About geoparquet-validation
A skill for users working with GeoParquet files, guiding inspection, validation, conversion, optimization, and publishing through gpio. It centers on the gpio toolchain and uses DuckDB for more complex SQL operations.
Key features
- 01Inspect GeoParquet metadata and statistics
- 02Validate files and apply automatic fixes
- 03Convert and optimize GeoParquet files
- 04Choose partitioning and publishing by dataset size
- 05Use DuckDB for complex SQL operations
Use cases
- 01Check row counts, CRS, and columns in a GeoParquet file
- 02Convert and validate formats such as GeoJSON
- 03Partition large datasets and generate STAC
- 04Publish datasets to S3
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ isaaccorley/ geospatial-skills --skill geoparquet-validationAfter installing, type/geoparquet-validationin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/distribution-best-practices.mdgpio-commands.mdtool-comparison.mdDetails
From the project READMEGeoParquet 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