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geospatial-ds

Define a standardized geospatial data-science Skill Unit with YAML frontmatter.

1|1|Updated Apr 25, 2026
Search Tags:#python#geospatial#h3#gis#geopandas#crs#contextily
One-click install
npx skills add https://github.com/b0glarka/la-wildfire-vulnerability-index --skill geospatial-ds
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geospatial-ds
Source: https://github.com/b0glarka/la-wildfire-vulnerability-index/tree/main/.claude/skills/geospatial-ds
Command: npx skills add https://github.com/b0glarka/la-wildfire-vulnerability-index --skill geospatial-ds

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Geospatial data science projects often suffer from inconsistent workflows, fragmented tooling, and unclear best practices. This Skill provides a standardized, frontmatter-driven template and guidance to unify projects around Python geospatial libraries and proven patterns.

Core Features & Use Cases

  • CRS and projections discipline: ensure correct coordinate reference systems and consistent reprojecting.
  • Data formats and tooling guidance: recommend modern formats like GeoParquet, GeoPackage, GeoJSON, and basemap tools with GeoPandas, rasterio, rioxarray, OSMnx, h3, contextily, and PySAL.
  • End-to-end workflow guidance: templates and patterns for vector + raster workflows, OSM data sourcing, geocoding, and spatial statistics.

Quick Start

Adopting these guidelines requires creating a SKILL.md with proper YAML frontmatter and applying CRS-first geospatial workflows to your Python projects.

Frequently Asked Questions about geospatial-ds

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage CRS and projections correctly in Python geospatial workflows?▼

To manage CRS and projections correctly, you must establish CRS-first geospatial workflows that ensure consistent coordinate reference systems and apply proper reprojecting before performing spatial operations.

What is the best way to source and process OpenStreetMap data using GeoPandas?▼

The best way to process OpenStreetMap data is using OSMnx for sourcing combined with GeoPandas for vector processing, following standardized patterns for geocoding and spatial data formatting.

Can I use H3 indexing and contextily basemaps in a standardized geospatial data science project?▼

Yes, you can use H3 for spatial indexing and contextily for adding basemaps, integrating them into vector and raster workflows alongside rasterio and rioxarray.

Which modern geospatial data formats should I use for Python GIS projects?▼

You should use modern formats like GeoParquet, GeoPackage, and GeoJSON for storing geospatial data, ensuring safe, self-contained, and standardized project structures.

How do I perform spatial statistics and raster processing in Python?▼

To perform spatial statistics and raster processing, apply end-to-end workflow guidance utilizing PySAL for spatial statistics and rioxarray or rasterio for raster data manipulation.

Do I need to write executable code or scripts to adopt these geospatial best practices?▼

No, you do not need executable code in the body; adoption requires creating a SKILL.md with YAML frontmatter that provides safe, self-contained guidance without executable scripts.

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