geopandas

21.8k

davila7

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

scvi-tools

21.8k

davila7

This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.

GeoMaster

10.8k

K-Dense-AI

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.

histolab

10.8k

K-Dense-AI

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

geopandas

10.8k

K-Dense-AI

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

scvi-tools

8.4k

anthropics

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

geospatial-analysis

571

benchflow-ai

Analyze geospatial data using geopandas with proper coordinate projections. Use when calculating distances between geographic features, performing spatial filtering, or working with plate boundaries and earthquake data.

gis-spatial-analyzer

376

a5c-ai

GIS spatial analysis capability for watershed delineation, floodplain mapping, and site analysis

h3-pg

355

Postgis H3 Pg Docs

PostgreSQL bindings for H3 hexagonal grid system. Use when working with H3 cells in Postgres, including spatial indexing, geometry/geography integration, and raster analysis.

bio-imaging-mass-cytometry-spatial-analysis

293

GPTomics

Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.

bio-spatial-transcriptomics-image-analysis

293

GPTomics

Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.

bio-spatial-transcriptomics-spatial-communication

293

GPTomics

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.

bio-workflows-imc-pipeline

293

GPTomics

End-to-end imaging mass cytometry workflow from raw acquisitions to spatial cell analysis. Orchestrates image preprocessing, segmentation, phenotyping, and spatial statistics. Use when analyzing imaging mass cytometry data end-to-end.

bio-spatial-transcriptomics-spatial-statistics

293

GPTomics

Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.

bio-workflows-spatial-pipeline

293

GPTomics

End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.

clash-detection-analysis

46

datadrivenconstruction

Detect and analyze geometric clashes between BIM elements. Identify hard clashes, soft clashes, and workflow conflicts using spatial analysis and rule-based detection.

precip_analyze_atlas14-variance

43

gpt-cmdr

Analyze spatial variability of NOAA Atlas 14 precipitation frequency estimates within HEC-RAS model domains using intelligent extent-based downloading. Helps determine whether uniform rainfall assumptions are appropriate for rain-on-grid modeling by calculating min/max/mean/range statistics within 2D flow areas or project extents. Uses NOAA CONUS NetCDF with HTTP byte-range requests for 99.9% data reduction compared to traditional state-level ZIP downloads. Primary sources: - ras_commander/precip/CLAUDE.md (lines 118-629) - Complete workflows - ras_commander/precip/Atlas14Grid.py - API reference - ras_commander/precip/Atlas14Variance.py - Variance analysis API - examples/725_atlas14_spatial_variance.ipynb - Working demonstration

ebfe_organize_models

43

gpt-cmdr

Organize downloaded FEMA eBFE/BLE model files into standardized 4-folder structure. Use when working with eBFE downloads that need organization into: - HMS Model/ (HEC-HMS hydrologic models) - RAS Model/ (HEC-RAS hydraulic models) - Spatial Data/ (GIS, terrain, geodatabases) - Documentation/ (reports, PDFs, metadata) Handles variable archive structures, nested zips, and mixed content through intelligent file analysis and recursive extraction.

precip_analyze_aorc

43

gpt-cmdr

Retrieves and processes AORC precipitation data for HEC-RAS/HMS models. Handles spatial averaging over watersheds, temporal aggregation, DSS export, and Atlas 14 design storms. Use when working with historical precipitation, AORC data, calibration workflows, design storm generation, rainfall analysis, SCS Type II distributions, AEP events, 100-year storms, or generating precipitation boundary conditions for rain-on-grid models. Triggers: precipitation, AORC, Atlas 14, design storm, rainfall, SCS Type II, AEP, 100-year, rain-on-grid, hyetograph, temporal distribution, areal reduction, calibration, historical precipitation.

scvi-tools

26

lifangda

This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.

convergence-study

22

HeshamFS

Spatial and temporal convergence analysis with Richardson extrapolation and Grid Convergence Index (GCI) for solution verification

frontend-design

16

JNLei

Create distinctive, production-grade frontend interfaces with exceptional design quality. Two modes - (1) New projects - bold aesthetic design philosophy including typography, color theory, spatial composition, motion design, visual details, avoiding generic AI aesthetics, creating unforgettable interfaces. (2) Existing codebases - mandatory design language analysis enforcing consistency by scanning layout patterns, typography hierarchy, component structure, spacing, theme systems before implementation. Use when building components, pages, applications, design systems, UI modifications. Covers React, Vue, Next.js, HTML/CSS, Tailwind. Keywords - create component, build page, design interface, add UI, aesthetic design, visual design, typography, animations, spatial layout, design system, consistency, pattern analysis, existing codebase.

histolab

15

Oimiragieo Agent Studio Histolab

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

geopandas

15

oimiragieo

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

geostatspy

13

SteadfastAsArt

GSLIB-inspired geostatistics library for variogram analysis, kriging, and simulation. Use when Claude needs to: (1) Calculate experimental variograms, (2) Fit variogram models, (3) Perform simple/ordinary kriging, (4) Run sequential Gaussian simulation (SGSIM), (5) Apply normal score transforms, (6) Decluster spatial data, (7) Generate multiple realizations for uncertainty.

verde

13

SteadfastAsArt

Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.

scikit-gstat

13

SteadfastAsArt

Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when Claude needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.

computer-vision-expert

5

agent-skills-hub

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

computer-vision-expert

4

ngxtm

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

geospatial-open-data

3

taivop

Use Land and Spatial Board geospatial open data services for maps, cadaster/cadastral parcels, and address-linked analysis in Estonia.

geospatial-open-data

3

taivop

Use Land and Spatial Board geospatial open data services for maps, cadaster/cadastral parcels, and address-linked analysis in Estonia.

geopandas

1

iamseungpil

Python library for working with geospatial vector data including Shapefiles, GeoJSON, and GeoPackage files. Use it when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading, writing, or analyzing vector geographic data. It supports PostGIS databases, interactive maps, and integration with matplotlib, folium, and cartopy. Use it for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas and distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

computer-vision-expert

1

rootcastleco

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

histolab

1

iamseungpil

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing — tissue detection, tile extraction, and stain normalization for H&E images. Best for simple pipelines, dataset preparation, and quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

computer-vision-expert

oki3505F

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

histolab

reikiplanet

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing, tissue detection, tile extraction, and stain normalization for H&E images. Best for simple pipelines, dataset preparation, and quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines, use PathML.

geopandas

reikiplanet

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

clash-detection-analysis

jdmorag97-rgb

Detect and analyze geometric clashes between BIM elements. Identify hard clashes, soft clashes, and workflow conflicts using spatial analysis and rule-based detection.

computer-vision-expert

haniakrim21

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

computer-vision-expert

reikiplanet

SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.

reframe

rana

Geometric cognitive reframing. Apply spatial search patterns — negative space, adjacent space, orthogonal space, parallel space — to see what conventional analysis misses. Geometric metaphors map naturally to multi-dimensional reasoning and produce more precise results than abstract descriptors.

reframe

rana

Geometric cognitive reframing. Apply spatial search patterns — negative space, adjacent space, orthogonal space, parallel space — to see what conventional analysis misses. Geometric metaphors map naturally to multi-dimensional reasoning and produce more precise results than abstract descriptors.