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 gemgisgit 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/gemgis)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/gemgis"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gemgis/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/gemgis"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gemgis.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 | $0.00103 | $0.01310 |
| Opus 5.5 | $0.00041 | $0.00524 |
| Sonnet 5.5 | $0.00021 | $0.00262 |
| Haiku 4.5 | $0.00010 | $0.00131 |
Grade A, and why
gemgis 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GIS contacts and DEM elevations
Use GemGIS to extract geological observations from spatial data. Establish the horizontal CRS, vertical units/datum, geometry type and missing-data policy before sampling. Model coordinates must share a projected metre CRS; converting horizontal coordinates does not transform elevations or their datum.
Extract contact vertices
The following function accepts a single-band DEM already in a metre projected
CRS, with elevations in metres. dem is an open Rasterio dataset, not a path.
GemGIS repeats attributes when a line becomes multiple points: do not overwrite
those attributes using the original feature index.
import gemgis as gg
from rasterio.transform import rowcol
import numpy as np
from pyproj import CRS
def contacts_at_dem(contacts, dem):
crs = CRS.from_user_input(dem.crs)
if (contacts.crs is None or not crs.is_projected or dem.count != 1
or any(not np.isclose(a.unit_conversion_factor, 1.) for a in crs.axis_info[:2])):
raise ValueError('Require a known vector CRS and a single-band metric DEM')
if contacts.empty or not contacts.is_valid.all() or contacts.geometry.has_z.any():
raise ValueError('Require valid nonempty 2D contact geometry')
if 'formation' not in contacts or contacts['formation'].isna().any():
raise ValueError('Formation labels are required')
points = gg.vector.extract_xy(contacts.to_crs(dem.crs))
xy = points[['X', 'Y']].to_numpy()
rows, cols = np.asarray(rowcol(dem.transform, xy[:, 0], xy[:, 1]))
if np.any((rows < 0) | (rows >= dem.height) | (cols < 0) | (cols >= dem.width)):
raise ValueError('Vertices outside DEM')
samples = np.ma.vstack(list(dem.sample(xy, indexes=1, masked=True)))[:, 0]
if np.ma.getmaskarray(samples).any() or not np.isfinite(samples.data).all():
raise ValueError('Missing DEM elevations')
return gg.vector.extract_xyz(gdf=points, dem=dem)[['X', 'Y', 'Z', 'formation']]
Open with with rasterio.open(dem_path) as dem: and call this function while the
dataset is open. Pixel sampling uses the containing cell; it is not bilinear
interpolation. Do not fill NoData or outside-extent samples with zero.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · -69 lines c616d5d4f847
- 1mo ago Changed · +2 lines · +1 tokens per session 99e1dc194a95
- 2mo ago First seen · 177 lines · 102 tokens per session scan A f1e634bced9d
gemgis is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 1,310 once invoked, about $0.0004 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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