geopandas

geopandas is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 125 tokens per session (1,841 once invoked), scanned A, original, Apache-2.0.

A Python library for working with geographic shapes and location-based data, such as points, boundaries, roads, and regions stored in formats like GeoJSON or shapefiles.

In plain words
What is it for?
It helps with spatial analysis, transforming map coordinates, combining datasets by location, calculating geographic measurements, and creating choropleth maps that shade regions by value.
Why use it?
It adds map-aware operations to table-based data, including coordinate conversion, spatial joins, overlays, area calculations, and plotting.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps with spatial analysis, transforming map coordinates, combining datasets by location, calculating geographic measurements, and creating choropleth maps that shade regions by value.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/synthetic-sciences/openscience/geopandas
About the project

synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.

synthetic-sciences/openscience · 4,233 stars · on GitHub · openscience.sh

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add synthetic-sciences/openscience --skill geopandas
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for geopandas

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/geopandas/github.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/geopandas)
Your own site
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/geopandas"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/geopandas/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for geopandas

Your own site · 80×15
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/geopandas"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/geopandas.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,841 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00125 $0.01841
Opus 5.5 $0.00050 $0.00736
Sonnet 5.5 $0.00025 $0.00368
Haiku 4.5 $0.00013 $0.00184

Measured 1mo ago against content hash 1698b99ab085, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

geopandas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

backend/cli/skills/data-engineering/geopandas/SKILL.md · 257 lines

How it starts

The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GeoPandas

GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.

Installation

uv pip install geopandas

Optional Dependencies

# For interactive maps
uv pip install folium

# For classification schemes in mapping
uv pip install mapclassify

# For faster I/O operations (2-4x speedup)
uv pip install pyarrow

# For PostGIS database support
uv pip install psycopg2
uv pip install geoalchemy2

# For basemaps
uv pip install contextily

# For cartographic projections
uv pip install cartopy

Quick Start

import geopandas as gpd

# Read spatial data
gdf = gpd.read_file("data.geojson")

# Basic exploration
print(gdf.head())
print(gdf.crs)
print(gdf.geometry.geom_type)

# Simple plot
gdf.plot()

# Reproject to different CRS
gdf_projected = gdf.to_crs("EPSG:3857")

# Calculate area (use projected CRS for accuracy)
gdf_projected['area'] = gdf_projected.geometry.area

# Save to file
gdf.to_file("output.gpkg")

Core Concepts

Data Structures

  • GeoSeries: Vector of geometries with spatial operations
  • GeoDataFrame: Tabular data structure with geometry column

See data-structures.md for details.

Reading and Writing Data

GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet.

# Read with filtering
gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))

# Write with Arrow acceleration
gdf.to_file("output.gpkg", use_arrow=True)

See data-io.md for comprehensive I/O operations.

Coordinate Reference Systems

Always check and manage CRS for accurate spatial operations:

# Check CRS
print(gdf.crs)

# Reproject (transforms coordinates)
gdf_projected = gdf.to_crs("EPSG:3857")

# Set CRS (only when metadata missing)
gdf = gdf.set_crs("EPSG:4326")

See crs-management.md for CRS operations.

Read the full file on GitHub · 257 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +6 lines 1698b99ab085
  2. 1mo ago First seen · 251 lines · 125 tokens per session scan A 81ca075473c8

Subscribe to this mod's changes

geopandas is a skill published in the GitHub repository synthetic-sciences/openscience (4,233 stars, last pushed 2d ago), licensed Apache-2.0. It adds 125 tokens to every session and 1,841 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.