From geoai-skills
Downloads Overture Maps data (buildings, places, roads, land use, water) for a bounding box using geoai library. Saves GeoDataFrame as GeoJSON or GeoPackage.
npx claudepluginhub opengeos/geoai-skills --plugin geoai-skillsThis skill is limited to using the following tools:
You are helping the user download Overture Maps data using geoai.
Downloads NAIP aerial imagery for a WGS84 bounding box via geoai library. Parses coordinates, validates extent, supports year, output dir, and max items.
Analyzes spatial data with DuckDB: distance calculations, spatial joins, containment checks, density analysis, format conversions. Uses Overture Maps for global POIs, buildings, roads.
Performs geospatial vector analysis with GeoPandas: read/write Shapefile/GeoJSON/GeoPackage/PostGIS/Parquet, spatial joins/overlays/dissolve, geometric ops (buffer/centroid), CRS transforms, choropleth/interactive maps.
Share bugs, ideas, or general feedback.
You are helping the user download Overture Maps data using geoai.
Input: $@
Follow these steps in order.
Extract:
$0 or the first positional argument as the Overture data type--bbox minx,miny,maxx,maxy as the bounding box (required)--output FILE as the output file path (optional, default: ./<data_type>_overture.gpkg)Valid Overture data types:
address, building, building_part, division, division_area,
division_boundary, place, segment, connector, infrastructure,
land, land_cover, land_use, water
If the data type is not recognized, print the list of valid types and ask the user to pick one.
If the user provided natural language (e.g. "get buildings in downtown Nashville"), extract the data type and either infer or ask for the bounding box.
Confirm the bounding box has 4 numeric values:
minx < maxx and miny < maxyIf validation fails, report the issue and ask for corrected coordinates.
python3 -c "
import geoai
gdf = geoai.download_overture_buildings(
bbox=(MINX, MINY, MAXX, MAXY),
output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"
python3 -c "
import geoai
gdf = geoai.get_overture_data(
overture_type='DATA_TYPE',
bbox=(MINX, MINY, MAXX, MAXY),
output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"
Replace DATA_TYPE, MINX, MINY, MAXX, MAXY, and OUTPUT_PATH with actual values.
If a state directory exists, update it:
STATE_DIR=""
test -f .geoai-skills/state.json && STATE_DIR=".geoai-skills"
PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
PROJECT_ID="$(echo "$PROJECT_ROOT" | tr '/' '-')"
test -f "$HOME/.geoai-skills/$PROJECT_ID/state.json" && STATE_DIR="$HOME/.geoai-skills/$PROJECT_ID"
If STATE_DIR is set:
python3 -c "
import json, os
state_file = 'STATE_DIR/state.json'
state = {}
if os.path.exists(state_file):
with open(state_file) as f:
state = json.load(f)
state.setdefault('downloaded_files', [])
state['downloaded_files'].append('OUTPUT_PATH')
with open(state_file, 'w') as f:
json.dump(state, f, indent=2)
"
Summarize:
Then suggest: "Use /geoai-skills:inspect-geo to examine the downloaded data in detail."
import geoai fails -> delegate to /geoai-skills:install-geoai.overturemaps not installed -> suggest pip install "geoai-py[extra]" which includes the overturemaps dependency.