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 | $0.00113 | $0.01789 |
| Opus 5 | $0.00056 | $0.00894 |
| Sonnet 5 | $0.00023 | $0.00358 |
| Haiku 4.5 | $0.00011 | $0.00179 |
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 12d 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 — 194 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.
- 12d ago First seen · 194 lines · 113 tokens per session scan A a0ee23ba4f2d
mplstereonet is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (59 stars, last pushed 5mo ago), licensed MIT. It adds 113 tokens to every session and 1,789 once invoked, about $0.0006 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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