sadassimov/geemu-skill

★ 24PythonAudience · researcherComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((repo))
    Skill Installation
      Clone full repo
      Place in skills dir
      Claude Code support
      Codex support
    Workflow Structure
      Confirm setup
      Define study area
      Choose data layers
      Write and run code
    Knowledge Base
      Proxy config
      Data selection
      Admin boundaries
      Export strategies
    Example Tasks
      Wildfire recovery
      Vegetation indices
      Tiled exports
      Land cover mapping
    Templates
      Run decisions log
      Data layer records
      Markdown format

mindmap root((repo)) Skill Installation Clone full repo Place in skills dir Claude Code support Codex support Workflow Structure Confirm setup Define study area Choose data layers Write and run code Knowledge Base Proxy config Data selection Admin boundaries Export strategies Example Tasks Wildfire recovery Vegetation indices Tiled exports Land cover mapping Templates Run decisions log Data layer records Markdown format

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Things people build with this

USE CASE 1

Map vegetation changes after wildfires using satellite imagery and spectral indices

USE CASE 2

Monitor land cover changes over time with Google Earth Engine datasets

USE CASE 3

Compute and export spectral indices from Sentinel-2 and other satellite bands

USE CASE 4

Run structured GEE workflows with AI assistance for large geospatial dataset exports

Tech stack

PythonGoogle Earth EngineSentinel-2Claude CodeOpenAI CodexMarkdown

Getting it running

Difficulty · moderate Time to first run · 30min

Clone the full repository and place the folder in your AI assistant skills directory. Requires a Google Cloud project ID, authenticated GEE Python environment, and optionally a network proxy.

In plain English

GEEMu is a skill folder for AI coding assistants, specifically Claude Code and OpenAI Codex. A skill folder is a set of files you install locally that the assistant reads to gain specialized knowledge and a structured workflow for a particular domain. GEEMu's domain is Google Earth Engine, a cloud-based platform that lets researchers run satellite imagery analysis and remote sensing tasks at scale without managing their own computing infrastructure. The skill is designed for research-grade tasks: mapping vegetation changes after wildfires, monitoring land cover, computing spectral indices from satellite bands, and exporting large geospatial datasets. Rather than jumping straight to code, GEEMu instructs the assistant to first confirm the user's setup, including the Google Cloud project ID, authentication credentials, Python environment, and whether a network proxy is needed. Then it walks through defining the study area, choosing the right data layers, and thinking through boundary complexity before writing any code. Inside the folder there are several components. A main instruction file tells the assistant how to approach every task. A references folder contains detailed guidance on proxy configuration, data selection, administrative boundaries, and export strategies. A local knowledge database stored as text files can be searched by keyword to find relevant GEE examples and dataset information. There are also templates for recording run decisions and data layer choices in Markdown, plus complete example workflows for tasks like Sentinel-2 vegetation index calculations, tiled exports for large regions, and fire recovery analysis. To use it, you clone or download the repository and place the folder in the skills directory that Claude Code or Codex looks for. Because the local knowledge database files are tens of megabytes, the README emphasizes doing a full clone rather than cherry-picking files. Once installed, you tell the assistant to use GEEMu for any Google Earth Engine task, and it follows the structured workflow automatically. The README is bilingual, written in both English and Chinese.

Copy-paste prompts

Prompt 1

Use GEEMu to map post-wildfire vegetation recovery in my study area using Sentinel-2 NDVI time series

Prompt 2

Help me export a tiled land cover dataset for a large region using Google Earth Engine with the GEEMu workflow

Prompt 3

Walk me through selecting the right satellite data layers for monitoring urban expansion in my GEE project

Prompt 4

Use GEEMu to compute and visualize a spectral index for my region of interest, confirming my GCP credentials first

Prompt 5

Guide me through setting up a proxy-aware Google Earth Engine Python environment and run a sample analysis

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