Generates seeded GeoJSON point, line, and polygon datasets within bounding boxes or masks for GIS testing and demos.
npx skills add https://github.com/jumboly/gis-skills --skill gis-data-gen
Please help me install this Agent Skill. Skill: gis-data-gen Source: https://github.com/jumboly/gis-skills/tree/main/gis-data-gen Command: npx skills add https://github.com/jumboly/gis-skills --skill gis-data-gen
This skill provides quick generation of synthetic GIS data (points, lines, and polygons) within a geographic area, enabling reliable testing, benchmarking, and demos without real data.
Run the gis-data-gen tool with a bbox or mask to generate a GeoJSON FeatureCollection of random GIS data suitable for testing.
High-intent search queries and answers about installing and using this skill.
Generate random GeoJSON data by creating synthetic points, lines, and polygons within a specified bounding box or GeoJSON mask. Output a GeoJSON FeatureCollection with optional metadata and deterministic seeding to ensure reliable, reproducible test fixtures for geospatial pipelines.
Yes, you can generate reproducible random GIS data using deterministic seeding. Specifying a fixed seed ensures the synthetic points, lines, and polygons remain identical across runs, providing consistent benchmarking and validation for geospatial workflows.
Yes, geospatial data generation supports polygon clipping with administrative boundaries. It fetches boundaries via Overpass integration and applies clipping to create sample administrative boundaries or test geometries constrained to specific geographic areas for demos.
Yes, you need SciPy and Shapely installed to generate synthetic GIS data using this method. The generation processes rely on these dependencies for geometric calculations and spatial operations, with optional reproducible dependency installation available.
The best way to create random points within a GeoJSON mask for benchmarks is using a generator that applies spatial constraints directly to the mask. This ensures all synthetic geometries stay within the specified area for accurate performance testing.
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