| name | geopandas |
| description | Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with. |
| source_skill_id | k-dense-ai-scientific-agent-skills-scientific-skills-geopandas-skill-md |
| category | Science, research & data analysis |
| source_mirror | ../../../../../skills/by-category/science-research-data-analysis/latest-release-community/geopandas/SKILL.md |
| benchmark_status | artifact_gated |
geopandas
Use this skill when the task matches the description above or the source path clearly applies. Start with this concise entrypoint; open ../../../../../skills/by-category/science-research-data-analysis/latest-release-community/geopandas/SKILL.md only when implementation details, commands, assets, or references are needed.
Workflow
- Confirm the task matches this skill's scope.
- Read the local source mirror if more detail is required.
- Follow repository-level
AGENTS.md; use one AI session only.
- Keep claims tied to files, commands, citations, or benchmark artifacts.
Verification
- Source mirror:
../../../../../skills/by-category/science-research-data-analysis/latest-release-community/geopandas/SKILL.md
- Source commit:
eb20fb0dcb0b1dadaa3db2737188f0755bbc4770
- Static benchmark results: see
docs/benchmark-results.md
- Runtime artifacts recorded by this entrypoint:
0
- Assigned scenarios:
skill-proof-k-dense-ai-scientific-agent-skills-scientific-skills-geopandas-skill-md, science-research-and-data-analysis-cellxgene-census, science-research-and-data-analysis-chembl, science-research-and-data-analysis-ome-ngff-samples
Do not claim this skill passed a runtime benchmark until a validated artifact exists.
