A Python tool for regression analysis where the relationship between variables can change from place to place, and optionally over time. It uses geographic coordinates to study these local differences.

Use it to model spatial or time-and-space data, estimate location-specific effects, create coefficient maps, and review measures such as R², AIC, RMSE, and F-tests.

It helps avoid treating an entire region as if the same factors affect every location in the same way.

gnnwr — Skill for Claude Code and Codex by SteadfastAsArt

Install

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.

Any agent

npx skills add SteadfastAsArt/geoscience-skills --skill gnnwr

Clone the repo

git clone --depth 1 https://github.com/SteadfastAsArt/geoscience-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin gnnwr — /plugin install gnnwr after adding the marketplace above.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gnnwr/github.svg)](https://agentmods.dev/skills/steadfastasart/geoscience-skills/gnnwr)

Your own site

<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.

Your own site · 80×15

<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>

Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,209 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00124	$0.02209
Opus 5	$0.00062	$0.01104
Sonnet 5	$0.00025	$0.00442
Haiku 4.5	$0.00012	$0.00221

Measured 10d ago against content hash f8fe513c3a15, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 10d 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.

gnnwr/SKILL.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 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

Read the full file on GitHub · 215 lines

Files

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.

Changes

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.

10d ago First seen · 215 lines · 124 tokens per session scan A f8fe513c3a15

Subscribe to this mod's changes

gnnwr is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (58 stars, last pushed 5mo ago), licensed MIT. It adds 124 tokens to every session and 2,209 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.

Analytics and metrics Claude Code Coefficient Mapping GNNWR GTNNWR GWR Geographic Weighting Non-Stationarity Scientific Spatial Regression ai-agents geophysics geoscience spatial-analysis

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