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 wellygit 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/welly)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/welly"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/welly/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/welly"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/welly.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 | $0.00115 | $0.01573 |
| Opus 5.5 | $0.00046 | $0.00629 |
| Sonnet 5.5 | $0.00023 | $0.00315 |
| Haiku 4.5 | $0.00012 | $0.00157 |
Grade A, and why
welly 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
welly - Well Data Analysis
The examples are checked with welly 0.5.2. This release imports
pkg_resources, so its environment needs setuptools<81. The repository's
scientific test requirements record the other tested dependency versions.
Quick Reference
from welly import Well, Project
# Load single well
w = Well.from_las('well.las')
# Access data
df = w.df() # DataFrame
gr = w.data['GR'] # Curve object
values = gr.values # numpy array
depth = gr.basis # depth array
# Well info
print(w.name, w.uwi)
print(w.data.keys()) # Available curves
# Load multiple wells
p = Project.from_las('wells/*.las')
for well in p:
print(well.name)
Key Classes
| Class | Purpose |
|---|---|
Well |
Single well with curves, location, tops |
Project |
Collection of wells for multi-well workflows |
Curve |
Log curve with depth basis, units, and processing methods |
Essential Operations
Access Curve Data
gr = w.data['GR']
print(gr.mnemonic, gr.units) # Metadata
print(gr.start, gr.stop, gr.step) # Depth range
Process Curves
import numpy as np
gr = w.data['GR']
# Clean and filter
gr_clean = gr.despike(window_length=5, z=2)
gr_smooth = gr_clean.apply(window_length=11, func1d=np.nanmean)
w.data['GR'] = gr_clean # Use the cleaned curve in subsequent well exports.
# Optional dimensionless display array; retain GR's physical API units in w.
values = gr_clean.as_numpy().ravel()
finite = np.isfinite(values)
gr_norm = np.full_like(values, np.nan, dtype=float)
if finite.any() and np.ptp(values[finite]) > 0:
gr_norm[finite] = (values[finite] - values[finite].min()) / np.ptp(values[finite])
gr_resampled = gr_clean.to_basis(step=0.5)
gr_window = gr_clean.to_basis(start=1500, stop=1510) # Outside support is NaN.
See the Curve API for
despike, apply, and to_basis parameters. Review filtering against thin beds
before treating the processed curve as interpretation input.
What ships with it
4 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 · +17 lines · +1 tokens per session 9f9af9dfb803
- 2mo ago First seen · 170 lines · 114 tokens per session scan A 93fd21a83600
welly is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,573 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.
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