geospatial-analysis

geospatial-analysis is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 37 tokens per session (1,936 once invoked), scanned A, original, Apache-2.0.

A guide to analyzing geographic data with GeoPandas, including earthquake locations and plate boundaries. It explains how coordinate systems affect distance calculations and spatial filtering.

In plain words
What is it for?
Use it to calculate geographic distances, filter spatial features, and work with earthquake or tectonic datasets using suitable map projections.
Why use it?
It prevents inaccurate distance measurements caused by treating latitude and longitude degrees as equal distances everywhere.

Skill for Claude CodeCodex

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

Good fit Use it to calculate geographic distances, filter spatial features, and work with earthquake or tectonic datasets using suitable map projections.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/geospatial-analysis
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,754 stars · on GitHub · skillsbench.ai

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 benchflow-ai/skillsbench --skill geospatial-analysis
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

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 geospatial-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/geospatial-analysis.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/geospatial-analysis)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/geospatial-analysis"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/geospatial-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,936 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 $0.00037 $0.01936
Opus 5 $0.00018 $0.00968
Sonnet 5 $0.00007 $0.00387
Haiku 4.5 $0.00004 $0.00194

Measured 5d ago against content hash 9ec15d10424e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

geospatial-analysis 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 5d 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

2 near-identical copies found in the catalogue:

tasks/earthquake-plate-calculation/environment/skills/geospatial-analysis/SKILL.md · 226 lines

How it starts

The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Geospatial Analysis with GeoPandas

Overview

When working with geographic data (earthquakes, plate boundaries, etc.), using geopandas with proper coordinate projections provides accurate distance calculations and efficient spatial operations. This guide covers best practices for geospatial analysis.

Key Concepts

Geographic vs Projected Coordinate Systems

Coordinate System Type Units Use Case
EPSG:4326 (WGS84) Geographic Degrees (lat/lon) Data storage, display
EPSG:4087 (World Equidistant Cylindrical) Projected Meters Distance calculations

Critical Rule: Never calculate distances directly in geographic coordinates (EPSG:4326). Always project to a metric coordinate system first.

Why Projection Matters

# ❌ INCORRECT: Calculating distance in EPSG:4326
# This treats degrees as if they were equal distances everywhere on Earth
gdf = gpd.GeoDataFrame(..., crs="EPSG:4326")
distance = point1.distance(point2)  # Wrong! Returns degrees, not meters

# ✅ CORRECT: Project to metric CRS first
gdf_projected = gdf.to_crs("EPSG:4087")
distance_meters = point1_proj.distance(point2_proj)  # Correct! Returns meters
distance_km = distance_meters / 1000.0

Loading Geospatial Data

From GeoJSON Files

import geopandas as gpd

# Load GeoJSON files directly
gdf_plates = gpd.read_file("plates.json")
gdf_boundaries = gpd.read_file("boundaries.json")

From Regular Data with Coordinates

from shapely.geometry import Point
import geopandas as gpd

# Convert coordinate data to GeoDataFrame
data = [
    {"id": 1, "lat": 35.0, "lon": 140.0, "value": 5.5},
    {"id": 2, "lat": 36.0, "lon": 141.0, "value": 6.0},
]

geometry = [Point(row["lon"], row["lat"]) for row in data]
gdf = gpd.GeoDataFrame(data, geometry=geometry, crs="EPSG:4326")

Spatial Filtering

Finding Points Within a Polygon

# Get the polygon of interest
target_poly = gdf_plates[gdf_plates["Name"] == "Pacific"].geometry.unary_union

# Filter points that fall within the polygon
points_inside = gdf_points[gdf_points.within(target_poly)]

print(f"Found {len(points_inside)} points inside the polygon")

Read the full file on GitHub · 226 lines

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. 5d ago First seen · 226 lines · 37 tokens per session scan A 9ec15d10424e

Subscribe to this mod's changes

geospatial-analysis is a skill published in the GitHub repository benchflow-ai/skillsbench (1,754 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,936 once invoked, about $0.0002 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 2026-09-03.