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.
npx skills add cxcscmu/SkillLearnBench --skill geopandas-projectionsgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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.
[](https://agentmods.dev/skills/cxcscmu/skilllearnbench/geopandas-projections)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/geopandas-projections"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/geopandas-projections/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.
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/geopandas-projections"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/geopandas-projections.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.00736 |
| Opus 5 | $0.00010 | $0.00368 |
| Sonnet 5 | $0.00004 | $0.00147 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
geopandas-projections 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeoPandas Spatial Projections
Overview
GeoPandas is built on top of Shapely and Fiona, enabling geographic data manipulation with proper coordinate reference systems (CRS). Using correct projections is critical for accurate distance calculations and spatial operations.
Installation
pip install geopandas shapely fiona pyproj
Key Concepts
Coordinate Reference Systems (CRS)
- EPSG:4326: WGS84 (lat/lon), commonly used for geographic data but NOT suitable for distance calculations
- EPSG:3857: Web Mercator, used for web mapping
- Regional Projected CRS: For accurate local distance calculations (e.g., UTM zones)
Distance Calculations
Always project to a projected CRS before calculating distances. Geographic CRS (like EPSG:4326) measure in degrees, not kilometers.
Code Examples
Creating GeoDataFrames from Points
import geopandas as gpd
from shapely.geometry import Point
import pandas as pd
# From earthquake data
earthquakes_df = pd.read_json('/root/earthquakes_2024.json')
geometry = [Point(xy) for xy in zip(earthquakes_df['longitude'], earthquakes_df['latitude'])]
gdf = gpd.GeoDataFrame(earthquakes_df, geometry=geometry, crs='EPSG:4326')
Loading GeoJSON with Boundaries
import json
# Load GeoJSON and convert to GeoDataFrame
with open('/root/PB2002_boundaries.json', 'r') as f:
geojson_data = json.load(f)
boundaries_gdf = gpd.GeoDataFrame.from_features(geojson_data['features'], crs='EPSG:4326')
Projecting to Projected CRS
# Project to a suitable CRS for distance calculations
# Example: project to Mercator for global analysis
gdf_projected = gdf.to_crs('EPSG:3857')
boundaries_projected = boundaries_gdf.to_crs('EPSG:3857')
# Or use a specific UTM zone for a region
# EPSG:32633 is UTM zone 33N
Spatial Filtering (Point in Polygon)
# Check if points fall within polygons
earthquakes_in_plate = gpd.sjoin(gdf, plate_polygons, how='inner', predicate='within')
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.
- 9d ago First seen · 87 lines · 21 tokens per session scan A e72274e474e1
geopandas-projections is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 736 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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