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 rock-physics-avogit 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/rock-physics-avo)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/rock-physics-avo"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/rock-physics-avo/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/rock-physics-avo"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/rock-physics-avo.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 | $0.00052 | $0.02413 |
| Opus 5 | $0.00026 | $0.01207 |
| Sonnet 5 | $0.00010 | $0.00483 |
| Haiku 4.5 | $0.00005 | $0.00241 |
Grade A, and why
rock-physics-avo 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rock Physics & AVO Workflow
End-to-end pipeline for rock physics analysis and AVO feasibility studies, from well log preparation through elastic property calculation, Gassmann fluid substitution, AVO modelling, and synthetic seismogram generation.
Skill Chain
lasio / welly bruges segyio / obspy
[Well Log Prep] --> [Rock Physics] --> [Synthetics / Tie]
| | |
Load LAS/DLIS Elastic moduli Wavelet extraction
QC & despike Gassmann fluid sub Reflectivity series
Resample curves AVO intercept/gradient Convolve synthetic
Extract Vp, Vs, rho Backus averaging Well-seismic tie
Decision Points
| Task | Library | When to Use |
|---|---|---|
| Load well logs | lasio / dlisio | Always the first step |
| Curve QC and management | welly | Multi-curve processing, despiking |
| Elastic moduli, AVO equations | bruges | Core rock physics calculations |
| Gassmann fluid substitution | bruges | Predict fluid replacement effects |
| Wavelet extraction from seismic | segyio + bruges | When tying to seismic |
| Synthetic seismogram | bruges | Generate reflectivity and convolve |
| Dispersion curves | disba | Surface wave rock physics |
Step-by-Step Orchestration
Stage 1: Well Log Preparation (lasio + welly)
import lasio
import numpy as np
from welly import Well
# Load well with sonic, density, and shear sonic
las = lasio.read('well.las')
df = las.df().dropna()
# Extract elastic logs
depth = df.index.values
vp = 1e6 / df['DT'].values # P-wave velocity (m/s) from sonic (us/ft)
vs = 1e6 / df['DTS'].values # S-wave velocity (m/s) from shear sonic
rho = df['RHOB'].values * 1000 # Density (kg/m3) from g/cc
# QC: check ranges
assert np.all(vp > 1500) and np.all(vp < 7000), "Vp out of range"
assert np.all(vs > 500) and np.all(vs < 4000), "Vs out of range"
assert np.all(rho > 1500) and np.all(rho < 3200), "Density out of range"
# If no shear sonic, estimate from Vp
# Castagna mudrock line: Vs = 0.8621 * Vp - 1172.4 (m/s)
if 'DTS' not in df.columns:
vs = 0.8621 * vp - 1172.4
vs = np.maximum(vs, 300) # Floor for shallow sediments
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 · 241 lines · 52 tokens per session scan A e7018817754d
rock-physics-avo is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (59 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 2,413 once invoked, about $0.0003 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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