[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDKCQ84D55Z7PFWPQ286GC":3,"skill-same:01M6KDKCQ84D55Z7PFWPQ286GC":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},"01M6KDKCQ84D55Z7PFWPQ286GC","plate-tectonics-analysis",null,"Analyzing earthquake data relative to tectonic plate boundaries using PB2002 dataset and GeoPandas.","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\u002Fb1-one-shot-claude-opus-4-6\u002Fearthquake-plate-calculation\u002Fplate-tectonics-analysis\u002FSKILL.md",[],[18,19,20,21,22,23,24,25],"analysis","plate tectonics","plate tectonics analysis","analyzing","data","geopandas","dataset","tectonic","2026-11-10 10:12:30",false,"---\nname: plate-tectonics-analysis\ndescription: Analyzing earthquake data relative to tectonic plate boundaries using PB2002 dataset and GeoPandas.\n---\n\n# Plate Tectonics Analysis with PB2002 Dataset\n\n## PB2002 Dataset Structure\n\n### Plates (`PB2002_plates.json`)\n- GeoJSON FeatureCollection with Polygon geometries\n- Properties include `PlateName` (e.g., \"PA\" for Pacific)\n- Each feature is a tectonic plate polygon\n\n### Boundaries (`PB2002_boundaries.json`)\n- GeoJSON FeatureCollection with LineString geometries\n- Properties: `PlateA`, `PlateB` (2-letter plate codes), `Name`, `Type`\n- Pacific plate code: \"PA\"\n- Boundaries involving Pacific: PlateA == \"PA\" or PlateB == \"PA\"\n\n### Common Plate Codes\n- PA: Pacific, NA: North America, SA: South America\n- EU: Eurasia, AF: Africa, AN: Antarctica\n- AU: Australia, IN: India, NZ: Nazca\n\n## Workflow: Find Earthquakes Inside a Plate\n\n```python\nimport geopandas as gpd\n\n# Load plate polygons and earthquake points\nplates = gpd.read_file(\"PB2002_plates.json\")\nearthquakes = gpd.read_file(\"earthquakes.json\")\n\n# Get Pacific plate polygon\npacific = plates[plates[\"PlateName\"] == \"PA\"]\n\n# Spatial join: earthquakes within Pacific plate\neq_in_pacific = gpd.sjoin(earthquakes, pacific, predicate=\"within\")\n```\n\n## Workflow: Get Plate Boundaries\n\n```python\nboundaries = gpd.read_file(\"PB2002_boundaries.json\")\n\n# Filter boundaries of Pacific plate\npa_bounds = boundaries[\n    (boundaries[\"PlateA\"] == \"PA\") | (boundaries[\"PlateB\"] == \"PA\")\n]\n\n# Merge all boundary segments into one geometry\npa_boundary_union = pa_bounds.unary_union\n```\n\n## Workflow: Distance from Earthquake to Plate Boundary\n\n```python\n# Project both to a suitable CRS\nproj_crs = \"+proj=aeqd +lat_0=0 +lon_0=-160 +datum=WGS84 +units=m\"\n\neq_proj = eq_in_pacific.to_crs(proj_crs)\nboundary_proj = pa_bounds.to_crs(proj_crs)\nboundary_union_proj = boundary_proj.unary_union\n\n# Calculate distances\neq_proj[\"dist_to_boundary\"] = eq_proj.geometry.distance(boundary_union_proj)\neq_proj[\"dist_km\"] = eq_proj[\"dist_to_boundary\"] \u002F 1000\n\n# Find the earthquake furthest from boundary\nfurthest = eq_proj.loc[eq_proj[\"dist_km\"].idxmax()]\n```\n","# Plate Tectonics Analysis with PB2002 Dataset\n\n## PB2002 Dataset Structure\n\n### Plates (`PB2002_plates.json`)\n- GeoJSON FeatureCollection with Polygon geometries\n- Properties include `PlateName` (e.g., \"PA\" for Pacific)\n- Each feature is a tectonic plate polygon\n\n### Boundaries (`PB2002_boundaries.json`)\n- GeoJSON FeatureCollection with LineString geometries\n- Properties: `PlateA`, `PlateB` (2-letter plate codes), `Name`, `Type`\n- Pacific plate code: \"PA\"\n- Boundaries involving Pacific: PlateA == \"PA\" or PlateB == \"PA\"\n\n### Common Plate Codes\n- PA: Pacific, NA: North America, SA: South America\n- EU: Eurasia, AF: Africa, AN: Antarctica\n- AU: Australia, IN: India, NZ: Nazca\n\n## Workflow: Find Earthquakes Inside a Plate\n\n```python\nimport geopandas as gpd\n\n# Load plate polygons and earthquake points\nplates = gpd.read_file(\"PB2002_plates.json\")\nearthquakes = gpd.read_file(\"earthquakes.json\")\n\n# Get Pacific plate polygon\npacific = plates[plates[\"PlateName\"] == \"PA\"]\n\n# Spatial join: earthquakes within Pacific plate\neq_in_pacific = gpd.sjoin(earthquakes, pacific, predicate=\"within\")\n```\n\n## Workflow: Get Plate Boundaries\n\n```python\nboundaries = gpd.read_file(\"PB2002_boundaries.json\")\n\n# Filter boundaries of Pacific plate\npa_bounds = boundaries[\n    (boundaries[\"PlateA\"] == \"PA\") | (boundaries[\"PlateB\"] == \"PA\")\n]\n\n# Merge all boundary segments into one geometry\npa_boundary_union = pa_bounds.unary_union\n```\n\n## Workflow: Distance from Earthquake to Plate Boundary\n\n```python\n# Project both to a suitable CRS\nproj_crs = \"+proj=aeqd +lat_0=0 +lon_0=-160 +datum=WGS84 +units=m\"\n\neq_proj = eq_in_pacific.to_crs(proj_crs)\nboundary_proj = pa_bounds.to_crs(proj_crs)\nboundary_union_proj = boundary_proj.unary_union\n\n# Calculate distances\neq_proj[\"dist_to_boundary\"] = eq_proj.geometry.distance(boundary_union_proj)\neq_proj[\"dist_km\"] = eq_proj[\"dist_to_boundary\"] \u002F 1000\n\n# Find the earthquake furthest from boundary\nfurthest = eq_proj.loc[eq_proj[\"dist_km\"].idxmax()]\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-opus-4-6\u002Fearthquake-plate-calculation\u002Fplate-tectonics-analysis",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 plate-tectonics-analysis",{"tool":45,"command":46},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill plate-tectonics-analysis",{"tool":48,"command":49},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill plate-tectonics-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-opus-4-6\u002Fearthquake-plate-calculation\u002Fplate-tectonics-analysis\u002FSKILL.md",true,[],[54,72,97,114,134,146],{"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",[82,83,84,85,86,87,88,89,90,91,92,93,94,95,96],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"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",[],[104,105,24,106,107,108,109,85,110,111,86,112,91,113,83],"search","tool","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"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",[],[121,122,123,91,124,125,126,127,128,129,130,131,112,132,133],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"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",[140],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},[142,133,123,122,128,127,124,143,144,91,145],"glm calibration","setup","companion","metric",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"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",[],[153,123,127,154,142,133,91,122,155,156,157,158,159,160,124],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]