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 mplstereonetgit 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/mplstereonet)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/mplstereonet"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/mplstereonet/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/mplstereonet"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/mplstereonet.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 | $0.00114 | $0.01816 |
| Opus 5.5 | $0.00046 | $0.00726 |
| Sonnet 5.5 | $0.00023 | $0.00363 |
| Haiku 4.5 | $0.00011 | $0.00182 |
Grade A, and why
mplstereonet 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mplstereonet - Stereonets for Matplotlib
Quick Reference
import mplstereonet
import matplotlib.pyplot as plt
# Create stereonet
fig, ax = mplstereonet.subplots()
# Plot plane and pole (strike/dip, right-hand rule)
ax.plane(315, 45, 'b-') # Great circle
ax.pole(315, 45, 'ko') # Pole to plane
# Plot lineation (trend/plunge)
ax.line(120, 30, 'r^')
ax.grid()
plt.savefig('stereonet.png', dpi=150)
Key Functions
| Function | Purpose |
|---|---|
mplstereonet.subplots() |
Create stereonet figure and axes |
ax.plane(strike, dip) |
Plot great circle |
ax.pole(strike, dip) |
Plot pole to plane |
ax.line(trend, plunge) |
Plot lineation point |
ax.density_contourf() |
Filled density contours |
mplstereonet.fit_girdle() |
Best-fit great circle |
mplstereonet.find_mean_vector() |
Mean orientation |
Essential Operations
Multiple Measurements with Contours
import numpy as np
strikes = [45, 52, 38, 48, 55, 41, 50, 43]
dips = [25, 30, 22, 28, 35, 24, 32, 27]
fig, ax = mplstereonet.subplots()
# Density contour of poles
ax.density_contourf(strikes, dips, measurement='poles', cmap='Reds')
ax.pole(strikes, dips, 'k.', markersize=5)
ax.grid()
ax.set_title('Bedding Orientations')
plt.savefig('density.png', dpi=150)
Calculate Mean Orientation
# Fit best-fit plane (girdle)
mean_strike, mean_dip = mplstereonet.fit_girdle(strikes, dips)
# Or calculate mean pole for clustered data
lon, lat = mplstereonet.pole(strikes, dips)
mean_lon, mean_lat = mplstereonet.find_mean_vector(lon, lat)
mean_s, mean_d = mplstereonet.pole2strike(mean_lon, mean_lat)
Pi-Diagram (Fold Axis)
# Bedding measurements around a fold
strikes = np.array([20, 35, 50, 70, 90, 110, 130, 150, 165, 180])
dips = np.array([45, 40, 35, 30, 25, 30, 35, 40, 45, 50])
fig, ax = mplstereonet.subplots()
ax.pole(strikes, dips, 'ko', markersize=6)
# Fit girdle to poles - fold axis is pole to girdle
girdle_strike, girdle_dip = mplstereonet.fit_girdle(strikes, dips)
ax.plane(girdle_strike, girdle_dip, 'r-', linewidth=2)
fold_trend, fold_plunge = mplstereonet.pole(girdle_strike, girdle_dip)
ax.line(fold_trend, fold_plunge, 'r^', markersize=12, label='Fold axis')
ax.grid()
ax.legend()
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 · +2 lines · +1 tokens per session 92ede9b419e8
- 2mo ago First seen · 194 lines · 113 tokens per session scan A a0ee23ba4f2d
mplstereonet is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 114 tokens to every session and 1,816 once invoked, about $0.0005 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.
Other skills, from other repositories
earthquake-guide
Gates and workflows for the earthquake tools (USGS ComCat and EMSC). Use to judge how bad a quake was, read USGS aftershock odds, check whether a depth or location holds up, match one event across USGS and EMSC, or count and list events over years or in a region — before concluding from a missing product, a default…
glm-lake-mendota
Simulate vertical water temperature profiles for lakes using the General Lake Model (GLM), calibrate key parameters to minimize RMSE against field observations, and produce validated NetCDF output.
xarray-netcdf
Labeled multi-dimensional array analysis with xarray: NetCDF/HDF5 I/O, lazy Dask loading, rechunking, Zarr stores, and CF conventions.
obspy-seismology
Seismological data analysis with ObsPy — FDSN waveform download, response removal, phase picking, moment tensor inversion, and seismicity mapping.
soil-data
Soil data analysis via SoilGrids 2.0 REST API and SSURGO: SOC stocks, texture classification, kriging interpolation, and soil profile visualization.
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…