[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KHJC8WMXT7EZ3TFSYRQT25":3,"skill-same:01M6KHJC8WMXT7EZ3TFSYRQT25":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},"01M6KHJC8WMXT7EZ3TFSYRQT25","mars-cloud-data-grouping",null,"Organizing annotation data by image identifiers to prepare for image-by-image clustering analysis.","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\u002Fb3-teacher-feedback-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Frun1_mars-cloud-data-grouping\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33],"cloud","data","grouping","mars cloud data grouping","image","prepare","clustering","analysis","clustering analysis","annotation","for","2026-11-10 10:12:30",false,"---\nname: mars-cloud-data-grouping\ndescription: Organizing annotation data by image identifiers to prepare for image-by-image clustering analysis.\n---\n\nThe dataset uses a `file_rad` identifier to link citizen science observations to expert labels. Since evaluations must happen per image:\n\n1.  Load CSVs using `pandas`.\n2.  Pre-group the DataFrames using `.groupby('file_rad')` and store them in a dictionary for fast lookup.\n3.  Ensure that the evaluation loop iterates over all `file_rad` values present in the **expert** dataset to account for missed detections (FNs).\n\n```python\nimport pandas as pd\n\ncitsci = pd.read_csv('citsci_train.csv')\nexpert = pd.read_csv('expert_train.csv')\n\n# Grouping for efficient access\ncit_groups = {name: group[['x', 'y']].values for name, group in citsci.groupby('file_rad')}\nexp_groups = {name: group[['x', 'y']].values for name, group in expert.groupby('file_rad')}\n\nall_image_ids = expert['file_rad'].unique()\n```","The dataset uses a `file_rad` identifier to link citizen science observations to expert labels. Since evaluations must happen per image:\n\n1.  Load CSVs using `pandas`.\n2.  Pre-group the DataFrames using `.groupby('file_rad')` and store them in a dictionary for fast lookup.\n3.  Ensure that the evaluation loop iterates over all `file_rad` values present in the **expert** dataset to account for missed detections (FNs).\n\n```python\nimport pandas as pd\n\ncitsci = pd.read_csv('citsci_train.csv')\nexpert = pd.read_csv('expert_train.csv')\n\n# Grouping for efficient access\ncit_groups = {name: group[['x', 'y']].values for name, group in citsci.groupby('file_rad')}\nexp_groups = {name: group[['x', 'y']].values for name, group in expert.groupby('file_rad')}\n\nall_image_ids = expert['file_rad'].unique()\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Frun1_mars-cloud-data-grouping",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 mars-cloud-data-grouping",{"tool":53,"command":54},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill mars-cloud-data-grouping",{"tool":56,"command":57},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill mars-cloud-data-grouping","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Frun1_mars-cloud-data-grouping\u002FSKILL.md",true,[],[62,79,103,121,141,153],{"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":73,"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":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[74,75,76,77,78,27],"object counter","object","vision","computer","Computer Vision",{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":88,"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",[85],{"categoryNo":86,"categoryName":87,"taxonomyType":71,"taxonomyTypeDesc":72},"01JBSEEDCAT000000000000023","Skill与智能体开发",[89,90,91,92,93,94,95,96,97,33,98,99,100,101,102],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"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",[],[110,111,112,113,114,115,116,92,117,118,93,119,33,120,90],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"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",[],[128,129,130,33,131,132,133,134,135,136,137,138,119,139,140],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":148,"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",[147],{"categoryNo":86,"categoryName":87,"taxonomyType":71,"taxonomyTypeDesc":72},[149,140,130,129,135,134,131,150,151,33,152],"glm calibration","setup","companion","metric",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"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",[],[160,130,134,161,149,140,33,129,162,163,164,165,166,167,131],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]