[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDTP4T58YYN4T4XRNGWCB5":3,"skill-same:01M6KDTP4T58YYN4T4XRNGWCB5":53},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":36,"ownerHtmlUrl":37,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":38,"install":39,"resources":52},"01M6KDTP4T58YYN4T4XRNGWCB5","geopandas-distance-analysis",null,"Calculate distances between geospatial points and boundaries using GeoPandas with proper metric projections (EPSG:4087).","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fearthquake-plate-calculation\u002Fgeopandas-distance-analysis\u002FSKILL.md",[],[18,19,20,21,22,23,24,25],"analysis","geopandas","geopandas distance analysis","geospatial","calculate","projections","with","metric","2026-11-10 10:12:30",false,"---\nname: geopandas-distance-analysis\ndescription: Calculate distances between geospatial points and boundaries using GeoPandas with proper metric projections (EPSG:4087).\n---\n\n# GeoPandas Distance Analysis\n\n## Setup\n```bash\npip install geopandas shapely\n```\n\n## Core Workflow\n\n### 1. Load GeoJSON files\n```python\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\ngdf_plates = gpd.read_file(\"PB2002_plates.json\")\ngdf_boundaries = gpd.read_file(\"PB2002_boundaries.json\")\n```\n\n### 2. Create GeoDataFrame from coordinate list\n```python\ngeometry = [Point(lon, lat) for lon, lat in zip(lons, lats)]\ngdf_points = gpd.GeoDataFrame(data, geometry=geometry, crs=\"EPSG:4326\")\n```\n\n### 3. Spatial filtering (points within polygon)\n```python\ntarget_poly = gdf_plates[gdf_plates[\"Code\"] == \"PA\"].geometry.unary_union\ninside = gdf_points[gdf_points.within(target_poly)].copy()\n```\n\n### 4. Project to metric CRS before distance calculation\n```python\nMETRIC_CRS = \"EPSG:4087\"  # World Equidistant Cylindrical (meters)\ninside_proj = inside.to_crs(METRIC_CRS)\nboundary_proj = gdf_boundaries.to_crs(METRIC_CRS).geometry.unary_union\ninside[\"distance_km\"] = inside_proj.geometry.distance(boundary_proj) \u002F 1000.0\n```\n\n### 5. Find furthest point\n```python\nresult = inside.nlargest(1, \"distance_km\").iloc[0]\nprint(f\"ID: {result['id']}, Distance: {result['distance_km']:.2f} km\")\n```\n\n## Key Rules\n- **NEVER** calculate distances in EPSG:4326 (degrees != meters)\n- Always project to EPSG:4087 or EPSG:3857 before `.distance()`\n- Use `.unary_union` to combine multiple boundary segments into one geometry\n- Use `.within()` for point-in-polygon tests (handles antimeridian better than manual checks)\n- Filter boundaries by PlateA\u002FPlateB before combining: `gdf_boundaries[(gdf_boundaries[\"PlateA\"]==\"PA\") | (gdf_boundaries[\"PlateB\"]==\"PA\")]`\n","# GeoPandas Distance Analysis\n\n## Setup\n```bash\npip install geopandas shapely\n```\n\n## Core Workflow\n\n### 1. Load GeoJSON files\n```python\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\ngdf_plates = gpd.read_file(\"PB2002_plates.json\")\ngdf_boundaries = gpd.read_file(\"PB2002_boundaries.json\")\n```\n\n### 2. Create GeoDataFrame from coordinate list\n```python\ngeometry = [Point(lon, lat) for lon, lat in zip(lons, lats)]\ngdf_points = gpd.GeoDataFrame(data, geometry=geometry, crs=\"EPSG:4326\")\n```\n\n### 3. Spatial filtering (points within polygon)\n```python\ntarget_poly = gdf_plates[gdf_plates[\"Code\"] == \"PA\"].geometry.unary_union\ninside = gdf_points[gdf_points.within(target_poly)].copy()\n```\n\n### 4. Project to metric CRS before distance calculation\n```python\nMETRIC_CRS = \"EPSG:4087\"  # World Equidistant Cylindrical (meters)\ninside_proj = inside.to_crs(METRIC_CRS)\nboundary_proj = gdf_boundaries.to_crs(METRIC_CRS).geometry.unary_union\ninside[\"distance_km\"] = inside_proj.geometry.distance(boundary_proj) \u002F 1000.0\n```\n\n### 5. Find furthest point\n```python\nresult = inside.nlargest(1, \"distance_km\").iloc[0]\nprint(f\"ID: {result['id']}, Distance: {result['distance_km']:.2f} km\")\n```\n\n## Key Rules\n- **NEVER** calculate distances in EPSG:4326 (degrees != meters)\n- Always project to EPSG:4087 or EPSG:3857 before `.distance()`\n- Use `.unary_union` to combine multiple boundary segments into one geometry\n- Use `.within()` for point-in-polygon tests (handles antimeridian better than manual checks)\n- Filter boundaries by PlateA\u002FPlateB before combining: `gdf_boundaries[(gdf_boundaries[\"PlateA\"]==\"PA\") | (gdf_boundaries[\"PlateB\"]==\"PA\")]`\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fearthquake-plate-calculation\u002Fgeopandas-distance-analysis",8,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill geopandas-distance-analysis",{"tool":45,"command":46},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill geopandas-distance-analysis",{"tool":48,"command":49},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill geopandas-distance-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fearthquake-plate-calculation\u002Fgeopandas-distance-analysis\u002FSKILL.md",true,[],[54,72,97,115,135,146],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":65,"publishedAt":26},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[60],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[66,67,68,69,70,71],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":81,"publishedAt":26},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000023","Skill与智能体开发",[82,83,84,85,86,87,88,89,90,91,92,93,94,95,96],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":26},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[104,105,106,107,108,109,110,85,111,112,86,113,91,114,83],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":26},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[122,123,124,91,125,126,127,128,129,130,131,132,113,133,134],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":26},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[141],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},[143,134,124,123,129,128,125,144,145,91,25],"glm calibration","setup","companion",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":26},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[153,124,128,154,143,134,91,123,155,156,157,158,159,160,125],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]