Architect, Cloud Engineer, Cybersecurity Engineer"
}
],
"tentative_id": "d_init_02"
}
],
"merge_log": [],
"placed": {
"name": "LLMs",
"placement_confidence": 0.92,
"primary_dimension": "d_init_01",
"reasoning": "Deterministic JD placement: locked_dimensions has 2 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
"secondary_dimensions": [
"d_init_02"
],
"skill_id": "llms"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [],
"requires": [],
"skill_id": "llms",
"suppress_on_match": []
},
"skill_id": "llms",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.97,
"name": "LLMs",
"reasoning": "LLMs are a named knowledge unit about a class of models, so by the Concept vs Methodology rule they are a Concept rather than a tool or framework.",
"skill_id": "llms",
"subtype": "large_language_models",
"type": "Concept"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [
{
"dimension": {
"difficulty_hint": null,
"display_name": "Node.js Runtime Development",
"id": null,
"rationale": "Covers building server-side applications and services using Node.js, including runtime behavior, module patterns, async programming, and ecosystem tooling. Node.js belongs here because it is the primary JavaScript runtime used to implement backend application logic.",
"slug": "d_init_01",
"source": "llm"
},
"input_skill": "Node.js",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Node.js",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Runtime",
"skill_nature": "RUNTIME",
"sub_category": "javascript_runtime",
"typical_lifespan": "EVERGREEN",
"version_strategy": "SEPARATE_ENTITY",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "Node.js is a specific JavaScript runtime with a distinctive name; in typical JDs it is unlikely to be confused with another catalog skill."
},
"context_keywords": {
"context_keywords": [
"Express",
"NestJS",
"npm",
"yarn",
"TypeScript",
"REST API",
"GraphQL",
"microservices",
"event loop",
"async/await",
"CommonJS",
"ES modules",
"Socket.io",
"JWT",
"MongoDB"
]
},
"maturity": {
"confidence": 0.98,
"maturity": "well_known",
"reasoning": "Node.js appears in high-volume job postings across backend, full-stack, and cloud roles, and remains a standard runtime in major vendor docs and ecosystem tooling."
},
"skill_id": "node-js",
"vendor_license": {
"confidence": 0.98,
"license": "mit",
"vendor": "OpenJS Foundation",
"year_introduced": 2009
},
"versioning": {
"current_version": "22",
"version_aliases": {
"Node 12": "12",
"Node 14": "14",
"Node 16": "16",
"Node 18": "18",
"Node 20": "20",
"Node 22": "22",
"Node.js 12": "12",
"Node.js 12.x": "12",
"Node.js 14": "14",
"Node.js 14.x": "14",
"Node.js 16": "16",
"Node.js 16.x": "16",
"Node.js 18": "18",
"Node.js 18.x": "18",
"Node.js 20": "20",
"Node.js 20.x": "20",
"Node.js 22": "22",
"Node.js 22.x": "22"
},
"versioned": true
}
},
"keep_log": [],
"locked_dimensions": [
{
"description": "Covers building server-side applications and services using Node.js, including runtime behavior, module patterns, async programming, and ecosystem tooling. Node.js belongs here because it is the primary JavaScript runtime used to implement backend application logic.",
"exemplar_skills": [
"Node.js",
"Express.js",
"NestJS",
"Fastify",
"async/await",
"event loop",
"npm"
],
"in_scope": "Node.js, npm, yarn, pnpm, CommonJS, ES modules, async/await, event loop, streams, Express.js, NestJS, Fastify, Koa, backend JavaScript services",
"name": "Node.js Runtime Development",
"out_of_scope": "Browser-only JavaScript and UI frameworks, container orchestration and deployment platforms, database administration, mobile app development, which belong to other dimensions",
"overlap_flags": [
{
"reason": "Node.js is often used to implement services, but this dimension focuses on the runtime and language ecosystem rather than service design patterns.",
"with_dim_id": "service-architecture-and-integration",
"with_dim_name": null,
"with_role": "Backend Engineer"
},
{
"reason": "Node.js can be used in AI product code, but that dimension is about AI feature implementation rather than general Node.js development.",
"with_dim_id": "programming-languages-for-ai-workflows",
"with_dim_name": null,
"with_role": "AI Engineer"
}
],
"tentative_id": "d_init_01"
}
],
"merge_log": [],
"placed": {
"name": "Node.js",
"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": "node-js"
},
"relationships": {
"child_skills": [],
"parent_skills": [],
"related_to": [
"npm",
"typescript",
"python",
"java",
"bash",
"http",
"rest-apis"
],
"requires": [
"javascript"
],
"skill_id": "node-js",
"suppress_on_match": []
},
"skill_id": "node-js",
"split_log": [],
"typed": {
"alternatives_considered": [],
"confidence": 0.99,
"name": "Node.js",
"reasoning": "By the Runtime rule, Node.js is an execution environment for code rather than a library, framework, or tool.",
"skill_id": "node-js",
"subtype": "javascript_runtime",
"type": "Runtime"
},
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"GoLang",
"LLMs",
"Node.js"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Software Engineer",
"id": null,
"rationale": "The skills split across frontend, backend, cloud, data, and AI dimensions, making the JD broadest fit a software engineering umbrella.",
"role_archetype": "Builds application and backend software with some cloud and AI-adjacent tooling. Often works across product, infrastructure, and integration layers rather than in a narrowly specialized role.",
"slug": "software-engineer",
"source": "llm"
},
"final_input_skills": [
{
"skill": "Argo Workflows",
"tag": "in_db"
},
{
"skill": "React",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "GoLang",
"tag": "new"
},
{
"skill": "LLMs",
"tag": "new"
},
{
"skill": "Node.js",
"tag": "new"
}
],
"persistence": {
"items": [
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Workflow Orchestration Systems",
"id": 64,
"rationale": "Operational orchestration of ML jobs, dependencies, and handoffs across training, validation, deployment, and retraining. This is a useful split from training pipelines because it emphasizes the scheduler and control plane.",
"slug": "workflow-orchestration-systems",
"source": "db"
},
"dimension_id": 64,
"input_skill": "Argo Workflows",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "mlops-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 380,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Component Frameworks and Rendering",
"id": 2,
"rationale": "Frameworks and rendering models used to build reusable UI components and page composition. This covers how frontend applications structure views, manage rendering, and organize feature code.",
"slug": "component-frameworks-and-rendering",
"source": "db"
},
"dimension_id": 2,
"input_skill": "React",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"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"
}
],
"skill_dimension_saved": false,
"skill_id": 7,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Analytical Programming Languages",
"id": 82,
"rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
"slug": "analytical-programming-languages",
"source": "db"
},
"dimension_id": 82,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Analyst",
"id": 20,
"rationale": null,
"role_archetype": null,
"slug": "data-analyst",
"source": "db"
},
{
"display_name": "Data Scientist",
"id": 7,
"rationale": null,
"role_archetype": null,
"slug": "data-scientist",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Automation Scripting and CLI",
"id": 48,
"rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
"slug": "automation-scripting-and-cli",
"source": "db"
},
"dimension_id": 48,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Network Automation and Scripting",
"id": 285,
"rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
"slug": "network-automation-and-scripting",
"source": "db"
},
"dimension_id": 285,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Network Engineer",
"id": 21,
"rationale": null,
"role_archetype": null,
"slug": "network-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for AI Workflows",
"id": 261,
"rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
"slug": "programming-languages-for-ai-workflows",
"source": "db"
},
"dimension_id": 261,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 12,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Backend Systems",
"id": 140,
"rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
"slug": "programming-languages-for-backend-systems",
"source": "db"
},
"dimension_id": 140,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Engineer",
"id": 14,
"rationale": null,
"role_archetype": null,
"slug": "backend-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 67,
"rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 67,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 6,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 113,
"rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 113,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Machine Learning Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "machine-learning-engineer",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Test Automation",
"id": 193,
"rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
"slug": "programming-languages-for-test-automation",
"source": "db"
},
"dimension_id": 193,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Automation Tester",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "automation-tester",
"source": "db"
}
],
"skill_dimension_saved": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Security Automation and Scripting",
"id": 258,
"rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
"slug": "security-automation-and-scripting",
"source": "db"
},
"dimension_id": 258,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 393,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"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": "AWS",
"llm_role": null,
"matched_chosen_role": false,
"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": false,
"skill_id": 163,
"skill_tag": "in_db",
"skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 api3_writes_enabled=False (writes disabled)"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": null,
"display_name": "Go Programming Language",
"id": null,
"rationale": "Core language knowledge for writing software in Go, including syntax, types, concurrency primitives, packages, and idiomatic code structure. This skill belongs here because it is the language itself rather than a framework or deployment practice.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "GoLang",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": null,
"display_name": "Large Language Model Applications",
"id": null,
"rationale": "Covers building product features and workflows that use large language models for generation, extraction, reasoning, and conversational interactions. This skill belongs here because LLMs are the core model capability being integrated into software systems.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "LLMs",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": null,
"display_name": "LLM Safety and Guardrails",
"id": null,
"rationale": "Covers controls that constrain, validate, and recover from unsafe or low-confidence LLM behavior. This skill can also fit here when the emphasis is on preventing harmful outputs, handling policy violations, and adding fallback logic around LLM responses.",
"slug": "d_init_02",
"source": "llm"
},
"dimension_id": null,
"input_skill": "LLMs",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
},
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": null,
"display_name": "Node.js Runtime Development",
"id": null,
"rationale": "Covers building server-side applications and services using Node.js, including runtime behavior, module patterns, async programming, and ecosystem tooling. Node.js belongs here because it is the primary JavaScript runtime used to implement backend application logic.",
"slug": "d_init_01",
"source": "llm"
},
"dimension_id": null,
"input_skill": "Node.js",
"llm_role": null,
"matched_chosen_role": false,
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": false,
"skill_id": null,
"skill_tag": "new",
"skipped_reason": "skill_not_in_db_v3_proposed"
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 16
},
"planner_output": null,
"run_id": "6a632e4a-e40e-4495-8433-0802372c6faf"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.
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