[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGDD1TNNE1A0ESMN3EXCCN":3,"skill-same:01M6KGDD1TNNE1A0ESMN3EXCCN":56},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":37,"repositoryHtmlUrl":38,"repositoryHomepageUrl":39,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":41,"install":42,"resources":55},"01M6KGDD1TNNE1A0ESMN3EXCCN","run2_geojson_handling",null,"Advanced handling of GeoJSON files in GeoPandas, emphasizing correct geometry formats and time processing to ISO 8601 standard.","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-pro-preview\u002Fearthquake-plate-calculation\u002Frun2_geojson_handling\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28],"geojson","run2 geojson handling","geopandas","correct","files","time","geometry","iso","processing","advanced","formats","2026-11-10 10:12:30",false,"---\nname: run2_geojson_handling\ndescription: Advanced handling of GeoJSON files in GeoPandas, emphasizing correct geometry formats and time processing to ISO 8601 standard.\n---\n\n# GeoJSON and Time Formatting\n\nGeoPandas natively reads GeoJSON files into a `GeoDataFrame`.\nProperties like Unix timestamps in JSON are often stored in milliseconds, and must be correctly formatted to ISO 8601 string representations (e.g. `YYYY-MM-DDTHH:MM:SSZ`) for external consistency.\n\n## Usage\n\n```python\nimport geopandas as gpd\nfrom datetime import datetime, timezone\n\n# Load points\ngdf = gpd.read_file('features.json')\n\n# Attributes are Pandas columns\nprint(gdf['mag'].max())\n\n# Spatial properties\nlon = gdf.geometry.x\nlat = gdf.geometry.y\n\n# Format Time to ISO 8601 (without fractional seconds)\ndef format_iso_time(ms_timestamp):\n    dt = datetime.fromtimestamp(ms_timestamp \u002F 1000.0, tz=timezone.utc)\n    return dt.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\nprint(format_iso_time(1735537742808))\n# Outputs: '2024-12-30T04:02:22Z'\n```\n","# GeoJSON and Time Formatting\n\nGeoPandas natively reads GeoJSON files into a `GeoDataFrame`.\nProperties like Unix timestamps in JSON are often stored in milliseconds, and must be correctly formatted to ISO 8601 string representations (e.g. `YYYY-MM-DDTHH:MM:SSZ`) for external consistency.\n\n## Usage\n\n```python\nimport geopandas as gpd\nfrom datetime import datetime, timezone\n\n# Load points\ngdf = gpd.read_file('features.json')\n\n# Attributes are Pandas columns\nprint(gdf['mag'].max())\n\n# Spatial properties\nlon = gdf.geometry.x\nlat = gdf.geometry.y\n\n# Format Time to ISO 8601 (without fractional seconds)\ndef format_iso_time(ms_timestamp):\n    dt = datetime.fromtimestamp(ms_timestamp \u002F 1000.0, tz=timezone.utc)\n    return dt.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n\nprint(format_iso_time(1735537742808))\n# Outputs: '2024-12-30T04:02:22Z'\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fearthquake-plate-calculation\u002Frun2_geojson_handling",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":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_geojson_handling",{"tool":48,"command":49},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_geojson_handling",{"tool":51,"command":52},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_geojson_handling","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_geojson_handling\u002FSKILL.md",true,[],[57,75,100,118,137,149],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":68,"publishedAt":29},"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",[63],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[69,70,71,72,73,74],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":29},"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",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":29},"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",[],[107,108,109,110,111,112,113,88,114,115,89,116,94,117,86],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":29},"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",[],[125,126,127,94,128,129,130,131,132,133,134,26,116,135,136],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","root","calibration",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":144,"publishedAt":29},"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",[143],{"categoryNo":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},[145,136,127,126,132,131,128,146,147,94,148],"glm calibration","setup","companion","metric",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":29},"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",[],[156,127,131,157,145,136,94,126,158,22,159,160,161,162,128],"glm basics","inspect","relevant","good","use","how","Evaluation"]