Geospatial Analysis

Community

beita6969

geospatial-analysis

Performs geospatial data analysis including GIS operations, spatial statistics, remote sensing image processing, geocoding, and cartographic visualization; trigger when users discuss maps, coordinates, satellite imagery, spatial patterns, or geographic data.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`geospatial-analysis`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Geospatial Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/geospatial-analysis
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/geospatial-analysis .claude/skills/geospatial-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Geospatial Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Geospatial Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Geospatial Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Trigger

Activate this skill when the user mentions:

GIS, geographic information systems, spatial data
Coordinates, latitude/longitude, projections, CRS
Spatial statistics, spatial autocorrelation, hotspot analysis
Remote sensing, satellite imagery, NDVI, land cover classification
Mapping, cartography, choropleth, heatmaps
Geocoding, reverse geocoding, routing, network analysis
Shapefiles, GeoJSON, raster data, vector data

Step-by-Step Methodology

Data acquisition and format assessment - Identify data types: vector (points, lines, polygons in shapefile/GeoJSON/GeoPackage) or raster (GeoTIFF, NetCDF). Determine coordinate reference system (CRS). Check for common issues: mixed CRS, topology errors, missing geometries.
Projection and transformation - Ensure all layers share the same CRS. Use geographic CRS (WGS84/EPSG:4326) for global data, projected CRS (UTM, state plane) for area/distance calculations. Apply appropriate datum transformation.
Spatial operations - Perform geoprocessing: buffer, intersect, union, clip, dissolve. For point data: spatial joins, nearest neighbor analysis. For raster: reclassification, map algebra, zonal statistics.
Spatial statistics - Test for spatial autocorrelation (Global Moran's I). Identify clusters and hotspots (Local Moran's I / LISA, Getis-Ord Gi*). For point patterns: kernel density estimation, Ripley's K function. For regression: spatial lag or spatial error models (GWR for non-stationarity).
Remote sensing analysis - Atmospheric correction and preprocessing. Compute indices (NDVI, NDWI, NDBI). Supervised classification (random forest, SVM) or unsupervised (K-means, ISODATA). Accuracy assessment with confusion matrix and Kappa statistic.
Visualization and cartography - Create maps with proper elements: title, scale bar, north arrow, legend, data source. Use appropriate color schemes (sequential for magnitude, diverging for deviation, qualitative for categories). Consider colorblind-safe palettes.
Validation - Verify spatial operations with visual inspection and area/count checks. Cross-validate classification accuracy. Assess edge effects in spatial statistics. Report spatial resolution and positional accuracy.

Key Databases and Tools

OpenStreetMap - Open geographic data
USGS Earth Explorer / Copernicus Open Access Hub - Satellite imagery
Natural Earth - Public domain map data
Census TIGER/Line - US geographic boundaries
QGIS / ArcGIS - GIS desktop software
GeoPandas / Rasterio / Folium - Python geospatial libraries
Google Earth Engine - Cloud-based remote sensing platform

Output Format

Maps with standard cartographic elements (title, legend, scale bar, north arrow, CRS noted).
Spatial statistics results with test statistic, p-value, and interpretation.
Classification accuracy as confusion matrix with overall accuracy, Kappa, and per-class metrics.
Coordinate data in standard formats (decimal degrees for geographic, meters for projected).
GeoJSON or shapefile outputs for derived spatial data.

Quality Checklist

CRS explicitly stated for all datasets and outputs
Projection appropriate for the analysis (equal-area for density, conformal for shape)
Spatial resolution and positional accuracy documented
Topology errors checked and cleaned
Color scheme appropriate for data type and accessible to colorblind viewers
Scale bar and north arrow included on all maps
Edge effects and modifiable areal unit problem (MAUP) considered
Data sources and vintage documented

Frequently asked questions

What does the Geospatial Analysis AI skill do?

Why use Geospatial Analysis on TypingMind?

Because you install it once and use it with any model. Geospatial Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Geospatial Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/geospatial-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Geospatial Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Geospatial Analysis?

As many as you like. As long as a model supports skills, you can use Geospatial Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Geospatial Analysis AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

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