Pipeline run
f22d2691-45b6-4261-93ec-b55b19b6008e
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Computer Vision Engineer
domain · AI / ML CASE DOMAINslug: computer-vision-engineer · id: 152 · source: db
Domain=AI / ML; The JD centers on Python-based deep learning work with OpenCV and computer vision concepts to develop and fine-tune traffic-engineering algorithms, which best matches Computer Vision Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Requirements • Strong background in Computer Science with 0-3 years of experience. • Should have at least an undergraduate degree in CS from a reputed college. • Should be well versed in Python (C/C++ will be a bonus) and machine learning including deep learning (PyTorch or TensorFlow) and should be familiar with OpenCV and computer vision-related concepts. • Should have an analytical mind and strong problem-solving skills. • Expected to work on developing or fine-tuning algorithms related to traffic engineering mainly related to computer vision and producing them. • Good communication skills in English are a must.
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
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
C# and .NET Languages Catalog dimension db id 362
Library dimension (catalog)
Roles linked in library: .NET Backend Developer
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Sitecore Development Languages Catalog dimension db id 438
Library dimension (catalog)
Roles linked in library: Sitecore Dev
-
Video Codec Languages and DSLs Catalog dimension db id 225
Library dimension (catalog)
Roles linked in library: Video Codec Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- C# (CANONICAL) primary
- C (CANONICAL)
- C# 1 (VERSION)
- C# 10 (VERSION)
- C# 11 (VERSION)
- C# 12 (VERSION)
- C# 13 (VERSION)
- C# 14 (VERSION)
- C# 2 (VERSION)
- C# 3 (VERSION)
- C# 4 (VERSION)
- C# 5 (VERSION)
- C# 6 (VERSION)
- C# 7 (VERSION)
- C# 8 (VERSION)
- C# 9 (VERSION)
- C# latest (VERSION)
- C#1 (VERSION)
- C#10 (VERSION)
- C#11 (VERSION)
- C#12 (VERSION)
- C#2 (VERSION)
- C#3 (VERSION)
- C#4 (VERSION)
- C#5 (VERSION)
- C#6 (VERSION)
- C#7 (VERSION)
- C#8 (VERSION)
- C#9 (VERSION)
- C++ (CANONICAL)
- C++03 (VERSION)
- C++11 (VERSION)
- C++14 (VERSION)
- C++17 (VERSION)
- C++20 (VERSION)
- C++23 (VERSION)
- C++26 (VERSION)
- C++98 (VERSION)
- c sharp (VERSION)
- c# (VERSION)
- cpp03 (VERSION)
- cpp11 (VERSION)
- cpp14 (VERSION)
- cpp17 (VERSION)
- cpp20 (VERSION)
- cpp23 (VERSION)
- cpp26 (VERSION)
- cpp98 (VERSION)
- csharp (VERSION)
- modern C++ (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2000
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- latest
Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
C# and .NET Languages Catalog dimension db id 362
Library dimension (catalog)
Roles linked in library: .NET Backend Developer
-
Cross-Platform App Languages Catalog dimension db id 167
Library dimension (catalog)
Roles linked in library: Hybrid Mobile Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Sitecore Development Languages Catalog dimension db id 438
Library dimension (catalog)
Roles linked in library: Sitecore Dev
-
Video Codec Languages and DSLs Catalog dimension db id 225
Library dimension (catalog)
Roles linked in library: Video Codec Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Machine Learning
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1024
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
AI Governance and Model Security Catalog dimension db id 50
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- PyTorch (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- Meta
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Model Fine-Tuning & Adaptation Catalog dimension db id 212
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- TensorFlow (CANONICAL) primary
- TF1 (VERSION)
- TF2 (VERSION)
- TensorFlow 1 (VERSION)
- TensorFlow 1.x (VERSION)
- TensorFlow 2 (VERSION)
- TensorFlow 2.x (VERSION)
- tensorflow 1 (VERSION)
- tensorflow 1.x (VERSION)
- tensorflow 2 (VERSION)
- tensorflow 2.x (VERSION)
- tensorflow v1 (VERSION)
- tensorflow v2 (VERSION)
- tf (VERSION)
- tf1 (VERSION)
- tf2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: TensorFlow appears in many ML/AI job descriptions and remains a standard production framework, with strong GitHub activity and broad vendor support from Google and cloud platforms.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C | in_db |
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
C# and .NET Languages
c-and-net-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Cross-Platform App Languages
cross-platform-app-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Sitecore Development Languages
sitecore-development-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| C++ | in_db |
Video Codec Languages and DSLs
video-codec-languages-and-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TensorFlow | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Deep Learning | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | OpenCV | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Computer Vision | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "fail",
"archetype_override_applied": true,
"archetype_override_matched_skills": [
"Python",
"Algorithms",
"Machine Learning",
"TensorFlow",
"PyTorch"
],
"role_archetype": "Engineering"
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "C"
},
{
"is_primary": false,
"skill_name": "C++"
},
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Deep Learning"
},
{
"is_primary": false,
"skill_name": "PyTorch"
},
{
"is_primary": false,
"skill_name": "TensorFlow"
},
{
"is_primary": false,
"skill_name": "OpenCV"
},
{
"is_primary": true,
"skill_name": "Computer Vision"
}
],
"jd_role": null,
"nano_parsed": {
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{
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],
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},
{
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{
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"Python"
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"case": "DOMAIN",
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"Algorithm Development and Fine-tuning",
"Traffic Engineering Applications",
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"producing them",
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"C/C++",
"machine learning",
"deep learning",
"PyTorch",
"TensorFlow",
"OpenCV",
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"queued": false,
"reasoning": "Domain=AI / ML; The JD centers on Python-based deep learning work with OpenCV and computer vision concepts to develop and fine-tune traffic-engineering algorithms, which best matches Computer Vision Engineer.",
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},
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{
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{
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],
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}
}
API 2 — extract-details
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{
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{
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"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Sitecore Development Languages",
"id": 438,
"rationale": "Core implementation languages and markup used to build Sitecore customizations, rendering logic, and site behavior. This is the primary authoring surface for Sitecore-specific code and templates.",
"slug": "sitecore-development-languages",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Sitecore Dev",
"id": 233,
"rationale": null,
"role_archetype": "Engineering",
"slug": "sitecore-dev",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Video Codec Languages and DSLs",
"id": 225,
"rationale": "Implementation languages and codec-specific description languages used to build compression, decoding, and transform logic. This is the primary coding surface for codec behavior and bitstream manipulation.",
"slug": "video-codec-languages-and-dsls",
"source": "db"
},
"input_skill": "C++",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Video Codec Engineer",
"id": 22,
"rationale": null,
"role_archetype": null,
"slug": "video-codec-engineer",
"source": "db"
}
]
}
],
"input_skill": "C++",
"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": "Machine Learning",
"alias_type": "CANONICAL",
"id": 2015,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Machine Learning",
"id": 1356,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "machine-learning",
"sub_category_id": 1024,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "AI Governance and Model Security",
"id": 50,
"rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
"slug": "ai-governance-and-model-security",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Machine Learning",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Deep Learning",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "deep-learning",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "PyTorch",
"alias_type": "CANONICAL",
"id": 441,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 7,
"display_name": "PyTorch",
"id": 195,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "pytorch",
"sub_category_id": 156,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"input_skill": "PyTorch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Fine-Tuning \u0026 Adaptation",
"id": 212,
"rationale": "Techniques and libraries for adapting pre-trained language models to specific tasks or domains.",
"slug": "model-fine-tuning-adaptation",
"source": "db"
},
"input_skill": "PyTorch",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
}
],
"input_skill": "PyTorch",
"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": "TensorFlow",
"alias_type": "CANONICAL",
"id": 442,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TF1",
"alias_type": "VERSION",
"id": 443,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TF2",
"alias_type": "VERSION",
"id": 444,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TensorFlow 1",
"alias_type": "VERSION",
"id": 445,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TensorFlow 1.x",
"alias_type": "VERSION",
"id": 447,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TensorFlow 2",
"alias_type": "VERSION",
"id": 446,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "TensorFlow 2.x",
"alias_type": "VERSION",
"id": 448,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow 1",
"alias_type": "VERSION",
"id": 2490,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow 1.x",
"alias_type": "VERSION",
"id": 2494,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow 2",
"alias_type": "VERSION",
"id": 2491,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow 2.x",
"alias_type": "VERSION",
"id": 2495,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow v1",
"alias_type": "VERSION",
"id": 2492,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tensorflow v2",
"alias_type": "VERSION",
"id": 2493,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tf",
"alias_type": "VERSION",
"id": 2487,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tf1",
"alias_type": "VERSION",
"id": 2488,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "tf2",
"alias_type": "VERSION",
"id": 2489,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 7,
"display_name": "TensorFlow",
"id": 196,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "tensorflow",
"sub_category_id": 156,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "ML Frameworks and Libraries",
"id": 40,
"rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
"slug": "ml-frameworks-and-libraries",
"source": "db"
},
"input_skill": "TensorFlow",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "TensorFlow",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "OpenCV",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "opencv",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Computer Vision",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "computer-vision",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Deep Learning",
"OpenCV",
"Computer Vision"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Computer Vision Engineer",
"id": 152,
"rationale": "Domain=AI / ML; The JD centers on Python-based deep learning work with OpenCV and computer vision concepts to develop and fine-tune traffic-engineering algorithms, which best matches Computer Vision Engineer.",
"role_archetype": null,
"slug": "computer-vision-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "C",
"tag": "in_db"
},
{
"skill": "C++",
"tag": "in_db"
},
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Deep Learning",
"tag": "new"
},
{
"skill": "PyTorch",
"tag": "in_db"
},
{
"skill": "TensorFlow",
"tag": "in_db"
},
{
"skill": "OpenCV",
"tag": "new"
},
{
"skill": "Computer Vision",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 152,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
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LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.