agentmods.dev

geomaster

geomaster is a skill for Claude Code, Codex from K-Dense-AI/scientific-agent-skills. It costs 80 tokens per session (2,765 once invoked), scanned A, original, MIT.

A geospatial science toolkit for working with maps, satellite images, location data, and Earth-observation measurements.

In plain words
What is it for?
Use it to process satellite imagery, analyze geographic patterns, work with map features and point clouds, run spatial machine-learning tasks, and create maps.
Why use it?
It brings common geographic data tasks and examples into one place instead of requiring separate tools for each type of map or dataset.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 55krepo +1.5k changed 26d ago A scan Socket: passSnyk: warnSkillSpector: pass 80 tokens original MIT

Good fit Use it to process satellite imagery, analyze geographic patterns, work with map features and point clouds, run spatial machine-learning tasks, and create maps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agent-skills/geomaster
View source ↗ K-Dense-AI/scientific-agent-skills arxiv.org
About the project

Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.

K-Dense-AI/scientific-agent-skills · 54,953 stars · on GitHub · arxiv.org

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 K-Dense-AI/scientific-agent-skills --skill geomaster
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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.

agentmods badge for geomaster

README.md
[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/geomaster/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/geomaster)
Your own site
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/geomaster"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-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.

agentmods 80×15 button for geomaster

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/geomaster"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-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. Third-party audits
  • Socket pass 2 Oct 2026
  • Snyk warn 2 Oct 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce 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 26d 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 26d 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

Copies of this mod

8 near-identical copies found in the catalogue:

  • geomaster — 100% identical, 463 lines differ
  • geomaster — 100% identical, 465 lines differ
  • geomaster — 100% identical, 465 lines differ
  • geomaster — 100% identical, 465 lines differ
  • geomaster — 100% identical, 0 lines differ
  • geomaster — 97% identical, 596 lines differ
  • geomaster — 97% identical, 464 lines differ
  • geomaster — 95% identical, 596 lines differ
skills/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

  1. Record product/collection ID, acquisition time, processing baseline, license, band names, scale/offset, QA meaning, horizontal/vertical CRS and units.
  2. Verify CRS from authoritative metadata. set_crs labels coordinates; to_crs transforms them. Never infer an unknown CRS from plausible bounds.
  3. 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.
  4. Preserve nodata, clouds, shadows and saturation masks. Convert unsigned integers to floating point before differences; apply the provider's radiometric transform once.
  5. Run the analysis at a defensible support/resolution. Keep training labels and validation blocks separate. Terrain elevation and horizontal units must agree.
  6. 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.

  1. 26d ago Changed · -162 lines · -68 tokens per session 7b3b88f13f00
  2. 27d ago Changed · +9 lines d78005a33900
  3. 1mo ago Changed · +1 lines 920615befd7a
  4. 1mo ago Changed · +17 lines aa1639b853a3
  5. 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 K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Scientificai-scientistbioinformaticschemoinformaticsclaudeclaude-skillsclaudecode
Related

Other skills, from other repositories

discovery-toolbox

A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…

dekan-aleksandr/biodiscovery-skills · 122 tokens

discovery-director

Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…

dekan-aleksandr/biodiscovery-skills · 114 tokens

bio-interdomain-hgt

Detect and polarize interdomain horizontal gene transfer with reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.

fmschulz/omics-skills · 41 tokens

scholion

Second-opinion support for individuals and clinicians, one person's data at a time — genome, laboratory history, prescriptions, wearables. It reads them locally and states what the data cannot support. No data is baked into the skill: everyone supplies their own. Use it when you need to check a physician's…

CrossRead/scholion · 209 tokens

csag-extraction

Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.

fmschulz/omics-skills · 38 tokens

notebooks

Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.

fmschulz/omics-skills · 29 tokens