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 geostatspygit 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/geostatspy)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/geostatspy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/geostatspy/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/geostatspy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/geostatspy.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 | $0.00095 | $0.02292 |
| Opus 5 | $0.00048 | $0.01146 |
| Sonnet 5 | $0.00019 | $0.00458 |
| Haiku 4.5 | $0.00010 | $0.00229 |
Grade A, and why
geostatspy 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 10d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeostatsPy - Geostatistical Analysis
Quick Reference
import geostatspy.GSLIB as GSLIB
import geostatspy.geostats as geostats
import pandas as pd
df = pd.read_csv('data.csv')
df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity') # Transform
lag, gamma, npairs = geostats.gamv(df, 'X', 'Y', 'npor', # Variogram
tmin=-9999, tmax=9999, xlag=50, xltol=25, nlag=15,
azm=0, atol=22.5, bandwh=9999, bandwd=9999)
vario = GSLIB.make_variogram(nug=0.0, nst=1, it1=1, cc1=1.0, # Model
azi1=0, hmaj1=300, hmin1=300)
est, var = geostats.kb2d(df, 'X', 'Y', 'npor', ..., vario=vario) # Krige
Key Functions
| Category | Functions |
|---|---|
| Visualization | locmap, pixelplt, hist |
| Variogram | gamv, vmodel |
| Kriging | kb2d, kb3d |
| Simulation | sgsim, sisim |
| Transforms | nscore, backtr |
| Declustering | declus |
Common Operations
1. Normal Score Transform
df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity')
original = geostats.backtr(nscore_data, tvpor, tnspor, zmin=0, zmax=0.3)
2. Experimental Variogram
lag, gamma, npairs = geostats.gamv(
df, 'X', 'Y', 'npor',
tmin=-9999, tmax=9999, # Trimming limits
xlag=50, xltol=25, # Lag distance, tolerance
nlag=15, azm=0, atol=22.5, bandwh=9999, bandwd=9999)
3. Variogram Model
# Types: 1=spherical, 2=exponential, 3=gaussian
vario = GSLIB.make_variogram(
nug=0.0, nst=1, # Nugget, number of structures
it1=1, cc1=1.0, # Type, sill contribution
azi1=0, hmaj1=300, hmin1=300) # Azimuth, major/minor range
4. Kriging (kb2d)
est, var = geostats.kb2d(
df, 'X', 'Y', 'npor', tmin=-9999, tmax=9999,
nx=50, xmn=25, xsiz=50, # Grid X: ncells, origin, size
ny=50, ymn=25, ysiz=50, # Grid Y
nxdis=1, nydis=1, ndmin=1, ndmax=10,
radius=500, ktype=0, skmean=0.0, vario=vario) # ktype: 0=simple, 1=ordinary
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.
- 10d ago First seen · 192 lines · 95 tokens per session scan A a4ec09648c3c
geostatspy is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (58 stars, last pushed 5mo ago), licensed MIT. It adds 95 tokens to every session and 2,292 once invoked, about $0.0005 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 2026-08-30.
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