[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KG0DYCK0SV73P5THWDCGCK":3,"skill-same:01M6KG0DYCK0SV73P5THWDCGCK":49},{"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":22,"linkOnly":23,"markdownContent":24,"markdownBody":25,"frontMatter":26,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":34,"install":35,"resources":48},"01M6KG0DYCK0SV73P5THWDCGCK","run2_antimeridian_handling",null,"Techniques for handling geometries that cross the 180\u002F-180 degree longitude line.","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-flash-preview\u002Fearthquake-plate-calculation\u002Frun2_antimeridian_handling\u002FSKILL.md",[],[18,19,20,21],"run2 antimeridian handling","line","techniques","for","2026-11-10 10:12:30",false,"---\nname: run2_antimeridian_handling\ndescription: Techniques for handling geometries that cross the 180\u002F-180 degree longitude line.\n---\n\n# Handling the Anti-meridian in Geospatial Analysis\n\nWhen working with data spanning the Pacific, geometries often cross the anti-meridian. Standard projections centered at 0° longitude will \"rip\" these geometries.\n\n## Techniques\n\n### 1. Shift Coordinates to 0-360\nYou can shift the longitude of all geometries to be in the [0, 360) range.\n```python\ndef shift_lon(geom):\n    from shapely.affinity import translate\n    # This is complex for polygons that cross the line. \n    # Better to use a specific CRS.\n    pass\n```\n\n### 2. Use a Pacific-Centered CRS\nThe most robust way is to use a CRS centered on the Pacific (e.g., 150°E or 180°).\n```python\n# Custom Equidistant Cylindrical centered at 150E\npacific_crs = \"+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=150 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs\"\ngdf_pacific = gdf.to_crs(pacific_crs)\n```\nWhen reprojecting to a Pacific-centered CRS, GeoPandas\u002FPyProj handles the wrap-around correctly for points. For polygons, they might need to be \"unwrapped\" first if they were originally defined in [-180, 180].\n\n### 3. Using `dateline_fix` (if available)\nSome libraries have tools to split or join geometries at the dateline.\n","# Handling the Anti-meridian in Geospatial Analysis\n\nWhen working with data spanning the Pacific, geometries often cross the anti-meridian. Standard projections centered at 0° longitude will \"rip\" these geometries.\n\n## Techniques\n\n### 1. Shift Coordinates to 0-360\nYou can shift the longitude of all geometries to be in the [0, 360) range.\n```python\ndef shift_lon(geom):\n    from shapely.affinity import translate\n    # This is complex for polygons that cross the line. \n    # Better to use a specific CRS.\n    pass\n```\n\n### 2. Use a Pacific-Centered CRS\nThe most robust way is to use a CRS centered on the Pacific (e.g., 150°E or 180°).\n```python\n# Custom Equidistant Cylindrical centered at 150E\npacific_crs = \"+proj=eqc +lat_ts=0 +lat_0=0 +lon_0=150 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs\"\ngdf_pacific = gdf.to_crs(pacific_crs)\n```\nWhen reprojecting to a Pacific-centered CRS, GeoPandas\u002FPyProj handles the wrap-around correctly for points. For polygons, they might need to be \"unwrapped\" first if they were originally defined in [-180, 180].\n\n### 3. Using `dateline_fix` (if available)\nSome libraries have tools to split or join geometries at the dateline.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3-flash-preview\u002Fearthquake-plate-calculation\u002Frun2_antimeridian_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":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_antimeridian_handling",{"tool":41,"command":42},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_antimeridian_handling",{"tool":44,"command":45},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_antimeridian_handling","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3-flash-preview\u002Fearthquake-plate-calculation\u002Frun2_antimeridian_handling\u002FSKILL.md",true,[],[50,68,92,110,130,142],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":54,"categories":55,"keywords":61,"publishedAt":22},"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",[56],{"categoryNo":57,"categoryName":58,"taxonomyType":59,"taxonomyTypeDesc":60},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[62,63,64,65,66,67],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":77,"publishedAt":22},"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",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":59,"taxonomyTypeDesc":60},"01JBSEEDCAT000000000000023","Skill与智能体开发",[78,79,80,81,82,83,84,85,86,21,87,88,89,90,91],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":22},"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",[],[99,100,101,102,103,104,105,81,106,107,82,108,21,109,79],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":22},"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",[],[117,118,119,21,120,121,122,123,124,125,126,127,108,128,129],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":137,"publishedAt":22},"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",[136],{"categoryNo":75,"categoryName":76,"taxonomyType":59,"taxonomyTypeDesc":60},[138,129,119,118,124,123,120,139,140,21,141],"glm calibration","setup","companion","metric",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":22},"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",[],[149,119,123,150,138,129,21,118,151,152,153,154,155,156,120],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]