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 SteadfastAsArt/geoscience-skills --skill geological-modellinggit clone --depth 1 https://github.com/SteadfastAsArt/geoscience-skillsWrote 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/steadfastasart/geoscience-skills/geological-modelling)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/geological-modelling"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/geological-modelling/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/steadfastasart/geoscience-skills/geological-modelling"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/geological-modelling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.02716 |
| Opus 5.5 | $0.00017 | $0.01086 |
| Sonnet 5.5 | $0.00009 | $0.00543 |
| Haiku 4.5 | $0.00004 | $0.00272 |
Grade A, and why
geological-modelling 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.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geological Modelling Workflow
End-to-end pipeline for building 3D geological models from spatial data, covering GIS data preparation, implicit surface modelling, and 3D visualization.
Skill Chain
gemgis gempy / loopstructural pyvista
[GIS Preprocessing] --> [Implicit Modelling] --> [3D Visualization]
| | |
Shapefile parsing Surface interpolation Volume render
Raster extraction Fault modelling Cross-sections
Borehole to points Unconformities Mesh export
CRS transforms Scalar field solving Interactive pick
Decision Points: GemPy vs LoopStructural
| Criterion | GemPy | LoopStructural |
|---|---|---|
| Standard layer-cake geology | Preferred | Works |
| Complex folding (refolded folds) | Limited | Preferred (structural frames) |
| Fault networks | Good | Good |
| Built-in gravity forward model | Yes | No |
| Learning curve | Gentler | Steeper |
| Data input | Points + orientations | Points + orientations + fold constraints |
| Unconformities | ERODE / ONLAP types | Supported |
| API style | Functional (gp.compute_model) |
Object-oriented (model.update()) |
Rule of thumb: Use GemPy for standard structural geology with faults and unconformities. Use LoopStructural when fold geometry is the primary control on model architecture.
Step-by-Step Orchestration
Stage 1: Spatial Data Preparation (gemgis)
import gemgis as gg
import geopandas as gpd
import rasterio
# Load geological map (shapefile)
contacts = gpd.read_file('geological_contacts.shp')
orientations = gpd.read_file('orientations.shp')
# Extract surface points from GIS contacts with DEM
with rasterio.open('dem.tif') as dem:
# All inputs must share the DEM's projected CRS and metre units.
contacts = contacts.to_crs(dem.crs)
orientations = orientations.to_crs(dem.crs)
surface_points = gg.vector.extract_xyz(contacts, dem=dem)
orientation_pts = gg.vector.extract_xyz(orientations, dem=dem)
# Define model extent from data bounds
extent = gg.utils.set_extent(
gdf=surface_points,
z_min=-500, z_max=1000
)
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.
- 1mo ago Changed · +28 lines 337b5afae33b
- 2mo ago First seen · 251 lines · 43 tokens per session scan A a7079529daa3
geological-modelling is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 2,716 once invoked, about $0.0002 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 2026-08-30.
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