Geoparquet validation
Skill isaaccorley/geospatial-skills/plugins/geoparquet-validation/skills/geoparquet-validation
This skill should be used when working with gpio for GeoParquet inspection, validation, optimization, and distribution. Covers GeoParquet best practices using gpio CLI and DuckDB.From its SKILL.md
npx -y skills add isaaccorley/geospatial-skills --skill geoparquet-validationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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GeoParquet Validation Skill
Guide users through GeoParquet workflows with a gpio-first approach: inspect, validate, optimize, and distribute GeoParquet files following current best practices.
What this skill is for
Use this skill when the user is working with GeoParquet files and needs:
- metadata inspection
- validation and auto-fix workflows
- conversion and optimization with
gpio - partitioning and publishing
- DuckDB support for heavier SQL transforms
This is not a general "anything about GeoParquet" skill. It is centered on the gpio toolchain.
Tools
gpio (geoparquet-io) - Preferred
Always prefer gpio for GeoParquet operations. It applies important best practices by default.
Installation:
pipx install --pre geoparquet-io
pip install --pre geoparquet-io
uv pip install --pre geoparquet-io
If gpio is missing, guide the user through installation before proceeding.
DuckDB - For Advanced Operations
Use DuckDB for complex SQL, joins, aggregations, or geometry operations.
pip install "duckdb>=1.5"
When using DuckDB, apply GeoParquet best practices manually:
ORDER BY ST_Hilbert(geometry)COMPRESSION ZSTDwithCOMPRESSION_LEVEL 15ROW_GROUP_SIZE 100000- validate output with
gpio check all
Workflow
1. Inspect
gpio inspect <file>
gpio inspect stats <file>
Report row count, geometry type, CRS, columns, and file size.
gpio inspect and gpio check accept only (Geo)Parquet files. For non-Parquet sources (shapefile, GPKG, GeoJSON, FGB), skip pre-inspection — convert first, then inspect the output. Do not spend calls pre-inspecting a non-Parquet source unless the conversion fails; if you truly need source metadata first, use pyogrio.read_info("<src>").
2. Convert or optimize
gpio convert geoparquet <input> <output> # input: any OGR-readable format (shp, gpkg, geojson, fgb, csv)
gpio convert geoparquet <input> <output> --compression-level 15
convert applies best practices by default (Hilbert sort, bbox covering column, ZSTD) and validates its own output — a clean convert rarely needs --fix afterwards.
3. Validate
gpio check all <file>
gpio check all <file> --fix --output <fixed>
check all passes when Spec Validation reports every check with a checkmark. Lines marked as informational (the "GeoParquet 2.0 is available" pointer in particular) are not failures — stay on 1.1.0, the widest-compatibility version, unless the user explicitly asks for 2.0. convert and check all already print file size and row-group stats; one gpio inspect on the final output is enough for reporting.
4. Scale based on size
- Small: single file, Hilbert sorted, bbox column
- Medium: single file, covering metadata, compression level 15
- Large: partition with kdtree/admin/quadkey and generate STAC
5. Publish
gpio publish stac <input> <output.json>
gpio publish upload <file> s3://bucket/path/
Quick Reference
# Inspect (Parquet input only)
gpio inspect <file>
gpio inspect stats <file>
# Convert (input: any OGR-readable format — shp, gpkg, geojson, fgb, csv)
gpio convert geoparquet <input> <output>
gpio convert geoparquet <input> <output> --compression-level 15
# Validate
gpio check all <file>
gpio check all <file> --fix --output <fixed>
# Extract
gpio extract <input> <output> --bbox "minx,miny,maxx,maxy"
gpio extract <input> <output> --where "column > value"
# Partition and publish
gpio partition kdtree <input> <output_dir> --max-rows-per-file 500000
gpio publish stac <input> <output.json>
References
references/gpio-commands.mdreferences/distribution-best-practices.mdreferences/tool-comparison.md