Plate Tectonics Geospatial logo

Plate Tectonics Geospatial

Organization
cxcscmu
plate-tectonics-geospatial

Analyze plate tectonics data using GeoPandas, identify points within plates, and calculate distances to boundaries.

Overview

Publishercxcscmu
RepositorySkillLearnBench
Skill nameplate-tectonics-geospatial
Stars
91
Forks
7
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cxcscmu on GitHub. Read the source before you install it.

Installation

Install the Plate Tectonics Geospatial AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-haiku-4-5/earthquake-plate-calculation/plate-tectonics-geospatial
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench.git /tmp/SkillLearnBench
mkdir -p .claude/skills
cp -r /tmp/SkillLearnBench/skills/b1-one-shot-claude-haiku-4-5/earthquake-plate-calculation/plate-tectonics-geospatial .claude/skills/plate-tectonics-geospatial
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plate Tectonics Geospatial in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Plate Tectonics Geospatial on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Plate Tectonics Geospatial is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Plate Tectonics Geospatial Analysis

Overview

The PB2002 (Plate Boundaries 2002) dataset provides comprehensive data on tectonic plate boundaries and plate definitions. This skill covers loading, processing, and spatial analysis of plate data.

PB2002 Dataset Structure

Boundary Data (PB2002_boundaries.json)

json
{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "LineString",
        "coordinates": [[lon1, lat1], [lon2, lat2], ...]
      },
      "properties": {
        "PLATE1": "Pacific",
        "PLATE2": "North American",
        "TYPE": "subduction zone",  // or "transform", "spreading", etc.
        "STEPOVER": ""
      }
    }
  ]
}

Plate Data (PB2002_plates.json)

json
{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "MultiPolygon",  // or Polygon
        "coordinates": [...]
      },
      "properties": {
        "PlateName": "Pacific",
        "PlateID": 101
      }
    }
  ]
}

Loading and Processing

Load Plate Boundaries

python
import geopandas as gpd
import json

with open('/root/PB2002_boundaries.json', 'r') as f:
    boundaries_geojson = json.load(f)

boundaries_gdf = gpd.GeoDataFrame.from_features(
    boundaries_geojson['features'],
    crs='EPSG:4326'
)

# Filter for specific plate boundary
pacific_boundaries = boundaries_gdf[
    (boundaries_gdf['PLATE1'] == 'Pacific') |
    (boundaries_gdf['PLATE2'] == 'Pacific')
]

Load Plate Polygons

python
with open('/root/PB2002_plates.json', 'r') as f:
    plates_geojson = json.load(f)

plates_gdf = gpd.GeoDataFrame.from_features(
    plates_geojson['features'],
    crs='EPSG:4326'
)

# Get Pacific plate polygon
pacific_plate = plates_gdf[plates_gdf['PlateName'] == 'Pacific']

Spatial Operations

Identify Points Within Plate

python
# Use spatial join to find earthquakes within Pacific plate
earthquakes_in_pacific = gpd.sjoin(
    earthquakes_gdf,
    pacific_plate,
    how='inner',
    predicate='within'
)

Calculate Distance to Boundary

python
# For each earthquake, calculate minimum distance to boundary
def min_distance_to_boundary(point, boundary_lines_gdf):
    """Calculate minimum distance from point to any boundary line"""
    min_dist = float('inf')
    for idx, boundary in boundary_lines_gdf.iterrows():
        dist = point.distance(boundary['geometry'])
        if dist < min_dist:
            min_dist = dist
    return min_dist

# Apply to all earthquakes in the plate
# First, project to projected CRS for accurate distance in km
earthquakes_projected = earthquakes_in_pacific.to_crs('EPSG:3857')
boundaries_projected = pacific_boundaries.to_crs('EPSG:3857')

earthquakes_projected['distance_to_boundary'] = earthquakes_projected.geometry.apply(
    lambda point: min_distance_to_boundary(point, boundaries_projected)
)

# Convert from meters to kilometers
earthquakes_projected['distance_km'] = earthquakes_projected['distance_to_boundary'] / 1000

Find Maximum Distance

python
# Find earthquake furthest from boundary
furthest_idx = earthquakes_projected['distance_km'].idxmax()
furthest_earthquake = earthquakes_projected.loc[furthest_idx]

Important Considerations

Polygon Validation

python
# Ensure polygons are valid before spatial operations
if not plates_gdf.geometry.is_valid.all():
    plates_gdf['geometry'] = plates_gdf.geometry.buffer(0)

if not boundaries_gdf.geometry.is_valid.all():
    boundaries_gdf['geometry'] = boundaries_gdf.geometry.buffer(0)

Handling MultiPolygons

python
# If a plate is represented as MultiPolygon, create a union
pacific_plate_union = pacific_plate.unary_union

Projection for Accurate Distances

python
# Use Web Mercator (EPSG:3857) for global distances in meters
# Then convert to kilometers

# For regional analysis, consider UTM zones
# EPSG:32633 = UTM Zone 33N
# EPSG:32733 = UTM Zone 33S

Common Pitfalls

  1. Not projecting before distance calculations - Distances in geographic CRS are meaningless
  2. Invalid geometries - Use .buffer(0) to fix self-intersecting polygons
  3. Antimeridian issues - Points near ±180° longitude may have wrapping issues
  4. MultiPolygon confusion - Remember that a plate may consist of multiple disconnected polygons

Frequently asked questions

What does the Plate Tectonics Geospatial AI skill do?

Analyze plate tectonics data using GeoPandas, identify points within plates, and calculate distances to boundaries.

Why use Plate Tectonics Geospatial on TypingMind?

Because you install it once and use it with any model. Plate Tectonics Geospatial is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Plate Tectonics Geospatial in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cxcscmu/SkillLearnBench/tree/main/skills/b1-one-shot-claude-haiku-4-5/earthquake-plate-calculation/plate-tectonics-geospatial. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plate Tectonics Geospatial?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Plate Tectonics Geospatial?

As many as you like. As long as a model supports skills, you can use Plate Tectonics Geospatial with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Plate Tectonics Geospatial AI skill free?

Yes. It is published on GitHub by cxcscmu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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