Atlas · GenAI 2026
Software Engineering for AI
32 skills · ontology graph below shows relations within this section.
What this domain covers
This edition groups 32 capabilities in Software Engineering for AI across 11 named categories. The inventory contains 6 concepts and 26 tools; 15 skills appeared in at least two of the three original research runs. The remaining entries stay visible with their lower coverage so a reader can distinguish taxonomy scope from research-system agreement.
Current category labels: AI-Assisted Development · APIs & Services · App Prototyping · DataFrame & In-Process Analytics · Dev Tooling · Feature Engineering · Programming Languages · Python Data Libraries · and 3 more
Frequent learning foundations
- Python supports 4 mapped skills
- API Development supports 1 mapped skill
- CI/CD supports 1 mapped skill
- ETL Pipeline Design supports 1 mapped skill
- SQL supports 1 mapped skill
Skills in this section
AI Code Generation
AI-Assisted Development
API Development
APIs & Services
FastAPI
APIs & Services
Streamlit
App Prototyping
DuckDB / Polars
DataFrame & In-Process Analytics
AI-Assisted Development
Dev Tooling
Feature Engineering
Feature Engineering
Python
Programming Languages
R
Programming Languages
Rust
Programming Languages
SQL
Programming Languages
Shell Scripting
Programming Languages
NumPy
Python Data Libraries
Pandas
Python Data Libraries
Scikit-learn
Python Data Libraries
Software Testing
Testing & Quality
Git
Version Control
GitHub
Version Control
Claude Code
AI-Assisted Development
GitHub Copilot
AI-Assisted Development
Flask
APIs & Services
Dash
App Prototyping
Feast
Feature Engineering
Computational Notebooks
Notebooks & Interactive Compute
Jupyter
Notebooks & Interactive Compute
GeoPandas
Python Data Libraries
Matplotlib
Python Data Libraries
Plotly
Python Data Libraries
SciPy
Python Data Libraries
Seaborn
Python Data Libraries
Statsmodels
Python Data Libraries
Hypothesis
Testing & Quality