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 gnnwrgit 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/gnnwr)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/gnnwr"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gnnwr/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/gnnwr"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gnnwr.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 | $0.00125 | $0.02243 |
| Opus 5.5 | $0.00050 | $0.00897 |
| Sonnet 5.5 | $0.00025 | $0.00449 |
| Haiku 4.5 | $0.00013 | $0.00224 |
Grade A, and why
gnnwr 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GNNWR - Geographically Neural Network Weighted Regression
Quick Reference
from gnnwr import models, datasets, utils
import pandas as pd
data = pd.read_csv("data.csv")
train, val, test = datasets.init_dataset(
data=data, test_ratio=0.2, valid_ratio=0.1,
x_column=["x1", "x2", "x3"], y_column=["y"],
spatial_column=["lon", "lat"], # REQUIRED: geographic coords
batch_size=32, process_fn="minmax_scale"
)
model = models.GNNWR(train, val, test, use_gpu=True, optimizer="Adam", start_lr=0.01)
model.run(max_epoch=200, early_stop=30)
result = model.reg_result(only_return=True) # DataFrame: coef_x1, coef_x2, ..., Pred_y
print(model.result()) # R², AIC, RMSE, F-tests summary
Spatiotemporal (GTNNWR)
train, val, test = datasets.init_dataset(
data=data, ...,
spatial_column=["lon", "lat"],
temp_column=["year", "month"], # add temporal coords
use_model="gtnnwr"
)
model = models.GTNNWR(train, val, test, use_gpu=True)
Large-Scale (N > 10k) — KNN Mode
train, val, test = datasets.init_dataset(
data=data, ..., knn_k=500 # only k nearest neighbor distances
)
# Memory: N=100k full=55GB → knn_k=2000 only 763MB
Key Classes
| Class | Purpose |
|---|---|
models.GNNWR |
Spatial regression with neural network geographic weighting |
models.GTNNWR |
Spatiotemporal regression with temporal + spatial weighting |
datasets.init_dataset |
Data splitting, normalization, distance matrix construction |
utils.Visualize |
Built-in folium interactive maps for coefficients and predictions |
Essential Operations
init_dataset Parameters
| Parameter | Default | Notes |
|---|---|---|
knn_k |
None | KNN sparse distance; None=full matrix |
process_fn |
"minmax_scale" | or "standard_scale" |
spatial_fun |
BasicDistance | Euclidean; or ManhattanDistance |
Reference |
None | "train", "train_val", or custom DataFrame |
sample_seed |
42 | Reproducibility |
What ships with it
2 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 654970a253e8
- 2mo ago First seen · 215 lines · 124 tokens per session scan A f8fe513c3a15
gnnwr is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 125 tokens to every session and 2,243 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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