GeoPandas Spatial Join Starter
SkillAI & modelsUse this skill to run a deterministic point-in-polygon join with GeoPandas and summarize the result in compact JSON.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the GeoPandas Spatial Join Starter skill
About this capability
A framework for discovering, compiling, and validating reusable skills for scientific agents.
What this skill tells your AI
The instructions your AI receives, as published by ma-compbio-lab/skillfoundry in skills/earth-climate-and-geospatial-science/geopandas-spatial-join-starter/SKILL.md and read by ahel’s review.
Use this skill to run a deterministic point-in-polygon join with GeoPandas and summarize the result in compact JSON.
What it does
- Builds a toy vector dataset with two regions and four observation points.
- Performs a spatial join to assign points to regions.
- Reprojects the joined data to
EPSG:3857and records projected bounds. - Emits a reusable JSON summary for downstream geospatial workflows.
When to use it
- You need a local, no-auth starter for vector geospatial analysis.
- You want a minimal example of
GeoDataFrame,sjoin, andto_crs. - You want a repo-aware wrapper that fixes the
PROJdata-path issue automatically.
Example
slurm/envs/geospatial/bin/python skills/earth-climate-and-geospatial-science/geopandas-spatial-join-starter/scripts/run_geopandas_spatial_join.py \
--out scratch/geopandas/spatial_join_summary.json
Verification
- Skill-local tests:
python3 -m unittest discover -s skills/earth-climate-and-geospatial-science/geopandas-spatial-join-starter/tests -p 'test_*.py' - Repository smoke:
python3 -m unittest tests.smoke.test_frontier_domain_skills -v
Signals
- GitHub stars
- 39
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
geopandas-spatial-join-starter- Source
- github.com/ma-compbio-lab/skillfoundry