geo-infer-ai
Machine learning pipelines and model selection for geospatial AI. Use when training spatial ML models, building prediction pipelines, performing feature engineering on geographic data, or selecting between ML approaches for spatial problems.
Best use case
geo-infer-ai is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Machine learning pipelines and model selection for geospatial AI. Use when training spatial ML models, building prediction pipelines, performing feature engineering on geographic data, or selecting between ML approaches for spatial problems.
Teams using geo-infer-ai should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-AI/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-ai Compares
| Feature / Agent | geo-infer-ai | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Machine learning pipelines and model selection for geospatial AI. Use when training spatial ML models, building prediction pipelines, performing feature engineering on geographic data, or selecting between ML approaches for spatial problems.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-AI
## Instructions
### Core Capabilities
- **ML pipelines**: End-to-end spatial feature engineering → training → evaluation
- **Model selection**: Cross-validation, hyperparameter optimization
- **Spatial features**: H3-based feature extraction, proximity features
- **Deep learning**: Spatial neural networks, graph neural networks
- **Transfer learning**: Pre-trained geospatial embeddings
### Key Imports
```python
from geo_infer_ai.core.pipeline import SpatialPipeline
from geo_infer_ai.core.model_selection import ModelSelector
from geo_infer_ai.core.feature_engineering import SpatialFeatureExtractor
```
## Examples
```python
from geo_infer_ai.core.pipeline import SpatialPipeline
pipeline = SpatialPipeline(
features=["h3_resolution", "proximity", "density"],
model="random_forest"
)
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
```
## Guidelines
### Integrations
- Integrates with SPACE module for H3-based feature extraction
- Depends on MATH for spatial statistics features
- Optional deps: scikit-learn, torch, tensorflow — graceful degradation
- Test: `uv run python -m pytest GEO-INFER-AI/tests/ -v`