A geospatial science toolkit covering maps, satellite and aerial imagery, location data, spatial analysis, and machine learning for Earth observation.

Processing Sentinel, Landsat, MODIS, radar, hyperspectral, vector, raster, network, cloud, database, and point-cloud data.

It brings guidance and examples for working with many kinds of geographic data and analysis tasks.

geomaster — Skill for Claude Code and Codex by Lzy599775

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 Lzy599775/agent-auto-sci-skills --skill geomaster

Clone the repo

git clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skills

Made for: Claude Code, Codex.

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/lzy599775/agent-auto-sci-skills/geomaster/github.svg)](https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/geomaster)

Your own site

<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/geomaster"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/geomaster/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/lzy599775/agent-auto-sci-skills/geomaster"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/geomaster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,765 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 100% copy Near-identical to another mod 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	$0.00080	$0.02765
Opus 5.5	$0.00032	$0.01106
Sonnet 5.5	$0.00016	$0.00553
Haiku 4.5	$0.00008	$0.00277

Measured 13d ago against content hash 7b3b88f13f00, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

geomaster 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 13d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/raster_workflows.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

100% identical to geomaster — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/kdense-geospatial-rs-selected/subskills/k-dense/geomaster/SKILL.md · 232 lines

How it starts

The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GeoMaster

Geospatial analysis across vector/raster GIS, remote sensing, spatial ML, terrain, networks, and scientific applications. Start with the relevant workflow, inspect input provenance, and load only the reference needed for the task.

Tested scope and installation

The local recipe suite targets GeoPandas 1.2.0, Rasterio 1.5.2, Shapely 2.1.2, PyProj 3.8.0, Rioxarray 0.23.0, Xarray 2026.9.0, OSMnx 2.1.1 and PySTAC Client 0.9.0. See review and source ledger for execution limits. Examples requiring actual input files are templates; authenticated Earth Engine/CDS/commercial services, desktop GIS and GPU training remain illustrative.

# In a dedicated environment; install only the workflow's optional dependencies.
uv venv --python 3.13
uv pip install geopandas==1.2.0 rasterio==1.5.2 shapely==2.1.2 pyproj==3.8.0
uv pip install rioxarray==0.23.0 xarray==2026.9.0 'dask[array]' scikit-learn==1.9.1
uv pip install pystac-client==0.9.0 planetary-computer==1.0.0 odc-stac
# For osgeo/native CLI or PDAL, use a separate conda-forge environment:
# conda create -n geo-native -c conda-forge python=3.13 gdal pdal python-pdal

Install TorchGeo/PyTorch, RSGISLib, Py6S/6S, ArcPy, QGIS or other specialist runtimes separately when required. A Rasterio wheel includes its own GDAL library; it does not install osgeo or the GDAL command-line programs.

Workflow

Record product/collection ID, acquisition time, processing baseline, license, band names, scale/offset, QA meaning, horizontal/vertical CRS and units.
Verify CRS from authoritative metadata. set_crs labels coordinates; to_crs transforms them. Never infer an unknown CRS from plausible bounds.
Align extent, affine transform, dimensions, pixel registration and resolution. Reproject categorical masks with nearest-neighbor resampling; choose an appropriate resampler for continuous data. Matching array shape alone does not prove alignment.
Preserve nodata, clouds, shadows and saturation masks. Convert unsigned integers to floating point before differences; apply the provider's radiometric transform once.
Run the analysis at a defensible support/resolution. Keep training labels and validation blocks separate. Terrain elevation and horizontal units must agree.
Export CRS, transform, valid-data mask, units, model settings and provenance. Check numeric expectations on a small known fixture before scaling up.

Read the full file on GitHub · 232 lines

Files

What ships with it

17 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.

13d ago Changed · -153 lines · -68 tokens per session 7b3b88f13f00
1mo ago Changed · +1 lines 920615befd7a
1mo ago Changed · +17 lines aa1639b853a3
1mo ago First seen · 367 lines · 148 tokens per session scan A 213ea4982a9a

Subscribe to this mod's changes

geomaster is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed 9d ago), licensed MIT. It adds 80 tokens to every session and 2,765 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to geomaster, differing in 0 lines, and is treated as a copy.

Data and AI Scientific bibliometrics codex-skills geospatial literature-review research-agent urban-health

code-execution

Execute scientific Python code for computation, data analysis, simulation, and verification. Use when: (1) running statistical analyses, (2) numerical computation, (3) data processing pipelines, (4) verifying calculations, (5) running simulations. NOT for: literature search (use literature-search), writing papers (use…

beita6969/ScienceClaw · 78 tokens

model-assessment

Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.

Aperivue/medsci-skills · 66 tokens

model-scaffold

Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.

Aperivue/medsci-skills · 62 tokens

radiomics-ml

Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.

Aperivue/medsci-skills · 64 tokens

imaging-data

Use when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling. Profiles spacing, orientation, intensity, label integrity, foreground fraction and target volume, gates them against the plan, then plans and audits preprocessing and augmentation for leakage.

Aperivue/medsci-skills · 54 tokens

model-selection

Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.

Aperivue/medsci-skills · 63 tokens