[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGDMHNQ77V35ESSRWHK6R4":3,"skill-same:01M6KGDMHNQ77V35ESSRWHK6R4":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},"01M6KGDMHNQ77V35ESSRWHK6R4","run2_geopandas_spatial_joins",null,"Improved guide for performing spatial joins with GeoPandas, specifically handling antimeridian crossing polygons like the Pacific Plate.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fearthquake-plate-calculation\u002Frun2_geopandas_spatial_joins\u002FSKILL.md",[],[18,19,20,21,22,23,24,25],"geopandas","Spatial","spatial joins","run2 geopandas spatial joins","guide","with","for","performing","2026-11-10 10:12:30",false,"---\nname: run2_geopandas_spatial_joins\ndescription: Improved guide for performing spatial joins with GeoPandas, specifically handling antimeridian crossing polygons like the Pacific Plate.\n---\n\n# Spatial Joins and Antimeridian in GeoPandas\n\nGeoPandas provides `sjoin` to join points and polygons based on their spatial relationship.\n\n## Pacific Plate and the Antimeridian\nThe Pacific Plate spans across the 180° meridian (antimeridian). Standard WGS84 (EPSG:4326) coordinates wrap around this line.\nHigh-quality datasets like PB2002 split tectonic plates that cross the antimeridian into `MultiPolygon` geometries. This means a standard spatial join using Euclidean intersection rules on EPSG:4326 will correctly identify points within the plate without false positives connecting -180 and 180 through Greenwich.\n\n## Usage\n\n```python\nimport geopandas as gpd\n\n# Load points and polygons\npoints = gpd.read_file('points.geojson')\npolygons = gpd.read_file('polygons.geojson')\n\n# Ensure matching CRS before spatial join\nif points.crs != polygons.crs:\n    points = points.to_crs(polygons.crs)\n\n# Filter polygons (e.g. Code == 'PA' for Pacific Plate)\npacific_plate = polygons[polygons['Code'] == 'PA']\n\n# Find points within the polygon\npoints_in_poly = gpd.sjoin(points, pacific_plate, predicate='within')\n\nprint(f\"Points inside PA plate: {len(points_in_poly)}\")\n```\n","# Spatial Joins and Antimeridian in GeoPandas\n\nGeoPandas provides `sjoin` to join points and polygons based on their spatial relationship.\n\n## Pacific Plate and the Antimeridian\nThe Pacific Plate spans across the 180° meridian (antimeridian). Standard WGS84 (EPSG:4326) coordinates wrap around this line.\nHigh-quality datasets like PB2002 split tectonic plates that cross the antimeridian into `MultiPolygon` geometries. This means a standard spatial join using Euclidean intersection rules on EPSG:4326 will correctly identify points within the plate without false positives connecting -180 and 180 through Greenwich.\n\n## Usage\n\n```python\nimport geopandas as gpd\n\n# Load points and polygons\npoints = gpd.read_file('points.geojson')\npolygons = gpd.read_file('polygons.geojson')\n\n# Ensure matching CRS before spatial join\nif points.crs != polygons.crs:\n    points = points.to_crs(polygons.crs)\n\n# Filter polygons (e.g. Code == 'PA' for Pacific Plate)\npacific_plate = polygons[polygons['Code'] == 'PA']\n\n# Find points within the polygon\npoints_in_poly = gpd.sjoin(points, pacific_plate, predicate='within')\n\nprint(f\"Points inside PA plate: {len(points_in_poly)}\")\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fearthquake-plate-calculation\u002Frun2_geopandas_spatial_joins",7,"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 run2_geopandas_spatial_joins",{"tool":45,"command":46},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_geopandas_spatial_joins",{"tool":48,"command":49},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_geopandas_spatial_joins","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fearthquake-plate-calculation\u002Frun2_geopandas_spatial_joins\u002FSKILL.md",true,[],[54,72,95,113,133,145],{"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",[82,83,84,85,86,87,88,25,89,24,90,91,92,93,94],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","plan","user","it","requirements","essential","itineraries",{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"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",[],[102,103,104,105,106,107,108,85,109,110,86,111,24,112,83],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"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",[],[120,121,122,24,123,124,125,126,127,128,129,130,111,131,132],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"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",[139],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},[141,132,122,121,127,126,123,142,143,24,144],"glm calibration","setup","companion","metric",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"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",[],[152,122,126,153,141,132,24,121,154,155,156,157,158,159,123],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]