detect-objects
Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with…
Verify that the geoai Python package is installed and functional. If not, provide installation instructions. Optionally check extra dependencies for deep learning models.
$ npx -y skills add opengeos/geoai-skills --skill install-geoai --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/install-geoaiContext preview
The summary Claude sees to decide when to auto-load this skill.
Verify that the geoai Python package is installed and functional. If not, provide installation instructions. Optionally check extra dependencies for deep learning models.
name: install-geoai description: > Verify that the geoai Python package is installed and functional. If not, provide installation instructions. Optionally check extra dependencies for deep learning models. argument-hint: "[--check] [--extras]" allowed-tools: Bash
Arguments: `$@`
python3 -c "import geoai; print(f'geoai v{geoai.__version__}')"If `--check` is present in `$@`, run a comprehensive dependency check:
python3 -c "
deps = [
'geoai', 'geopandas', 'rasterio', 'rioxarray', 'shapely',
'leafmap', 'numpy', 'pandas', 'matplotlib',
]
for dep in deps:
try:
mod = __import__(dep)
ver = getattr(mod, '__version__', 'unknown')
print(f'{dep}: {ver}')
except ImportError:
print(f'{dep}: NOT INSTALLED')
"If `--extras` is present in `$@`, also check deep learning dependencies:
python3 -c "
import sys
dl_deps = ['torch', 'torchvision', 'transformers', 'timm', 'segmentation_models_pytorch']
for dep in dl_deps:
try:
mod = __import__(dep)
ver = getattr(mod, '__version__', 'unknown')
print(f'{dep}: {ver}')
except ImportError:
print(f'{dep}: NOT INSTALLED')
try:
import torch
if torch.cuda.is_available():
print(f'CUDA: {torch.version.cuda} (device: {torch.cuda.get_device_name(0)})')
else:
print('CUDA: not available (CPU only)')
except ImportError:
print('CUDA: torch not installed')
"Report the results and note any missing packages.
If geoai is not installed, tell the user:
> **geoai is not installed.** Install it with: > > ``` > pip install geoai-py > ``` > > For GPU-accelerated AI models (object detection, segmentation), also install PyTorch: > > ``` > pip install torch torchvision > ``` > > For the full set of optional dependencies: > > ``` > pip install "geoai-py[extra]" > ```
Stop after showing the instructions. Do not attempt to install automatically unless the user explicitly asks.
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.
Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with…
Download NAIP aerial imagery for a bounding box. Specify coordinates as minx,miny,maxx,maxy in WGS84 and optionally a year.
Inspect any raster or vector geospatial file. Returns CRS, bounds, bands, resolution, dtype, attribute summaries, and band statistics. Supports GeoTIFF,…
Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box. Returns a GeoDataFrame saved as GeoJSON or GeoPackage.
Process raster data: clip by bounding box, stack multiple bands, mosaic GeoTIFFs, or convert between raster and vector formats.
Search past Claude Code session logs to recover context from previous conversations. Finds past decisions, data paths, CRS info, model configurations, and…