[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KEHAZY5JHNETK8PYZ0ARE1":3,"skill-same:01M6KEHAZY5JHNETK8PYZ0ARE1":52},{"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":25,"linkOnly":26,"markdownContent":27,"markdownBody":28,"frontMatter":29,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":35,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":37,"install":38,"resources":51},"01M6KEHAZY5JHNETK8PYZ0ARE1","spatial-filtering",null,"Methods for filtering GeoDataFrames based on spatial relationships like containment and intersection.","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\u002Fb1-one-shot-gemini-3-flash-preview\u002Fearthquake-plate-calculation\u002Fspatial-filtering\u002FSKILL.md",[],[18,19,20,21,22,23,24],"filtering","Spatial","spatial filtering","containment","methods","relationships","for","2026-11-10 10:12:30",false,"---\nname: spatial-filtering\ndescription: Methods for filtering GeoDataFrames based on spatial relationships like containment and intersection.\n---\n\n# Spatial Filtering\n\n## Overview\nSpatial filtering allows you to select features based on their location relative to other features.\n\n## Point-in-Polygon\nTo find points that lie inside a specific polygon:\n\n```python\n# Assuming gdf_points and gdf_polygons are GeoDataFrames\n# 1. Get a specific polygon geometry\npacific_poly = gdf_polygons[gdf_polygons['Code'] == 'PA'].geometry.unary_union\n\n# 2. Filter points\npoints_in_pacific = gdf_points[gdf_points.within(pacific_poly)]\n```\n\n## Spatial Joins\nAlternatively, use `sjoin` for batch processing:\n\n```python\njoined = gpd.sjoin(gdf_points, gdf_polygons, predicate='within')\n```\n\n## Attribute-based Spatial Selection\nOften you need to combine attribute filters with spatial operations:\n\n```python\n# Find boundaries associated with the Pacific plate (PA)\npacific_boundaries = gdf_boundaries[\n    (gdf_boundaries['PlateA'] == 'PA') | (gdf_boundaries['PlateB'] == 'PA')\n]\n```\n","# Spatial Filtering\n\n## Overview\nSpatial filtering allows you to select features based on their location relative to other features.\n\n## Point-in-Polygon\nTo find points that lie inside a specific polygon:\n\n```python\n# Assuming gdf_points and gdf_polygons are GeoDataFrames\n# 1. Get a specific polygon geometry\npacific_poly = gdf_polygons[gdf_polygons['Code'] == 'PA'].geometry.unary_union\n\n# 2. Filter points\npoints_in_pacific = gdf_points[gdf_points.within(pacific_poly)]\n```\n\n## Spatial Joins\nAlternatively, use `sjoin` for batch processing:\n\n```python\njoined = gpd.sjoin(gdf_points, gdf_polygons, predicate='within')\n```\n\n## Attribute-based Spatial Selection\nOften you need to combine attribute filters with spatial operations:\n\n```python\n# Find boundaries associated with the Pacific plate (PA)\npacific_boundaries = gdf_boundaries[\n    (gdf_boundaries['PlateA'] == 'PA') | (gdf_boundaries['PlateB'] == 'PA')\n]\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3-flash-preview\u002Fearthquake-plate-calculation\u002Fspatial-filtering",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":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill spatial-filtering",{"tool":44,"command":45},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill spatial-filtering",{"tool":47,"command":48},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill spatial-filtering","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3-flash-preview\u002Fearthquake-plate-calculation\u002Fspatial-filtering\u002FSKILL.md",true,[],[53,71,95,113,133,145],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":64,"publishedAt":25},"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",[59],{"categoryNo":60,"categoryName":61,"taxonomyType":62,"taxonomyTypeDesc":63},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[65,66,67,68,69,70],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":80,"publishedAt":25},"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",[77],{"categoryNo":78,"categoryName":79,"taxonomyType":62,"taxonomyTypeDesc":63},"01JBSEEDCAT000000000000023","Skill与智能体开发",[81,82,83,84,85,86,87,88,89,24,90,91,92,93,94],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","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":25},"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",[],[102,103,104,105,106,107,108,84,109,110,85,111,24,112,82],"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":25},"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":25},"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":78,"categoryName":79,"taxonomyType":62,"taxonomyTypeDesc":63},[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":25},"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"]