{"id":4257,"external_id":"plugins_6a68c8b958b88191b2bfeae31847c8da","name":"geoai-skills","display_name":"GeoAI Skills","developer":"Muhammed Enes Duran","category":"Data & Analytics","listing_language":"en","listing_language_details":{"method":"cld-0.13.0/listing-v1","reliable":true,"detected_at":"2026-10-01T13:22:03Z","input_sha256":"a85273ee7e7fa76a0c59599118d6f9a19b8aecad1ee349a121ad0eb2b22d4651","source_fields":["release.description","release.interface.short_description","release.interface.long_description"]},"version":"0.4.0","skill_count":18,"first_seen_at":"2026-09-30T22:02:35.000Z","last_seen_at":"2026-10-02T18:00:00.211Z","last_changed_at":"2026-09-30T22:02:35.000Z","install_count":null,"research_summary":null,"research_reviewed_at":null,"metadata":{"id":"plugins_6a68c8b958b88191b2bfeae31847c8da","name":"geoai-skills","scope":"GLOBAL","status":"ENABLED","release":{"id":"pluginrel_390c2972442c81918116d1c5f88d7b06","skills":[{"name":"arcgis-pro-automation","interface":{"brand_color":null,"iconography":"default","display_name":"ArcGIS Pro Automation","default_prompt":"Use $arcgis-pro-automation to plan and execute this ArcGIS Pro workflow safely.","icon_large_url":null,"icon_small_url":null,"short_description":"Automate guarded ArcGIS Pro and ArcPy work"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432db98b88191ba487446c7448bed"},{"name":"cartography-geoviz","interface":{"brand_color":null,"iconography":"cursor","display_name":"Cartography and Geovisualization","default_prompt":"Use $cartography-geoviz to design an accessible map for this spatial dataset.","icon_large_url":null,"icon_small_url":null,"short_description":"Design clear, accessible geospatial maps"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432e6e7a881918c58dd3ff3a6a8fc"},{"name":"change-detection","interface":{"brand_color":null,"iconography":"radar","display_name":"Change Detection","default_prompt":"Use $change-detection to design a defensible before-and-after change analysis.","icon_large_url":null,"icon_small_url":null,"short_description":"Detect and verify geospatial change"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432d928c48191bbde62d61d238fa9"},{"name":"geo-data-engineering","interface":{"brand_color":null,"iconography":"hierarchy","display_name":"Geospatial Data Engineering","default_prompt":"Use $geo-data-engineering to inspect, clean, and prepare this spatial dataset.","icon_large_url":null,"icon_small_url":null,"short_description":"Build reliable geospatial data pipelines"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432d97b548191972bf382bb9f55ad"},{"name":"geo-deep-learning","interface":{"brand_color":null,"iconography":"radar","display_name":"Geospatial Deep Learning","default_prompt":"Use $geo-deep-learning to design a spatially safe training and inference workflow.","icon_large_url":null,"icon_small_url":null,"short_description":"Train and validate geospatial neural models"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432d7a038819183a1e7eda54597d3"},{"name":"geoai-orchestrator","interface":{"brand_color":null,"iconography":"hierarchy","display_name":"GeoAI Orchestrator","default_prompt":"Use $geoai-orchestrator to plan and route this multi-stage geospatial project.","icon_large_url":null,"icon_small_url":null,"short_description":"Route complex GeoAI workflows safely"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432d695c88191b8bb0b6d375d4a31"},{"name":"geostatistics-interpolation","interface":{"brand_color":null,"iconography":"radar","display_name":"Geostatistics and Interpolation","default_prompt":"Use $geostatistics-interpolation to estimate a surface and quantify uncertainty.","icon_large_url":null,"icon_small_url":null,"short_description":"Interpolate spatial surfaces with uncertainty"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432e62a40819185e94c47a93f7aa6"},{"name":"google-earth-engine","interface":{"brand_color":null,"iconography":"default","display_name":"Google Earth Engine","default_prompt":"Use $google-earth-engine to design a quota-aware server-side Earth observation workflow.","icon_large_url":null,"icon_small_url":null,"short_description":"Build scalable Earth Engine workflows"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432e0df9c8191a3b252811b6ba527"},{"name":"mcda-suitability-analysis","interface":{"brand_color":null,"iconography":"radar","display_name":"MCDA Suitability Analysis","default_prompt":"Use $mcda-suitability-analysis to create and stress-test this site suitability model.","icon_large_url":null,"icon_small_url":null,"short_description":"Build transparent spatial suitability models"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432e300f08191b1a2f8f414bf7cde"},{"name":"ml-experiment-standards","interface":{"brand_color":null,"iconography":"default","display_name":"ML Experiment Standards","default_prompt":"Use $ml-experiment-standards to audit this model experiment for leakage and metric validity.","icon_large_url":null,"icon_small_url":null,"short_description":"Design honest, reproducible ML experiments"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432dca2a88191bb4efa1cff47405c"},{"name":"movement-trajectory","interface":{"brand_color":null,"iconography":"chart","display_name":"Movement and Trajectory Analytics","default_prompt":"Use $movement-trajectory to clean and analyze these timestamped movement tracks.","icon_large_url":null,"icon_small_url":null,"short_description":"Analyze observed movement and GPS tracks"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432df81088191a96fce5ee04d34a8"},{"name":"network-accessibility-analysis","interface":{"brand_color":null,"iconography":"default","display_name":"Network Accessibility Analysis","default_prompt":"Use $network-accessibility-analysis to measure network-based accessibility for this study.","icon_large_url":null,"icon_small_url":null,"short_description":"Analyze routing, service areas, and access"},"description":"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.","plugin_release_skill_id":"pluginrsk_6a7432de7de88191b39bebee461aa8cb"},{"name":"point-cloud-lidar","interface":{"brand_color":null,"iconography":"default","display_name":"Point Clouds and LiDAR","default_prompt":"Use $point-cloud-lidar to validate and process this point cloud workflow.","icon_large_url":null,"icon_small_url":null,"short_description":"Process LiDAR and 3D point cloud data"},"description":"LiDAR and point cloud processing: PDAL pipelines, LAS/LAZ/COPC handling, ground classification, DTM/DSM/CHM generation, canopy and building metrics, and photogrammetric (SfM) point clouds. Use when the primary input is LAS, LAZ, COPC, LiDAR, or an unstructured 3D point cloud. This skill owns vertical datum agreement, co-registration and the vertical-accuracy budget when two acquisitions are differenced with comparability not yet established; once datum, geoid and accuracy are documented, a subsidence or elevation-change question is change-detection's. Route a derived DEM, DTM, DSM or CHM to terrain-hydrology unless point-level classification, comparability, or metrics remain in scope.","plugin_release_skill_id":"pluginrsk_6a7432de2ae881918393c69adcd605c7"},{"name":"postgis-spatial-sql","interface":{"brand_color":null,"iconography":"radar","display_name":"PostGIS and Spatial SQL","default_prompt":"Use $postgis-spatial-sql to diagnose and optimize this spatial database task.","icon_large_url":null,"icon_small_url":null,"short_description":"Design and optimize spatial SQL workflows"},"description":"Invoke whenever spatial SQL or its execution backend is the decision: PostGIS, DuckDB Spatial, SpatiaLite, ST_* functions, recurring spatial joins, concurrent/growing workloads, or large GeoParquet queries. Covers backend selection, schemas, GiST/BRIN indexes, KNN, geometry versus geography, correctness benchmarks, and EXPLAIN optimization. Use PostGIS for managed concurrent services and embedded engines for bounded local analytics when evidence supports that choice. Use geo-data-engineering for acquisition, conversion, and file-based ETL without spatial SQL.","plugin_release_skill_id":"pluginrsk_6a7432e262b081918cf04f65dfe2834a"},{"name":"remote-sensing-analysis","interface":{"brand_color":null,"iconography":"radar","display_name":"Remote Sensing Analysis","default_prompt":"Use $remote-sensing-analysis to build a defensible imagery analysis workflow.","icon_large_url":null,"icon_small_url":null,"short_description":"Prepare and analyze Earth observation imagery"},"description":"Always invoke for classical analysis, classification, validation, or comparability of satellite, aerial, or drone imagery. This skill owns sensor, product, processing-level and processing-baseline harmonization, including multi-date inputs; add change-detection only after comparable observations exist. Two scenes of the same product level are not automatically comparable: Sentinel-2 L2A crossed a reflectance offset at Processing Baseline 04.00 in January 2022, so any pair spanning that date starts here. Covers spectral indices, masking, compositing, SAR, land cover, and accuracy assessment. Route neural methods to geo-deep-learning and planetary server-side execution to google-earth-engine.","plugin_release_skill_id":"pluginrsk_6a7432e3c1cc8191b235a18395dcf123"},{"name":"spatial-statistics","interface":{"brand_color":null,"iconography":"default","display_name":"Spatial Statistics","default_prompt":"Use $spatial-statistics to select and verify the right spatial inference method.","icon_large_url":null,"icon_small_url":null,"short_description":"Run rigorous spatial statistical analysis"},"description":"Always invoke before testing a geographic pattern for clustering, hotspots, dependence, or explanatory regression, even when aggregation or ordinary OLS is proposed as routine. Covers Moran's I, LISA, Getis-Ord Gi*, weights, MAUP and scale sensitivity for areas/grids, residual dependence, and spatial lag/error/GWR/MGWR models. Use ML standards for predictive evaluation and geostatistics for continuous surfaces from sparse samples.","plugin_release_skill_id":"pluginrsk_6a7432e3c5248191b15115eb552c8091"},{"name":"swe-devops-standards","interface":{"brand_color":null,"iconography":"code","display_name":"Geospatial SWE and DevOps","default_prompt":"Use $swe-devops-standards to make this geospatial code production-ready.","icon_large_url":null,"icon_small_url":null,"short_description":"Ship reliable geospatial code and pipelines"},"description":"Always invoke to review, repair, or deliver geospatial or GeoAI code, including contract compliance, security, error handling, transactions, tests, scripts, functions, notebooks, packages, CI/CD, and repository changes, even when deployment is not requested. Pair with the domain skill for ETL and other production code. Covers CRS/data invariants, dependencies, cross-platform reproducibility, automation, and shipping. Do not trigger for unrelated software or analysis requesting no code or repository artifact.","plugin_release_skill_id":"pluginrsk_6a7432e597048191838caa48f152c6a0"},{"name":"terrain-hydrology","interface":{"brand_color":null,"iconography":"radar","display_name":"Terrain and Hydrology","default_prompt":"Use $terrain-hydrology to design and verify this DEM-based terrain workflow.","icon_large_url":null,"icon_small_url":null,"short_description":"Analyze terrain, drainage, and watersheds"},"description":"Always invoke for terrain, drainage, viewshed, or visibility analysis from elevation, even before the DEM or correct surface is chosen. Covers DTM-versus-DSM selection, slope, aspect, curvature, hillshade, conditioning, flow direction/accumulation, streams, watersheds, and catchments. Use point-cloud-lidar first only when an elevation surface must be created from LiDAR or photogrammetric points.","plugin_release_skill_id":"pluginrsk_6a7432e5ad0c8191ab838323b8f9d7b5"}],"app_ids":[],"version":"0.4.0","keywords":["geoai","geospatial","remote-sensing","spatial-analysis","gis"],"interface":{"category":"Data & Analytics","logo_url":"https://files.openai.com/content?id=file_00000000350482438cb50e06b42db920","brand_color":"#08B6D4","website_url":"https://github.com/muend/geoai-skills","capabilities":["Geospatial analysis","Spatial data engineering","Remote sensing","Spatial databases","Cartography"],"logo_url_dark":null,"default_prompt":"Design a leakage-safe GeoAI workflow for my geospatial project.","developer_name":"Muhammed Enes Duran","default_prompts":["Design a leakage-safe GeoAI workflow for my geospatial project.","Audit this spatial analysis for CRS, leakage, and uncertainty failures.","Plan a defensible remote-sensing change-detection workflow."],"screenshot_urls":[],"long_description":"Eighteen measured Agent Skills for the full GeoAI lifecycle, with explicit safeguards for CRS, spatial leakage, uncertainty, provenance, and silent geospatial failure modes.","composer_icon_url":"https://files.openai.com/content?id=file_00000000becc81f4bc5b4b8f64e2f94e","short_description":"Measured geospatial workflows","plugin_category_id":"data & analytics","privacy_policy_url":"https://github.com/muend/geoai-skills/blob/main/PRIVACY.md","terms_of_service_url":"https://github.com/muend/geoai-skills/blob/main/TERMS.md","composer_icon_dark_url":null},"description":"Measured Agent Skills for reliable geospatial analysis, engineering, modeling, and delivery.","app_manifest":null,"display_name":"GeoAI Skills","app_templates":[],"onboarding_skill_name":null,"requires_local_executor":false},"created_at":"2026-07-28T15:30:58.138475Z","is_template":false,"connector_id":null,"discoverability":"UNLISTED","canonical_app_id":null},"research":null,"package_metadata":{"name":"geoai-skills","author":{"url":"https://github.com/muend","name":"Muhammed Enes Duran"},"license":"MIT","sources":[{"path":".codex-plugin/plugin.json","sha256":"e144d69a140d31280430ea7abbd9e64a14027591e70345cd033e70b39a1d3c2c"}],"version":"0.4.0","homepage":"https://github.com/muend/geoai-skills","keywords":["geoai","geospatial","remote-sensing","spatial-analysis","gis"],"repository":"https://github.com/muend/geoai-skills","artifact_id":11097,"observed_at":"2026-10-02T00:29:34Z","support_url":"https://github.com/muend/geoai-skills/issues","capabilities":["Geospatial analysis","Spatial data engineering","Remote sensing","Spatial databases","Cartography"],"field_sources":{"name":0,"author":0,"license":0,"version":0,"homepage":0,"keywords":0,"repository":0,"support_url":0,"capabilities":0},"extraction_version":1,"conflicts_or_errors":[]}}