google-earth-engine
SolidInvoke when Earth Engine, GEE, ee., or geemap is named; when work needs its server-side catalog; or when choosing Earth Engine versus local xarray or desktop processing for a large area or long archive. Covers image collections, masking, compositing, reducers, zonal statistics, time series, classification, quota-aware batching, and exports. This is an execution platform skill; combine it with remote-sensing-analysis or change-detection when those skills own the scientific method.
Install
Quality Score: 81/100
Skill Content
Details
- Author
- muend
- Repository
- muend/geoai-skills
- Created
- 1 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
Bundled in these plugins
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geo-data-engineering
Always invoke when geospatial data must be acquired, prepared, repaired, scaled, or moved through a repeatable pipeline. Covers open-data/OSM/STAC acquisition, spatial formats, CRS transforms, quality checks, and batch ETL architecture for growing or recurring joins. Invoke alongside PostGIS for database execution and alongside SWE standards when code is delivered. Do not trigger merely because another specialist reads analysis-ready data.
geo
GEO-first SEO analysis tool. Optimizes websites for AI-powered search engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) while maintaining traditional SEO foundations. Performs full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific optimization, schema markup, technical SEO, content quality (E-E-A-T), and client-ready GEO report generation. Use when user says "geo", "seo", "audit", "AI search", "AI visibility", "optimize", "citability", "llms.txt", "schema", "brand mentions", "GEO report", or any URL for analysis.
geo-deep-learning
Invoke before recommending, training, or auditing a neural method for geospatial imagery, including vision transformers, U-Net/DeepLab/SegFormer, object detection, pixel classification, building/road extraction, and EO foundation-model fine-tuning. Also invoke for neural chip-split validity, IoU/accuracy claims, augmentation, imbalanced losses, spatial validation, or sliding-window inference. Use remote-sensing-analysis for non-neural methods and change-detection when temporal change is the deliverable.