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isaaccorleyisaaccorley/geospatial-skills/skills/overture-data/SKILL.md

overture-data

Skill78 starsChanged 8 months agoApache-2.0View on GitHub

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

  • Installs packages

1 other project uses a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Follow these steps in order.

    line 14
  2. Distinctive:

    If the data type is not recognized, print the list of valid types and ask the user to pick one.

    line 28
  3. Distinctive:

    If the user provided natural language (e.g.

    line 30
  4. Distinctive:

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

    line 38
  5. Distinctive:

    If a state directory exists, update it:

    line 87
  6. Very specific:

    If STATE_DIR is set:

    line 97

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 the gdal skill to examine the downloaded data, or the geospatial-viewers skill to view it."

Error handling

  • import geoai fails -> suggest pip install "geoai-py[extra]" (the Overture helpers need the extra).
  • 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.

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