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 pyrolitegit 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/pyrolite)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/pyrolite"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/pyrolite/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/pyrolite"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/pyrolite.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 | $0.00110 | $0.01554 |
| Opus 5 | $0.00055 | $0.00777 |
| Sonnet 5 | $0.00022 | $0.00311 |
| Haiku 4.5 | $0.00011 | $0.00155 |
Grade A, and why
pyrolite 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 9d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pyrolite - Geochemistry Analysis
Quick Reference
import pandas as pd
import matplotlib.pyplot as plt
from pyrolite.geochem.norm import get_reference_composition
df = pd.read_csv('samples.csv')
df.pyrochem # Geochemistry methods
df.pyrocomp # Compositional methods
# Normalize and plot REE
chondrite = get_reference_composition('Chondrite_McDonough1995')
ax = df.pyrochem.normalize_to(chondrite, units='ppm').pyroplot.REE(unity_line=True)
Key Modules
| Module | Purpose |
|---|---|
pyrolite.plot |
Ternary, spider diagrams |
pyrolite.geochem.norm |
Normalization references |
pyrolite.comp |
CLR, ALR, ILR transforms |
pyrolite.plot.templates |
TAS, Pearce diagrams |
pyrolite.mineral.normative |
CIPW norm |
Essential Operations
Ternary Diagram
ax = df[['SiO2', 'CaO', 'Na2O']].pyroplot.scatter(c='k', s=50)
TAS Diagram
from pyrolite.plot.templates import TAS
df['Na2O_K2O'] = df['Na2O'] + df['K2O']
ax = TAS()
ax.scatter(df['SiO2'], df['Na2O_K2O'], c='red', s=50)
REE Pattern
chondrite = get_reference_composition('Chondrite_McDonough1995')
ax = df.pyrochem.normalize_to(chondrite, units='ppm').pyroplot.REE(unity_line=True)
Trace Element Spider
pm = get_reference_composition('PM_McDonough1995')
ax = df.pyrochem.normalize_to(pm).pyroplot.spider(unity_line=True)
Compositional Transforms
df_closed = df.pyrocomp.renormalise(scale=100) # Closure
df_clr = df.pyrocomp.CLR() # Centered log-ratio
df_alr = df.pyrocomp.ALR() # Additive log-ratio
df_ilr = df.pyrocomp.ILR() # Isometric log-ratio
Element Ratios and Anomalies
df['La_Yb'] = df['La'] / df['Yb'] # LREE/HREE
df['Eu_Eu*'] = df['Eu'] / (df['Sm'] * df['Gd']) ** 0.5 # Eu anomaly
lambdas = df.pyrochem.lambda_lnREE() # REE shape
CIPW Norm
from pyrolite.mineral.normative import CIPW_norm
norm = CIPW_norm(df) # df must have major oxides in wt%
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.
- 9d ago First seen · 162 lines · 110 tokens per session scan A 828a255e34f9
pyrolite is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (57 stars, last pushed 5mo ago), licensed MIT. It adds 110 tokens to every session and 1,554 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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