[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KG7EKYTCPEPFCS4XVDS2T7":3,"skill-same:01M6KG7EKYTCPEPFCS4XVDS2T7":61},{"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":22,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":42,"repositoryHtmlUrl":43,"repositoryHomepageUrl":44,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M6KG7EKYTCPEPFCS4XVDS2T7","run2_geospatial-analysis",null,"Geospatial data analysis with geopandas for distance calculations, filtering, and modern API usage.","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\u002Fb2-self-feedback-gemini-3.1-flash-lite-preview\u002Fearthquake-plate-calculation\u002Frun2_geospatial-analysis\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33],"geospatial","analysis","run2 geospatial analysis","data analysis","data","geopandas","api","filtering","with","usage","for","2026-11-10 10:12:30",false,"---\nname: run2_geospatial-analysis\ndescription: Geospatial data analysis with geopandas for distance calculations, filtering, and modern API usage.\n---\n# Geospatial Analysis with GeoPandas (Improved)\n\n## Key Concepts\n- **Coordinate Systems**: EPSG:4326 for storage, EPSG:4087 (metric) for distance.\n- **Modern API**: As of GeoPandas 1.0+, use `unary_union` (or `union_all()` for newer versions) to combine geometries.\n- **Data Conversion**: When using raw GeoJSON, extract `properties` and `geometry.coordinates` manually before GeoDataFrame creation.\n\n## Updated Pattern\n```python\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\n# 1. Load data\n# 2. Extract components if necessary\n# 3. Project to metric CRS\nMETRIC_CRS = \"EPSG:4087\"\npoints_proj = gdf.to_crs(METRIC_CRS)\n# 4. Combine geometries using .union_all()\nboundary_geom = boundary_gdf.to_crs(METRIC_CRS).geometry.union_all()\n# 5. Calculate distance\ngdf[\"dist\"] = points_proj.geometry.distance(boundary_geom)\n```\n","# Geospatial Analysis with GeoPandas (Improved)\n\n## Key Concepts\n- **Coordinate Systems**: EPSG:4326 for storage, EPSG:4087 (metric) for distance.\n- **Modern API**: As of GeoPandas 1.0+, use `unary_union` (or `union_all()` for newer versions) to combine geometries.\n- **Data Conversion**: When using raw GeoJSON, extract `properties` and `geometry.coordinates` manually before GeoDataFrame creation.\n\n## Updated Pattern\n```python\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\n# 1. Load data\n# 2. Extract components if necessary\n# 3. Project to metric CRS\nMETRIC_CRS = \"EPSG:4087\"\npoints_proj = gdf.to_crs(METRIC_CRS)\n# 4. Combine geometries using .union_all()\nboundary_geom = boundary_gdf.to_crs(METRIC_CRS).geometry.union_all()\n# 5. Calculate distance\ngdf[\"dist\"] = points_proj.geometry.distance(boundary_geom)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-flash-lite-preview\u002Fearthquake-plate-calculation\u002Frun2_geospatial-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":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_geospatial-analysis",{"tool":53,"command":54},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_geospatial-analysis",{"tool":56,"command":57},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_geospatial-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-flash-lite-preview\u002Fearthquake-plate-calculation\u002Frun2_geospatial-analysis\u002FSKILL.md",true,[],[62,78,102,120,140,152],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":71,"publishedAt":34},"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",[68],{"categoryNo":69,"categoryName":70,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[72,73,74,75,76,77],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":87,"publishedAt":34},"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",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[88,89,90,91,92,93,94,95,96,33,97,98,99,100,101],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":34},"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",[],[109,110,111,112,113,114,115,91,116,117,92,118,33,119,89],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":34},"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",[],[127,128,129,33,130,131,132,133,134,135,136,137,118,138,139],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":34},"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",[146],{"categoryNo":85,"categoryName":86,"taxonomyType":20,"taxonomyTypeDesc":21},[148,139,129,128,134,133,130,149,150,33,151],"glm calibration","setup","companion","metric",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":34},"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",[],[159,129,133,160,148,139,33,128,161,162,163,164,165,166,130],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]