[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QEPJCJXJYV7X4P8HZVG1JY":3,"skill-same:01M6QEPJCJXJYV7X4P8HZVG1JY":138},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":14,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6QEPJCJXJYV7X4P8HZVG1JY","geomaster",null,"Provides geospatial and Earth observation workflows using GeoPandas, Rasterio, GDAL, Xarray, Shapely, Laspy, PDAL, Google Earth Engine, and STAC\u002FPlanetary Computer, covering Sentinel, Landsat, MODIS, SAR and hyperspectral imagery, spectral indices, terrain and network analysis, point clouds, COGs, CRS handling, and spatial ML, with code in Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use when computing NDVI or other indices from satellite imagery, when running vector overlays, reprojection, or spatial statistics on shapefiles, GeoJSON, or GeoPackage data, when searching STAC catalogs and reading cloud-optimized GeoTIFFs, when classifying land cover or training ML models on Earth observation data, or when doing terrain, hydrology, or point cloud analysis. Not for general non-spatial data analysis or tabular ML.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-geomaster\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"c++","for","overlays","sar","reading","remote","hydrology","spatial statistics","Julia","data analysis","general","use","engine","vector","training","workflows","python","go","google","2026-11-10 10:12:30",false,"---\nname: geomaster\ndescription: Provides geospatial and Earth observation workflows using GeoPandas, Rasterio, GDAL, Xarray, Shapely, Laspy, PDAL, Google Earth Engine, and STAC\u002FPlanetary Computer, covering Sentinel, Landsat, MODIS, SAR and hyperspectral imagery, spectral indices, terrain and network analysis, point clouds, COGs, CRS handling, and spatial ML, with code in Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use when computing NDVI or other indices from satellite imagery, when running vector overlays, reprojection, or spatial statistics on shapefiles, GeoJSON, or GeoPackage data, when searching STAC catalogs and reading cloud-optimized GeoTIFFs, when classifying land cover or training ML models on Earth observation data, or when doing terrain, hydrology, or point cloud analysis. Not for general non-spatial data analysis or tabular ML.\nlicense: MIT\nmetadata:\n  version: '1.3'\n  category: physical-sciences\n  maintainer: Kalaris Labs\n  tags: geospatial, remote sensing, Earth observation, satellite imagery, GIS\n---\n\n# GeoMaster\n\nComprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages.\n\n## Installation\n\n```bash\n# Core Python stack (conda recommended)\nconda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas\n\n# Remote sensing & ML (rsgislib is conda-forge only, not on PyPI)\nconda install -c conda-forge rsgislib\nuv pip install torchgeo earthengine-api\nuv pip install scikit-learn xgboost torch-geometric\n\n# Network & visualization\nuv pip install osmnx networkx folium keplergl\nuv pip install cartopy contextily mapclassify\n\n# Big data & cloud\nuv pip install xarray rioxarray dask-geopandas\nuv pip install pystac-client planetary-computer\n\n# Point clouds\nuv pip install laspy pylas open3d pdal\n\n# Databases\nconda install -c conda-forge postgis spatialite\n```\n\n## Quick Start\n\n### NDVI from Sentinel-2\n\n```python\nimport rasterio\nimport numpy as np\n\nwith rasterio.open('sentinel2.tif') as src:\n    red = src.read(4).astype(float)   # B04\n    nir = src.read(8).astype(float)   # B08\n    ndvi = (nir - red) \u002F (nir + red + 1e-8)\n    ndvi = np.nan_to_num(ndvi, nan=0)\n\n    profile = src.profile\n    profile.update(count=1, dtype=rasterio.float32)\n\n    with rasterio.open('ndvi.tif', 'w', **profile) as dst:\n        dst.write(ndvi.astype(rasterio.float32), 1)\n```\n\n### Spatial Analysis with GeoPandas\n\n```python\nimport geopandas as gpd\n\n# Load and ensure same CRS\nzones = gpd.read_file('zones.geojson')\npoints = gpd.read_file('points.geojson')\n\nif zones.crs != points.crs:\n    points = points.to_crs(zones.crs)\n\n# Spatial join and statistics\njoined = gpd.sjoin(points, zones, how='inner', predicate='within')\nstats = joined.groupby('zone_id').agg({\n    'value': ['count', 'mean', 'std', 'min', 'max']\n}).round(2)\n```\n\n### Google Earth Engine Time Series\n\n```python\nimport ee\nimport pandas as pd\n\nee.Initialize(project='your-project')\nroi = ee.Geometry.Point([-122.4, 37.7]).buffer(10000)\n\ns2 = (ee.ImageCollection('COPERNICUS\u002FS2_SR_HARMONIZED')\n      .filterBounds(roi)\n      .filterDate('2020-01-01', '2023-12-31')\n      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))\n\ndef add_ndvi(img):\n    return img.addBands(img.normalizedDifference(['B8', 'B4']).rename('NDVI'))\n\ns2_ndvi = s2.map(add_ndvi)\n\ndef extract_series(image):\n    stats = image.reduceRegion(ee.Reducer.mean(), roi.centroid(), scale=10, maxPixels=1e9)\n    return ee.Feature(None, {'date': image.date().format('YYYY-MM-dd'), 'ndvi': stats.get('NDVI')})\n\nseries = s2_ndvi.map(extract_series).getInfo()\ndf = pd.DataFrame([f['properties'] for f in series['features']])\ndf['date'] = pd.to_datetime(df['date'])\n```\n\n## Core Concepts\n\n### Data Types\n\n| Type | Examples | Libraries |\n|------|----------|-----------|\n| Vector | Shapefile, GeoJSON, GeoPackage | GeoPandas, Fiona, GDAL |\n| Raster | GeoTIFF, NetCDF, COG | Rasterio, Xarray, GDAL |\n| Point Cloud | LAS, LAZ | Laspy, PDAL, Open3D |\n\n### Coordinate Systems\n\n- **EPSG:4326** (WGS 84) - Geographic, lat\u002Flon, use for storage\n- **EPSG:3857** (Web Mercator) - Web maps only (don't use for area\u002Fdistance!)\n- **EPSG:326xx\u002F327xx** (UTM) - Metric calculations, \u003C1% distortion per zone\n- Use `gdf.estimate_utm_crs()` for automatic UTM detection\n\n```python\n# Always check CRS before operations\nassert gdf1.crs == gdf2.crs, \"CRS mismatch!\"\n\n# For area\u002Fdistance calculations, use projected CRS\ngdf_metric = gdf.to_crs(gdf.estimate_utm_crs())\narea_sqm = gdf_metric.geometry.area\n```\n\n### OGC Standards\n\n- **WMS**: Web Map Service - raster maps\n- **WFS**: Web Feature Service - vector data\n- **WCS**: Web Coverage Service - raster coverage\n- **STAC**: Spatiotemporal Asset Catalog - modern metadata\n\n## Common Operations\n\n### Spectral Indices\n\n```python\ndef calculate_indices(image_path):\n    \"\"\"NDVI, EVI, SAVI, NDWI from Sentinel-2.\"\"\"\n    with rasterio.open(image_path) as src:\n        B02, B03, B04, B08, B11 = [src.read(i).astype(float) for i in [1,2,3,4,5]]\n\n    ndvi = (B08 - B04) \u002F (B08 + B04 + 1e-8)\n    evi = 2.5 * (B08 - B04) \u002F (B08 + 6*B04 - 7.5*B02 + 1)\n    savi = ((B08 - B04) \u002F (B08 + B04 + 0.5)) * 1.5\n    ndwi = (B03 - B08) \u002F (B03 + B08 + 1e-8)\n\n    return {'NDVI': ndvi, 'EVI': evi, 'SAVI': savi, 'NDWI': ndwi}\n```\n\n### Vector Operations\n\n```python\n# Buffer (use projected CRS!)\ngdf_proj = gdf.to_crs(gdf.estimate_utm_crs())\ngdf['buffer_1km'] = gdf_proj.geometry.buffer(1000)\n\n# Spatial relationships\nintersects = gdf[gdf.geometry.intersects(other_geometry)]\ncontains = gdf[gdf.geometry.contains(point_geometry)]\n\n# Geometric operations\ngdf['centroid'] = gdf.geometry.centroid\ngdf['simplified'] = gdf.geometry.simplify(tolerance=0.001)\n\n# Overlay operations\nintersection = gpd.overlay(gdf1, gdf2, how='intersection')\nunion = gpd.overlay(gdf1, gdf2, how='union')\n```\n\n### Terrain Analysis\n\n```python\ndef terrain_metrics(dem_path):\n    \"\"\"Calculate slope, aspect, hillshade from DEM.\"\"\"\n    with rasterio.open(dem_path) as src:\n        dem = src.read(1)\n\n    dy, dx = np.gradient(dem)\n    slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 \u002F np.pi\n    aspect = (90 - np.arctan2(-dy, dx) * 180 \u002F np.pi) % 360\n\n    # Hillshade\n    az_rad, alt_rad = np.radians(315), np.radians(45)\n    hillshade = (np.sin(alt_rad) * np.sin(np.radians(slope)) +\n                 np.cos(alt_rad) * np.cos(np.radians(slope)) *\n                 np.cos(np.radians(aspect) - az_rad))\n\n    return slope, aspect, hillshade\n```\n\n### Network Analysis\n\n```python\nimport osmnx as ox\nimport networkx as nx\n\n# Download and analyze street network\nG = ox.graph_from_place('San Francisco, CA', network_type='drive')\nG = ox.add_edge_speeds(G).add_edge_travel_times(G)\n\n# Shortest path\norig = ox.distance.nearest_nodes(G, -122.4, 37.7)\ndest = ox.distance.nearest_nodes(G, -122.3, 37.8)\nroute = nx.shortest_path(G, orig, dest, weight='travel_time')\n```\n\n## Image Classification\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nimport rasterio\nfrom rasterio.features import rasterize\n\ndef classify_imagery(raster_path, training_gdf, output_path):\n    \"\"\"Train RF and classify imagery.\"\"\"\n    with rasterio.open(raster_path) as src:\n        image = src.read()\n        profile = src.profile\n        transform = src.transform\n\n    # Extract training data\n    X_train, y_train = [], []\n    for _, row in training_gdf.iterrows():\n        mask = rasterize([(row.geometry, 1)],\n                        out_shape=(profile['height'], profile['width']),\n                        transform=transform, fill=0, dtype=np.uint8)\n        pixels = image[:, mask > 0].T\n        X_train.extend(pixels)\n        y_train.extend([row['class_id']] * len(pixels))\n\n    # Train and predict\n    rf = RandomForestClassifier(n_estimators=100, max_depth=20, n_jobs=-1)\n    rf.fit(X_train, y_train)\n\n    prediction = rf.predict(image.reshape(image.shape[0], -1).T)\n    prediction = prediction.reshape(profile['height'], profile['width'])\n\n    profile.update(dtype=rasterio.uint8, count=1)\n    with rasterio.open(output_path, 'w', **profile) as dst:\n        dst.write(prediction.astype(rasterio.uint8), 1)\n\n    return rf\n```\n\n## Modern Cloud-Native Workflows\n\n### STAC + Planetary Computer\n\n```python\nimport pystac_client\nimport planetary_computer\nimport odc.stac\n\n# Search Sentinel-2 via STAC\ncatalog = pystac_client.Client.open(\n    \"https:\u002F\u002Fplanetarycomputer.microsoft.com\u002Fapi\u002Fstac\u002Fv1\",\n    modifier=planetary_computer.sign_inplace,\n)\n\nsearch = catalog.search(\n    collections=[\"sentinel-2-l2a\"],\n    bbox=[-122.5, 37.7, -122.3, 37.9],\n    datetime=\"2023-01-01\u002F2023-12-31\",\n    query={\"eo:cloud_cover\": {\"lt\": 20}},\n)\n\n# Load as xarray (cloud-native!)\ndata = odc.stac.load(\n    list(search.get_items())[:5],\n    bands=[\"B02\", \"B03\", \"B04\", \"B08\"],\n    crs=\"EPSG:32610\",\n    resolution=10,\n)\n\n# Calculate NDVI on xarray\nndvi = (data.B08 - data.B04) \u002F (data.B08 + data.B04)\n```\n\n### Cloud-Optimized GeoTIFF (COG)\n\n```python\nimport rasterio\nfrom rasterio.session import AWSSession\n\n# Read COG directly from cloud (partial reads)\nsession = AWSSession(aws_access_key_id=..., aws_secret_access_key=...)\nwith rasterio.open('s3:\u002F\u002Fbucket\u002Fpath.tif', session=session) as src:\n    # Read only window of interest\n    window = ((1000, 2000), (1000, 2000))\n    subset = src.read(1, window=window)\n\n# Write COG\nwith rasterio.open('output.tif', 'w', **profile,\n                   tiled=True, blockxsize=256, blockysize=256,\n                   compress='DEFLATE', predictor=2) as dst:\n    dst.write(data)\n\n# Validate COG\nfrom rio_cogeo.cogeo import cog_validate\ncog_validate('output.tif')\n```\n\n## Performance Tips\n\n```python\n# 1. Spatial indexing (10-100x faster queries)\ngdf.sindex  # Auto-created by GeoPandas\n\n# 2. Chunk large rasters\nwith rasterio.open('large.tif') as src:\n    for i, window in src.block_windows(1):\n        block = src.read(1, window=window)\n\n# 3. Dask for big data\nimport dask.array as da\ndask_array = da.from_rasterio('large.tif', chunks=(1, 1024, 1024))\n\n# 4. Use Arrow for I\u002FO\ngdf.to_file('output.gpkg', use_arrow=True)\n\n# 5. GDAL caching\nfrom osgeo import gdal\ngdal.SetCacheMax(2**30)  # 1GB cache\n\n# 6. Parallel processing\nrf = RandomForestClassifier(n_jobs=-1)  # All cores\n```\n\n## Best Practices\n\n1. **Always check CRS** before spatial operations\n2. **Use projected CRS** for area\u002Fdistance calculations\n3. **Validate geometries**: `gdf = gdf[gdf.is_valid]`\n4. **Handle missing data**: `gdf['geometry'] = gdf['geometry'].fillna(None)`\n5. **Use efficient formats**: GeoPackage > Shapefile, Parquet for large data\n6. **Apply cloud masking** to optical imagery\n7. **Preserve lineage** for reproducible research\n8. **Use appropriate resolution** for your analysis scale\n\n## Detailed Documentation\n\n- **[Coordinate Systems](references\u002Fcoordinate-systems.md)** - CRS fundamentals, UTM, transformations\n- **[Core Libraries](references\u002Fcore-libraries.md)** - GDAL, Rasterio, GeoPandas, Shapely\n- **[Remote Sensing](references\u002Fremote-sensing.md)** - Satellite missions, spectral indices, SAR\n- **[Machine Learning](references\u002Fmachine-learning.md)** - Deep learning, CNNs, GNNs for RS\n- **[GIS Software](references\u002Fgis-software.md)** - QGIS, ArcGIS, GRASS integration\n- **[Scientific Domains](references\u002Fscientific-domains.md)** - Marine, hydrology, agriculture, forestry\n- **[Advanced GIS](references\u002Fadvanced-gis.md)** - 3D GIS, spatiotemporal, topology\n- **[Big Data](references\u002Fbig-data.md)** - Distributed processing, GPU acceleration\n- **[Industry Applications](references\u002Findustry-applications.md)** - Urban planning, disaster management\n- **[Programming Languages](references\u002Fprogramming-languages.md)** - Python, R, Julia, JS, C++, Java, Go, Rust\n- **[Data Sources](references\u002Fdata-sources.md)** - Satellite catalogs, APIs\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, error reference\n- **[Code Examples](references\u002Fcode-examples.md)** - 500+ examples\n\n---\n\n**GeoMaster covers everything from basic GIS operations to advanced remote sensing and machine learning.**\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm library versions, units, coordinate systems and data formats (FITS, CIF, HDF5, netCDF).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run on a small grid, region or subset and check physical plausibility.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check units and dimensional consistency, conservation laws or known limits, and numerical convergence.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Results violate known physical limits | Check units, boundary conditions and solver tolerances before reporting. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Report uncertainties and numerical tolerances with every quantitative result.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `geopandas`: Guidance and local audit CLIs for Python workflows using GeoPandas 1.1.4 GeoSeries and GeoDataFrame for planar vector data: CRS handling, g…\n","# GeoMaster\n\nComprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages.\n\n## Installation\n\n```bash\n# Core Python stack (conda recommended)\nconda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas\n\n# Remote sensing & ML (rsgislib is conda-forge only, not on PyPI)\nconda install -c conda-forge rsgislib\nuv pip install torchgeo earthengine-api\nuv pip install scikit-learn xgboost torch-geometric\n\n# Network & visualization\nuv pip install osmnx networkx folium keplergl\nuv pip install cartopy contextily mapclassify\n\n# Big data & cloud\nuv pip install xarray rioxarray dask-geopandas\nuv pip install pystac-client planetary-computer\n\n# Point clouds\nuv pip install laspy pylas open3d pdal\n\n# Databases\nconda install -c conda-forge postgis spatialite\n```\n\n## Quick Start\n\n### NDVI from Sentinel-2\n\n```python\nimport rasterio\nimport numpy as np\n\nwith rasterio.open('sentinel2.tif') as src:\n    red = src.read(4).astype(float)   # B04\n    nir = src.read(8).astype(float)   # B08\n    ndvi = (nir - red) \u002F (nir + red + 1e-8)\n    ndvi = np.nan_to_num(ndvi, nan=0)\n\n    profile = src.profile\n    profile.update(count=1, dtype=rasterio.float32)\n\n    with rasterio.open('ndvi.tif', 'w', **profile) as dst:\n        dst.write(ndvi.astype(rasterio.float32), 1)\n```\n\n### Spatial Analysis with GeoPandas\n\n```python\nimport geopandas as gpd\n\n# Load and ensure same CRS\nzones = gpd.read_file('zones.geojson')\npoints = gpd.read_file('points.geojson')\n\nif zones.crs != points.crs:\n    points = points.to_crs(zones.crs)\n\n# Spatial join and statistics\njoined = gpd.sjoin(points, zones, how='inner', predicate='within')\nstats = joined.groupby('zone_id').agg({\n    'value': ['count', 'mean', 'std', 'min', 'max']\n}).round(2)\n```\n\n### Google Earth Engine Time Series\n\n```python\nimport ee\nimport pandas as pd\n\nee.Initialize(project='your-project')\nroi = ee.Geometry.Point([-122.4, 37.7]).buffer(10000)\n\ns2 = (ee.ImageCollection('COPERNICUS\u002FS2_SR_HARMONIZED')\n      .filterBounds(roi)\n      .filterDate('2020-01-01', '2023-12-31')\n      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))\n\ndef add_ndvi(img):\n    return img.addBands(img.normalizedDifference(['B8', 'B4']).rename('NDVI'))\n\ns2_ndvi = s2.map(add_ndvi)\n\ndef extract_series(image):\n    stats = image.reduceRegion(ee.Reducer.mean(), roi.centroid(), scale=10, maxPixels=1e9)\n    return ee.Feature(None, {'date': image.date().format('YYYY-MM-dd'), 'ndvi': stats.get('NDVI')})\n\nseries = s2_ndvi.map(extract_series).getInfo()\ndf = pd.DataFrame([f['properties'] for f in series['features']])\ndf['date'] = pd.to_datetime(df['date'])\n```\n\n## Core Concepts\n\n### Data Types\n\n| Type | Examples | Libraries |\n|------|----------|-----------|\n| Vector | Shapefile, GeoJSON, GeoPackage | GeoPandas, Fiona, GDAL |\n| Raster | GeoTIFF, NetCDF, COG | Rasterio, Xarray, GDAL |\n| Point Cloud | LAS, LAZ | Laspy, PDAL, Open3D |\n\n### Coordinate Systems\n\n- **EPSG:4326** (WGS 84) - Geographic, lat\u002Flon, use for storage\n- **EPSG:3857** (Web Mercator) - Web maps only (don't use for area\u002Fdistance!)\n- **EPSG:326xx\u002F327xx** (UTM) - Metric calculations, \u003C1% distortion per zone\n- Use `gdf.estimate_utm_crs()` for automatic UTM detection\n\n```python\n# Always check CRS before operations\nassert gdf1.crs == gdf2.crs, \"CRS mismatch!\"\n\n# For area\u002Fdistance calculations, use projected CRS\ngdf_metric = gdf.to_crs(gdf.estimate_utm_crs())\narea_sqm = gdf_metric.geometry.area\n```\n\n### OGC Standards\n\n- **WMS**: Web Map Service - raster maps\n- **WFS**: Web Feature Service - vector data\n- **WCS**: Web Coverage Service - raster coverage\n- **STAC**: Spatiotemporal Asset Catalog - modern metadata\n\n## Common Operations\n\n### Spectral Indices\n\n```python\ndef calculate_indices(image_path):\n    \"\"\"NDVI, EVI, SAVI, NDWI from Sentinel-2.\"\"\"\n    with rasterio.open(image_path) as src:\n        B02, B03, B04, B08, B11 = [src.read(i).astype(float) for i in [1,2,3,4,5]]\n\n    ndvi = (B08 - B04) \u002F (B08 + B04 + 1e-8)\n    evi = 2.5 * (B08 - B04) \u002F (B08 + 6*B04 - 7.5*B02 + 1)\n    savi = ((B08 - B04) \u002F (B08 + B04 + 0.5)) * 1.5\n    ndwi = (B03 - B08) \u002F (B03 + B08 + 1e-8)\n\n    return {'NDVI': ndvi, 'EVI': evi, 'SAVI': savi, 'NDWI': ndwi}\n```\n\n### Vector Operations\n\n```python\n# Buffer (use projected CRS!)\ngdf_proj = gdf.to_crs(gdf.estimate_utm_crs())\ngdf['buffer_1km'] = gdf_proj.geometry.buffer(1000)\n\n# Spatial relationships\nintersects = gdf[gdf.geometry.intersects(other_geometry)]\ncontains = gdf[gdf.geometry.contains(point_geometry)]\n\n# Geometric operations\ngdf['centroid'] = gdf.geometry.centroid\ngdf['simplified'] = gdf.geometry.simplify(tolerance=0.001)\n\n# Overlay operations\nintersection = gpd.overlay(gdf1, gdf2, how='intersection')\nunion = gpd.overlay(gdf1, gdf2, how='union')\n```\n\n### Terrain Analysis\n\n```python\ndef terrain_metrics(dem_path):\n    \"\"\"Calculate slope, aspect, hillshade from DEM.\"\"\"\n    with rasterio.open(dem_path) as src:\n        dem = src.read(1)\n\n    dy, dx = np.gradient(dem)\n    slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 \u002F np.pi\n    aspect = (90 - np.arctan2(-dy, dx) * 180 \u002F np.pi) % 360\n\n    # Hillshade\n    az_rad, alt_rad = np.radians(315), np.radians(45)\n    hillshade = (np.sin(alt_rad) * np.sin(np.radians(slope)) +\n                 np.cos(alt_rad) * np.cos(np.radians(slope)) *\n                 np.cos(np.radians(aspect) - az_rad))\n\n    return slope, aspect, hillshade\n```\n\n### Network Analysis\n\n```python\nimport osmnx as ox\nimport networkx as nx\n\n# Download and analyze street network\nG = ox.graph_from_place('San Francisco, CA', network_type='drive')\nG = ox.add_edge_speeds(G).add_edge_travel_times(G)\n\n# Shortest path\norig = ox.distance.nearest_nodes(G, -122.4, 37.7)\ndest = ox.distance.nearest_nodes(G, -122.3, 37.8)\nroute = nx.shortest_path(G, orig, dest, weight='travel_time')\n```\n\n## Image Classification\n\n```python\nfrom sklearn.ensemble import RandomForestClassifier\nimport rasterio\nfrom rasterio.features import rasterize\n\ndef classify_imagery(raster_path, training_gdf, output_path):\n    \"\"\"Train RF and classify imagery.\"\"\"\n    with rasterio.open(raster_path) as src:\n        image = src.read()\n        profile = src.profile\n        transform = src.transform\n\n    # Extract training data\n    X_train, y_train = [], []\n    for _, row in training_gdf.iterrows():\n        mask = rasterize([(row.geometry, 1)],\n                        out_shape=(profile['height'], profile['width']),\n                        transform=transform, fill=0, dtype=np.uint8)\n        pixels = image[:, mask > 0].T\n        X_train.extend(pixels)\n        y_train.extend([row['class_id']] * len(pixels))\n\n    # Train and predict\n    rf = RandomForestClassifier(n_estimators=100, max_depth=20, n_jobs=-1)\n    rf.fit(X_train, y_train)\n\n    prediction = rf.predict(image.reshape(image.shape[0], -1).T)\n    prediction = prediction.reshape(profile['height'], profile['width'])\n\n    profile.update(dtype=rasterio.uint8, count=1)\n    with rasterio.open(output_path, 'w', **profile) as dst:\n        dst.write(prediction.astype(rasterio.uint8), 1)\n\n    return rf\n```\n\n## Modern Cloud-Native Workflows\n\n### STAC + Planetary Computer\n\n```python\nimport pystac_client\nimport planetary_computer\nimport odc.stac\n\n# Search Sentinel-2 via STAC\ncatalog = pystac_client.Client.open(\n    \"https:\u002F\u002Fplanetarycomputer.microsoft.com\u002Fapi\u002Fstac\u002Fv1\",\n    modifier=planetary_computer.sign_inplace,\n)\n\nsearch = catalog.search(\n    collections=[\"sentinel-2-l2a\"],\n    bbox=[-122.5, 37.7, -122.3, 37.9],\n    datetime=\"2023-01-01\u002F2023-12-31\",\n    query={\"eo:cloud_cover\": {\"lt\": 20}},\n)\n\n# Load as xarray (cloud-native!)\ndata = odc.stac.load(\n    list(search.get_items())[:5],\n    bands=[\"B02\", \"B03\", \"B04\", \"B08\"],\n    crs=\"EPSG:32610\",\n    resolution=10,\n)\n\n# Calculate NDVI on xarray\nndvi = (data.B08 - data.B04) \u002F (data.B08 + data.B04)\n```\n\n### Cloud-Optimized GeoTIFF (COG)\n\n```python\nimport rasterio\nfrom rasterio.session import AWSSession\n\n# Read COG directly from cloud (partial reads)\nsession = AWSSession(aws_access_key_id=..., aws_secret_access_key=...)\nwith rasterio.open('s3:\u002F\u002Fbucket\u002Fpath.tif', session=session) as src:\n    # Read only window of interest\n    window = ((1000, 2000), (1000, 2000))\n    subset = src.read(1, window=window)\n\n# Write COG\nwith rasterio.open('output.tif', 'w', **profile,\n                   tiled=True, blockxsize=256, blockysize=256,\n                   compress='DEFLATE', predictor=2) as dst:\n    dst.write(data)\n\n# Validate COG\nfrom rio_cogeo.cogeo import cog_validate\ncog_validate('output.tif')\n```\n\n## Performance Tips\n\n```python\n# 1. Spatial indexing (10-100x faster queries)\ngdf.sindex  # Auto-created by GeoPandas\n\n# 2. Chunk large rasters\nwith rasterio.open('large.tif') as src:\n    for i, window in src.block_windows(1):\n        block = src.read(1, window=window)\n\n# 3. Dask for big data\nimport dask.array as da\ndask_array = da.from_rasterio('large.tif', chunks=(1, 1024, 1024))\n\n# 4. Use Arrow for I\u002FO\ngdf.to_file('output.gpkg', use_arrow=True)\n\n# 5. GDAL caching\nfrom osgeo import gdal\ngdal.SetCacheMax(2**30)  # 1GB cache\n\n# 6. Parallel processing\nrf = RandomForestClassifier(n_jobs=-1)  # All cores\n```\n\n## Best Practices\n\n1. **Always check CRS** before spatial operations\n2. **Use projected CRS** for area\u002Fdistance calculations\n3. **Validate geometries**: `gdf = gdf[gdf.is_valid]`\n4. **Handle missing data**: `gdf['geometry'] = gdf['geometry'].fillna(None)`\n5. **Use efficient formats**: GeoPackage > Shapefile, Parquet for large data\n6. **Apply cloud masking** to optical imagery\n7. **Preserve lineage** for reproducible research\n8. **Use appropriate resolution** for your analysis scale\n\n## Detailed Documentation\n\n- **[Coordinate Systems](references\u002Fcoordinate-systems.md)** - CRS fundamentals, UTM, transformations\n- **[Core Libraries](references\u002Fcore-libraries.md)** - GDAL, Rasterio, GeoPandas, Shapely\n- **[Remote Sensing](references\u002Fremote-sensing.md)** - Satellite missions, spectral indices, SAR\n- **[Machine Learning](references\u002Fmachine-learning.md)** - Deep learning, CNNs, GNNs for RS\n- **[GIS Software](references\u002Fgis-software.md)** - QGIS, ArcGIS, GRASS integration\n- **[Scientific Domains](references\u002Fscientific-domains.md)** - Marine, hydrology, agriculture, forestry\n- **[Advanced GIS](references\u002Fadvanced-gis.md)** - 3D GIS, spatiotemporal, topology\n- **[Big Data](references\u002Fbig-data.md)** - Distributed processing, GPU acceleration\n- **[Industry Applications](references\u002Findustry-applications.md)** - Urban planning, disaster management\n- **[Programming Languages](references\u002Fprogramming-languages.md)** - Python, R, Julia, JS, C++, Java, Go, Rust\n- **[Data Sources](references\u002Fdata-sources.md)** - Satellite catalogs, APIs\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging, error reference\n- **[Code Examples](references\u002Fcode-examples.md)** - 500+ examples\n\n---\n\n**GeoMaster covers everything from basic GIS operations to advanced remote sensing and machine learning.**\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm library versions, units, coordinate systems and data formats (FITS, CIF, HDF5, netCDF).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run on a small grid, region or subset and check physical plausibility.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check units and dimensional consistency, conservation laws or known limits, and numerical convergence.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Results violate known physical limits | Check units, boundary conditions and solver tolerances before reporting. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Report uncertainties and numerical tolerances with every quantitative result.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `geopandas`: Guidance and local audit CLIs for Python workflows using GeoPandas 1.1.4 GeoSeries and GeoDataFrame for planar vector data: CRS handling, g…\n",{"name":5,"license":15,"metadata":48,"description":7},{"tags":49,"version":50,"category":51,"maintainer":52},"geospatial, remote sensing, Earth observation, satellite imagery, GIS","1.3","physical-sciences","Kalaris 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Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[145],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,25,165,166,167],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":172,"categories":173,"keywords":174,"publishedAt":43},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[175,176,177,178,153,179,180,181,182,183,184,36,185,186,35,187,188,189,190,25],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","write","strategy","platform","grow","posting","competitive",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":200,"publishedAt":43},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. 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Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[225,226,227,228,229,230,231,232,233,35,234,160,235,236,237,238,239,240,241,242],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":244,"name":245,"nameZh":6,"description":246,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":247,"categories":248,"keywords":250,"publishedAt":43},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[249],{"categoryNo":198,"categoryName":199,"taxonomyType":21,"taxonomyTypeDesc":22},[251,252,253,254,39,203,230,255,256,257,258,259,185,260,159,261,262,263,264,265],"workflow","workflow builder","builder","design","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":267,"name":268,"nameZh":6,"description":269,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":270,"categories":271,"keywords":272,"publishedAt":43},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[273,274,275,276,277,278,279,280,281,258,282,283,284,285,286,287,161,288,289,242],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]