Agent skill · research science · k-dense-ai
geomaster
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Why this skill is useful
Adds extensive geospatial processing capabilities with 500+ code examples and specialized workflows for remote sensing and spatial analysis.
What it needs
Requires conda, uv installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
GeoMaster Comprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages. Installation Quick Start NDVI from Sentinel-2 Spatial Analysis with GeoPandas Google Earth Engine Time Series Core Concepts Data Types Type Examples Libraries ------ ---------- ----------- Vector Shapefile, GeoJSON, GeoPackage GeoPandas, Fiona, GDAL Raster GeoTIFF, NetCDF, COG Rasterio, Xarray, GDAL Point Cloud LAS, LAZ Laspy, PDAL, Open3D Coordinate Systems EPSG:4326 (WGS 84) - Geographic, lat/lon, use for storage EPSG:3857 (Web Mercator) - Web maps only (don't use for area/distance!) EPSG:326xx/327xx (UTM) - Metric calculations, <1% distortion per zone Use gdf.estimateutmcrs() for automatic UTM detection OGC Standards WMS: Web Map Service - raster maps WFS: Web Feature Service - vector data WCS: Web Coverage Service - raster coverage STAC: Spatiotemporal Asset Catalog - modern metadata Common Operations Spectral Indices Vector Operations Terrain Analysis Network Analysis Image Classification Modern Cloud-Native Workflows STAC + Planetary Computer Cloud-Optimized GeoTIFF (COG) Performance Tips Best Practices 1. Always check CRS before spatial operations 2. Use projected CRS for area/distance calculations 3. Validate geometries: gdf = gdf[gdf.isvalid] 4. Handle missing data: gdf['geometry'] = gdf['geometry'].fillna(None) 5. Use efficient formats: GeoPackage > Shapefile, Parquet for large data 6. Apply cloud masking to optical imagery 7. Preserve lineage for reproducible research 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/geomaster