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 pylopsgit 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/pylops)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/pylops"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/pylops/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/pylops"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/pylops.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 | $0.00103 | $0.01673 |
| Opus 5.5 | $0.00041 | $0.00669 |
| Sonnet 5.5 | $0.00021 | $0.00335 |
| Haiku 4.5 | $0.00010 | $0.00167 |
Grade A, and why
pylops 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyLops - Linear Operators Library
Quick Reference
import numpy as np
import pylops
# Create operator and apply forward/adjoint
A = pylops.FirstDerivative(n=100, dtype='float64')
y = A @ x # Forward: y = A @ x
x_adj = A.H @ y # Adjoint: x = A.H @ y
x_est = A / y # Solve inverse problem
Key Classes
| Class | Purpose |
|---|---|
LinearOperator |
Base class for all operators |
VStack/HStack |
Vertical/horizontal operator stacking |
BlockDiag |
Block diagonal operator composition |
Essential Operations
Basic Operators
# Diagonal operator
D = pylops.Diagonal(np.array([1., 2., 3.]))
y = D @ x; x_adj = D.H @ y
# Derivatives
D1 = pylops.FirstDerivative(n, dtype='float64')
D2 = pylops.SecondDerivative(n, dtype='float64')
G = pylops.Gradient(dims=(64, 64), dtype='float64')
Convolution
wavelet = np.sin(np.linspace(0, 2*np.pi, 21)) * np.hanning(21)
C = pylops.signalprocessing.Convolve1D(n, h=wavelet, offset=10)
y = C @ x # Convolve
x_adj = C.H @ y # Correlation (adjoint)
Compose and Stack Operators
# Chain: y = C @ B @ A @ x
composed = pylops.Smoothing1D(5, n) @ pylops.FirstDerivative(n) @ pylops.Identity(n)
# Stack operators
V = pylops.VStack([A, B]) # Vertical: (2n, n)
H = pylops.HStack([A, B]) # Horizontal: (n, 2n)
BD = pylops.BlockDiag([A, B]) # Block diagonal: (2n, 2n)
Solve Inverse Problems
# Simple least squares
x_est = A / y
# Normal equations
x_est = pylops.optimization.leastsquares.NormalEquationsInversion(A, None, y)
# Regularized inversion with smoothness
Reg = pylops.SecondDerivative(n)
x_est = pylops.optimization.leastsquares.RegularizedInversion(
A, [Reg], y, epsRs=[0.1]
)
Iterative Solvers
x_lsqr = pylops.optimization.solver.lsqr(A, y, iter_lim=100)[0]
x_cgls = pylops.optimization.solver.cgls(A, y, niter=100)[0]
Sparsity-Promoting Inversion
x_l1 = pylops.optimization.sparsity.fista(A, y, niter=100, eps=0.1)[0]
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 43beab2433f8
- 2mo ago First seen · 179 lines · 102 tokens per session scan A 16a80fb0bc46
pylops is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 1,673 once invoked, about $0.0004 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.
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