[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KKAXBTJKT6TCKXQMPQ9WM3":3,"skill-same:01M6KKAXBTJKT6TCKXQMPQ9WM3":46},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":14,"categories":15,"keywords":16,"publishedAt":20,"linkOnly":21,"markdownContent":22,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":31,"install":32,"resources":45},"01M6KKAXBTJKT6TCKXQMPQ9WM3","pareto-frontier-analysis",null,"cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Fpareto-frontier-analysis\u002FSKILL.md",[],[17,18,19],"analysis","pareto frontier analysis","pareto frontier","2026-11-10 10:12:30",false,"name: pareto-frontier-analysis\ndescription: Identifying Pareto-optimal points from a set of multi-objective results. Use this when you need to find the best trade-offs between conflicting goals like maximizing F1 and minimizing error.\n\n# Pareto Frontier Analysis\n\nThe Pareto frontier consists of all points that are not dominated by any other point.\n\n## Dominance Definition\nA point $A$ dominates point $B$ if:\n1. $A$ is at least as good as $B$ in all objectives.\n2. $A$ is strictly better than $B$ in at least one objective.\n\nFor the Mars cloud task:\n- Objective 1: Maximize F1\n- Objective 2: Minimize Delta\n\nPoint $(f1_1, d_1)$ dominates $(f1_2, d_2)$ if:\n- $(f1_1 \\ge f1_2)$ AND $(d_1 \\le d_2)$\n- AND ($(f1_1 > f1_2)$ OR $(d_1 \u003C d_2)$)\n\n## Algorithm to Find Pareto Frontier\n1. Filter results to meet baseline criteria (e.g., F1 > 0.5).\n2. For each point $P$ in the filtered set:\n   - Check if any other point $P'$ dominates $P$.\n   - If no such $P'$ exists, $P$ is on the Pareto frontier.\n\n## Implementation Tip\nTo speed up, sort the points by one objective (e.g., descending F1). Then, as you iterate, keep track of the minimum Delta seen so far.\n",{},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Fpareto-frontier-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":7,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-analysis",{"tool":38,"command":39},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-analysis",{"tool":41,"command":42},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill pareto-frontier-analysis","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fdbscan-parameter-tuning\u002Fpareto-frontier-analysis\u002FSKILL.md",true,[],[47,65,90,108,128,140],{"skillNo":48,"name":49,"nameZh":6,"description":50,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":51,"categories":52,"keywords":58,"publishedAt":20},"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",[53],{"categoryNo":54,"categoryName":55,"taxonomyType":56,"taxonomyTypeDesc":57},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[59,60,61,62,63,64],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":74,"publishedAt":20},"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",[71],{"categoryNo":72,"categoryName":73,"taxonomyType":56,"taxonomyTypeDesc":57},"01JBSEEDCAT000000000000023","Skill与智能体开发",[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":20},"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",[],[97,98,99,100,101,102,103,78,104,105,79,106,84,107,76],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":20},"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",[],[115,116,117,84,118,119,120,121,122,123,124,125,106,126,127],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"publishedAt":20},"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",[134],{"categoryNo":72,"categoryName":73,"taxonomyType":56,"taxonomyTypeDesc":57},[136,127,117,116,122,121,118,137,138,84,139],"glm calibration","setup","companion","metric",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":6,"repositoryNo":11,"starsCount":12,"licenseSpdxId":13,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":20},"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",[],[147,117,121,148,136,127,84,116,149,150,151,152,153,154,118],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]