geoai-skills

geoai-skills

A Claude Code plugin that adds GeoAI-powered skills for geospatial data exploration, satellite imagery download, AI-based object detection, and session memory.

Built on the GeoAI Python library.

Installation

From GitHub

Add the repository as a plugin source and install:

/plugin marketplace add opengeos/geoai-skills
/plugin install geoai-skills@geoai-skills

This registers the GitHub repo as a marketplace and installs the plugin. Skills will be available as /geoai-skills:<skill-name> in all future sessions.

Updating

To pull the latest version, update the marketplace first and then the plugin:

/plugin marketplace update geoai-skills
/plugin update geoai-skills@geoai-skills

Prerequisites

• Python 3.10+ with the geoai-py package installed (pip install geoai-py)
• For AI models (object detection, segmentation): PyTorch with CUDA (optional, CPU works but is slower)
• For Overture Maps data: pip install "geoai-py[extra]"

Skills

inspect-geo

Inspect any raster or vector geospatial file -- CRS, bounds, bands, resolution, dtype, attribute summaries, and band statistics. Supports GeoTIFF, Shapefile, GeoJSON, GeoPackage, GeoParquet, and more.

/geoai-skills:inspect-geo dem.tif
/geoai-skills:inspect-geo buildings.gpkg what CRS is this in?
/geoai-skills:inspect-geo landcover.tif how many bands does it have?

download-data

Download NAIP aerial imagery for a bounding box. Specify coordinates as minx,miny,maxx,maxy in WGS84 and optionally a year.

/geoai-skills:download-data -83.5,35.5,-83.4,35.6 --year 2022
/geoai-skills:download-data -122.5,37.7,-122.3,37.8 --output ./naip/

search-stac

Search and download satellite imagery from Microsoft Planetary Computer. Browse collections, search by bbox and time range, and download items.

/geoai-skills:search-stac list
/geoai-skills:search-stac sentinel-2-l2a --bbox -83.5,35.5,-83.4,35.6 --datetime 2023-01-01/2023-06-30
/geoai-skills:search-stac naip --bbox -83.5,35.5,-83.4,35.6 --download

overture-data

Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box.

/geoai-skills:overture-data building --bbox -83.5,35.5,-83.4,35.6
/geoai-skills:overture-data land_use --bbox -122.5,37.7,-122.3,37.8 --output land_use.gpkg

process-raster

Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.

/geoai-skills:process-raster clip input.tif --bbox -83.5,35.5,-83.4,35.6
/geoai-skills:process-raster mosaic ./tiles/ --output mosaic.tif
/geoai-skills:process-raster stack band1.tif band2.tif band3.tif --output stacked.tif
/geoai-skills:process-raster raster-to-vector classification.tif --output polygons.gpkg

detect-objects

Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, or use text-prompted segmentation with GroundedSAM.

/geoai-skills:detect-objects buildings naip_image.tif
/geoai-skills:detect-objects cars parking_lot.tif --output car_detections.gpkg
/geoai-skills:detect-objects grounded-sam satellite.tif --text "swimming pools"

read-memories

Search past Claude Code session logs to recover context from previous conversations -- past decisions, data paths, CRS info, model configurations, and unresolved work.

/geoai-skills:read-memories naip --here
/geoai-skills:read-memories building detection --geo

install-geoai

Verify that the geoai Python package is installed and functional. Optionally check extra dependencies for deep learning models.

/geoai-skills:install-geoai
/geoai-skills:install-geoai --check
/geoai-skills:install-geoai --extras

Session state

Skills can share state through a JSON file per project (state.json), containing information about recently inspected files, downloaded data paths, and working directories. When state is first needed, you will be asked where to store it:

1. In the project directory (.geoai-skills/state.json) -- colocated with the project, optionally gitignored
2. In your home directory (~/.geoai-skills/<project-id>/state.json) -- keeps the project directory clean

State is used to auto-fill file paths across skills. For example, after downloading NAIP imagery, inspect-geo and detect-objects can reference the downloaded files without re-specifying paths.

How the skills work together

Skills reference each other where it makes sense: