geo-infer-space
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Best use case
geo-infer-space is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Teams using geo-infer-space 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-SPACE/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-space Compares
| Feature / Agent | geo-infer-space | 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?
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
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-SPACE
## Instructions
### Core Capabilities
- **H3 v4 indexing**: Cell operations, hierarchical resolution, k-ring neighbors
- **Backend dispatch**: Interface pattern for H3, SRAI, PostGIS backends
- **Coordinate systems**: CRS transformations, EPSG management
- **Spatial operations**: Buffers, intersections, unions, containment
- **Visualization**: Choropleth maps, heatmaps, spatial dashboards
### Key Imports
```python
from geo_infer_space.backends.h3 import H3Backend
from geo_infer_space.core.spatial_operations import SpatialEngine
from geo_infer_space.core.coordinate_systems import CRSManager
```
### H3 v4 API (Critical)
```python
import h3
# CORRECT (v4):
cell = h3.latlng_to_cell(lat, lng, resolution)
lat, lng = h3.cell_to_latlng(cell)
neighbors = h3.grid_disk(cell, k)
# WRONG (legacy v3 — never use):
# h3.geo_to_h3(), h3.h3_to_geo(), h3.k_ring()
```
## Examples
```python
from geo_infer_space.backends.h3 import H3Backend
backend = H3Backend()
# Index a point to an H3 cell at resolution 7
cell = backend.latlng_to_cell(45.5231, -122.6765, resolution=7)
print(f"H3 cell: {cell}")
# Get neighbors
neighbors = backend.grid_disk(cell, k=2)
print(f"Neighbors (k=2): {len(neighbors)} cells")
# Tessellate a region
from shapely.geometry import box
region = box(-122.8, 45.4, -122.5, 45.6)
cells = backend.tessellate(region, resolution=8)
print(f"Tessellation: {len(cells)} cells")
```
```python
from geo_infer_space.core.coordinate_systems import CRSManager
crs = CRSManager()
x, y = crs.transform(45.5, -122.6, from_epsg=4326, to_epsg=32610)
print(f"UTM Zone 10N: ({x:.0f}, {y:.0f})")
```
## Guidelines
- Always use H3 v4 API — zero legacy calls allowed
- Backend-agnostic: use the dispatcher pattern, not direct H3 calls
- EPSG:4326 (WGS84) is the default CRS
- Test: `uv run python -m pytest GEO-INFER-SPACE/tests/ -v`
### Integrations
- **MATH** → Spatial weights for statistics
- **TIME** → Spatio-temporal analysis
- **DATA** → Spatial indexing of datasets
- **PLACE** → Boundary tessellation with H3
- Nearly every module depends on SPACE for geographic indexing