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Gempy

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SteadfastAsArt
gempy

3D structural geological modeling using implicit methods. Create geological models with faults, folds, and unconformities from surface points and orientations. Use when the agent needs to: (1) Build 3D geological models from surface contacts and orientations, (2) Model faults, unconformities, or intrusions, (3) Compute and visualize subsurface geology, (4) Export models to VTK or numpy arrays, (5) Generate gravity forward models, (6) Create cross-sections or 3D visualizations.

Overview

PublisherSteadfastAsArt
Repositorygeoscience-skills
Skill namegempy
Stars
75
Forks
9
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Gempy 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/SteadfastAsArt/geoscience-skills/tree/main/gempy
  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/SteadfastAsArt/geoscience-skills.git /tmp/geoscience-skills
mkdir -p .claude/skills
cp -r /tmp/geoscience-skills/gempy .claude/skills/gempy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gempy 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 Gempy 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 Gempy 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.

GemPy - 3D Geological Modelling

Quick Reference

python
import gempy as gp

# Create model
geo_model = gp.create_geomodel(
    project_name='Model',
    extent=[0, 1000, 0, 1000, 0, 500],  # [xmin, xmax, ymin, ymax, zmin, zmax]
    resolution=[50, 50, 25]
)

# Add surface points and orientations
gp.add_surface_points(geo_model, x=[100, 500, 900], y=[500, 500, 500],
                      z=[400, 350, 400], surface='TopFormation')
gp.add_orientations(geo_model, x=[500], y=[500], z=[375],
                    pole_vector=[0, 0, 1], surface='TopFormation')

# Compute and visualize
gp.set_interpolator(geo_model)
sol = gp.compute_model(geo_model)
gp.plot_2d(geo_model, cell_number=[25], direction='y')

Key Classes

ClassPurpose
GeoModelMain container - holds all model data
StructuralFrameManages geological relationships
GridComputation mesh for interpolation
SolutionsModel results (lithology, scalar fields)

Essential Operations

Define Geological Relationships

python
gp.map_stack_to_surfaces(geo_model, mapping={
    'Strata1': ['TopFormation', 'BaseFormation'],
    'Basement': ['Basement']
})
geo_model.structural_frame.structural_groups[0].structural_relation = \
    gp.data.StackRelationType.ERODE

Add Faults

python
gp.add_surface_points(geo_model, x=[300, 300, 300], y=[200, 500, 800],
                      z=[100, 250, 400], surface='Fault1')
gp.add_orientations(geo_model, x=[300], y=[500], z=[250],
                    pole_vector=[1, 0, 0.5], surface='Fault1')
gp.map_stack_to_surfaces(geo_model, mapping={
    'Fault_Series': ['Fault1'],
    'Strata1': ['Layer1', 'Layer2'],
})
geo_model.structural_frame.structural_groups[0].structural_relation = \
    gp.data.StackRelationType.FAULT

Load Data from Files

python
import pandas as pd

points_df = pd.read_csv('surface_points.csv')  # Columns: X, Y, Z, surface
gp.add_surface_points(geo_model, x=points_df['X'], y=points_df['Y'],
                      z=points_df['Z'], surface=points_df['surface'])

ori_df = pd.read_csv('orientations.csv')  # Columns: X, Y, Z, dip, azimuth, surface
gp.add_orientations(geo_model, x=ori_df['X'], y=ori_df['Y'], z=ori_df['Z'],
                    dip=ori_df['dip'], azimuth=ori_df['azimuth'],
                    surface=ori_df['surface'])

Visualization

python
gp.plot_2d(geo_model, cell_number=[25], direction='y', show_data=True)
gp.plot_3d(geo_model, show_data=True, show_surfaces=True)  # Requires PyVista

Access Results

python
sol = gp.compute_model(geo_model)
lithology = sol.raw_arrays.lith_block
lith_3d = lithology.reshape(geo_model.grid.regular_grid.resolution)

Structural Relation Types

TypeDescription
ERODEYounger surface erodes older (unconformity)
ONLAPYounger onlaps onto older
FAULTSurface is a fault plane
INTRUSIONIntrusive body

When to Use vs Alternatives

ScenarioRecommendation
3D implicit geological modelling from surface dataGemPy - purpose-built, Python-native
Complex fold modelling with structural framesLoopStructural - better fold support
Commercial-grade subsurface modellingSKUA-GOCAD - industry standard, proprietary
Quick 2D cross-sections onlyGemPy works but may be overkill; consider manual interpolation
Need gravity/magnetics forward model from geologyGemPy - has built-in potential field support

Choose GemPy when: You need implicit 3D geological modelling from surface contacts and orientations with fault/unconformity relationships, especially when integrated with Python workflows. It has a gentler learning curve than LoopStructural for standard cases.

Avoid GemPy when: Your geology is dominated by complex folding (use LoopStructural), or you need production-grade reservoir modelling (use commercial tools).

Common Workflows

Build 3D geological model from surface data

  • Load or define surface contact points (min 2 per surface) and orientations (min 1 per surface)
  • Create GeoModel with appropriate extent and resolution
  • Add surface points with gp.add_surface_points()
  • Add orientations with gp.add_orientations()
  • Map geological stack with gp.map_stack_to_surfaces()
  • Set structural relations (ERODE, ONLAP, FAULT)
  • Set interpolator with gp.set_interpolator()
  • Compute model with gp.compute_model()
  • Validate with 2D cross-sections using gp.plot_2d()
  • Visualize 3D result with gp.plot_3d() or export to VTK

Common Issues

IssueSolution
Model not computingCheck min 2 points + 1 orientation per surface
Artifacts at edgesExtend model extent beyond data
Wrong fault offsetCheck pole_vector direction
Memory errorsReduce grid resolution

References

Scripts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gempy AI skill do?

3D structural geological modeling using implicit methods. Create geological models with faults, folds, and unconformities from surface points and orientations. Use when the agent needs to: (1) Build 3D geological models from surface contacts and orientations, (2) Model faults, unconformities, or intrusions, (3) Compute and visualize subsurface geology, (4) Export models to VTK or numpy arrays, (5) Generate gravity forward models, (6) Create cross-sections or 3D visualizations.

Why use Gempy on TypingMind?

Because you install it once and use it with any model. Gempy 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 Gempy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SteadfastAsArt/geoscience-skills/tree/main/gempy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gempy?

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 Gempy?

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

Is the Gempy AI skill free?

Yes. It is published on GitHub by SteadfastAsArt 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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