Overture data

Skill opengeos/geoai-skills/skills/overture-data

A Claude Code plugin that adds GeoAI-powered skills for data exploration and session memory.

Install

npx -y skills add opengeos/geoai-skills --skill overture-data

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

Copied from the file, not written here

Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box. Returns a GeoDataFrame saved as GeoJSON or GeoPackage.

SKILL.md

3.6 KB, as published. Nobody here has run it

You are helping the user download Overture Maps data using geoai.

Input: $@

Follow these steps in order.

Step 1 -- Parse arguments

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.

Step 2 -- Validate the bounding box

Confirm the bounding box has 4 numeric values:

minx < maxx and miny < maxy
Values within WGS84 range

If validation fails, report the issue and ask for corrected coordinates.

Step 3 -- Download the data

For building data specifically

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())
"

For all other data types

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.

Step 4 -- Update state

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)
"

Step 5 -- Report results

Summarize:

Data type downloaded
Number of features
Output file path and size
Column summary
CRS and spatial extent

Then suggest: "Use /geoai-skills:inspect-geo to examine the downloaded data in detail."

Error handling

import geoai fails -> delegate to /geoai-skills:install-geoai.
overturemaps not installed -> suggest pip install "geoai-py[extra]" which includes the overturemaps dependency.
No features found -> suggest expanding the bounding box or trying a different data type.
Network error -> report and suggest retrying.