Raster at scale

Skill buildmoonshot/skillpacks/skills/gis/expert/raster-at-scale

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Install

npx -y skills add buildmoonshot/skillpacks --skill raster-at-scale

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Use when processing raster or imagery data that is large — multi-gigabyte GeoTIFFs, mosaics, satellite scenes, DEMs — where loading the whole array into memory would fail or thrash. Makes the agent work in windows/tiles, use overviews and cloud-optimized formats, and avoid out-of-memory full-array reads.

SKILL.md

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Raster at Scale

A large raster will not fit in memory, and the naive read everything into a NumPy array is how raster jobs crash. Process big rasters the way they're built to be processed.

Don't load the whole thing

Read in windows/blocks, not all at once (rasterio windowed reads, GDAL block reads). Process tile by tile and write tile by tile.
Read in the raster's native block size where you can — aligned reads are far faster than arbitrary windows.

Use the right format and pyramids

Prefer Cloud-Optimized GeoTIFF (COG) for efficient partial/range reads.
Build overviews (gdaladdo) so display and downsampled analysis don't touch full resolution.
For chunked array work, reach for xarray/dask or rioxarray rather than a monolithic array.

Reproject and warp without loading

Resample/reproject with gdalwarp (use -multi -wo NUM_THREADS=ALL_CPUS), not by reading arrays into Python.
Pick the resampling method by data type: nearest for categorical/classified rasters (preserves class values), bilinear/cubic for continuous (elevation, imagery). Using bilinear on a land-cover raster invents classes that don't exist.

Get the details right

Honor the NoData value so it doesn't poison statistics or show up as real zeros.
Confirm the CRS before warping or aligning to other layers (see crs-discipline).

Why this matters

The failure modes at scale are distinct: out-of-memory crashes, hour-long full-resolution passes that should have used overviews, and silently wrong resampling that corrupts categorical data. Windowed reads, COGs/overviews, and method-appropriate resampling turn an unworkable raster job into a fast, correct one.