GeoAI Skills

arcgis-pro-automation

Automate controlled local ArcGIS Pro and ArcPy workflows through arcgis-mcp-bridge: inspect .aprx projects and file geodatabases, run geoprocessing, projection, raster, network, spatial-statistics, editing, symbology, and layout export with path and mutation guards. Use when the user explicitly names ArcGIS Pro, ArcPy, .aprx, .gdb, Esri geoprocessing, muend/arcgis-mcp-bridge, its health_check, PathGuard, or confirmation gates, or sketch-to-GIS extraction. Do not trigger for ArcGIS Online or Enterprise administration, QGIS or PyQGIS, generic open-source GIS, or live GUI control of an already-open ArcGIS Pro session.

cartography-geoviz

Always invoke before answering any request to create, compare, design, or review a user-facing map, even if the request is terse or underspecified. Covers publication maps, choropleths, map series and small multiples, comparable multi-date panels, proportional/bivariate/flow maps, raster rendering, and interactive web maps. Includes classification, color, legends, projections, accessibility, and large-data aggregation. Do not trigger for a temporary diagnostic plot inside another analysis.

change-detection

Change analysis, once the observations are comparable. Not for cases whose blocker is comparability itself: mixed sensors, product levels or processing baselines to remote-sensing-analysis, undocumented vertical datums to point-cloud-lidar, multi-decade archive trends over large areas to google-earth-engine. Matching product level does not prove comparability. Otherwise invoke for what, where or how much changed: two-scene comparison, deforestation, urban growth, disaster damage, parcel-change audits, bi-temporal differencing, post-classification comparison, adjusted area, break detection in a series in hand (BFAST/LandTrendr/CCDC). Seasonal mismatch is this skill's own confounder; a documented datum with a stated accuracy budget is settled comparability. Keep both.

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-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.

geoai-orchestrator

Route genuinely ambiguous or multi-stage geospatial work across specialist skills while enforcing shared CRS, validity, leakage, units, verification, and reproducibility rules. Use for requests spanning multiple stages such as acquisition, imagery, modeling, analysis, and map delivery, or for an explicit end-to-end pipeline. Never invoke for one domain merely because a parameter is unclear. Code implementation/review, backend or platform choice, and production-readiness review are direct specialist tasks. Do not add this skill as a layer around one specialist.

geostatistics-interpolation

Turn scattered point measurements into continuous surfaces with quantified uncertainty: variogram modeling, ordinary/universal/regression kriging, IDW, and spatially honest cross-validation. Use when unobserved values must be estimated from sparse samples such as stations, wells, or soundings. Trigger on "interpolate", "kriging", "variogram", or "IDW"; a named interpolation method is sufficient even when the requested surface is described informally as a heatmap. Do not use for point-density heatmaps, zonal aggregation, or raster resampling without value interpolation.

google-earth-engine

Invoke 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.

mcda-suitability-analysis

Always invoke for spatial suitability, site selection, AHP, criteria weights, or weighted-overlay work, including audits of inconsistent pairwise judgments and requests for only a final map. Covers consistency, standardization, constraints, ranked surfaces, shortlists, and sensitivity. Route travel-time placement and location-allocation to network-accessibility-analysis.

ml-experiment-standards

Always invoke for training, validating, tuning, benchmarking, or claiming readiness of a predictive model. Covers leakage audits, spatial and grouped splits, metrics, reproducibility, and honest reporting. Invoke especially when spatial dependence, split design, or deployment geography is unknown; uncertainty is a reason to use this skill. Do not trigger for descriptive EDA or non-predictive statistical inference.

movement-trajectory

Movement and trajectory analytics from GPS/GNSS tracks: cleaning, stop/trip detection, road-network map matching, speed/direction, flow aggregation, and origin-destination construction. Use for fleets, human mobility, animal tracking, AIS, or sports tracks. Trigger on GPS points, GPX, trajectories, stop detection, map matching, or timestamped positions per moving object. Also invoke for privacy, aggregation, de-identification, or release of individual trajectories. Use network-accessibility-analysis for hypothetical routes, isochrones, or static OD costs without observed tracks.

network-accessibility-analysis

Always invoke for access to facilities or opportunities by walking, driving, cycling, or public transport, even for a conceptual question with no routing terms or data yet. Covers hospital and service access, transit/GTFS, routes, isochrones, OD matrices, closest facility, 2SFCA, walkability, coverage, and equity. Invoke when Euclidean buffers proxy for network access. Use movement-trajectory for observed tracks and MCDA for suitability without network costs.