From geoai-skills
Verifies geoai Python package installation and functionality, checks optional geospatial and deep learning dependencies, provides pip instructions if missing. Useful before geo AI tasks.
How this skill is triggered — by the user, by Claude, or both
Slash command
/geoai-skills:install-geoaiThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Arguments: `$@`
Arguments: $@
python3 -c "import geoai; print(f'geoai v{geoai.__version__}')"
--check or --extras was passed, otherwise stop.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-pyFor GPU-accelerated AI models (object detection, segmentation), also install PyTorch:
pip install torch torchvisionFor 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.
npx claudepluginhub opengeos/geoai-skills --plugin geoai-skillsGuides installation of Earth2Studio via uv or pip, selecting model extras and configuring the environment.
Detects buildings, cars, ships, solar panels, parking lots, agriculture fields in geospatial imagery using GeoAI models or GroundedSAM text-prompted segmentation. GPU recommended.
Provides geospatial analysis: remote sensing, GIS, spatial ML, satellite imagery processing (Sentinel, Landsat, etc.), vector/raster ops, point clouds, network analysis, cloud-native workflows with 500+ code examples in 8 languages.