[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KEYCGMRRFJEGZACVRZX84T":3,"skill-same:01M6KEYCGMRRFJEGZACVRZX84T":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},"01M6KEYCGMRRFJEGZACVRZX84T","pareto-optimization",null,"Algorithm to identify Pareto-optimal points from a set of multi-objective evaluations.","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\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Fpareto-optimization\u002FSKILL.md",[],[18,19,20,21],"Optimization","pareto optimization","SET","multi","2026-11-10 10:12:30",false,"---\nname: pareto-optimization\ndescription: Algorithm to identify Pareto-optimal points from a set of multi-objective evaluations.\n---\n\n# Pareto Optimization\n\nWhen evaluating multiple conflicting objectives (e.g., maximize F1 score, minimize distance), a point is Pareto optimal if no other point is better in all objectives.\n\n## Usage\n```python\nimport numpy as np\n\ndef is_pareto_efficient(costs, maximize=[True, False]):\n    \"\"\\\"\n    Find the pareto-efficient points\n    costs: An (n_points, n_costs) array\n    maximize: Boolean array indicating if the corresponding objective should be maximized\n    \"\"\\\"\n    is_efficient = np.ones(costs.shape[0], dtype=bool)\n    \n    # Adjust costs so we can simply look for points that are strictly \"less than or equal\"\n    # For objectives we want to maximize, we negate them.\n    adjusted_costs = np.copy(costs)\n    for i, max_obj in enumerate(maximize):\n        if max_obj:\n            adjusted_costs[:, i] = -adjusted_costs[:, i]\n            \n    for i, c in enumerate(adjusted_costs):\n        if is_efficient[i]:\n            # Keep any point with a lower cost or not strictly dominated\n            # A point is strictly dominated if it's >= in all dimensions and > in at least one\n            is_efficient[is_efficient] = np.any(adjusted_costs[is_efficient] \u003C c, axis=1) | np.all(adjusted_costs[is_efficient] == c, axis=1)\n            is_efficient[i] = True  # And keep self\n    return is_efficient\n```\n","# Pareto Optimization\n\nWhen evaluating multiple conflicting objectives (e.g., maximize F1 score, minimize distance), a point is Pareto optimal if no other point is better in all objectives.\n\n## Usage\n```python\nimport numpy as np\n\ndef is_pareto_efficient(costs, maximize=[True, False]):\n    \"\"\\\"\n    Find the pareto-efficient points\n    costs: An (n_points, n_costs) array\n    maximize: Boolean array indicating if the corresponding objective should be maximized\n    \"\"\\\"\n    is_efficient = np.ones(costs.shape[0], dtype=bool)\n    \n    # Adjust costs so we can simply look for points that are strictly \"less than or equal\"\n    # For objectives we want to maximize, we negate them.\n    adjusted_costs = np.copy(costs)\n    for i, max_obj in enumerate(maximize):\n        if max_obj:\n            adjusted_costs[:, i] = -adjusted_costs[:, i]\n            \n    for i, c in enumerate(adjusted_costs):\n        if is_efficient[i]:\n            # Keep any point with a lower cost or not strictly dominated\n            # A point is strictly dominated if it's >= in all dimensions and > in at least one\n            is_efficient[is_efficient] = np.any(adjusted_costs[is_efficient] \u003C c, axis=1) | np.all(adjusted_costs[is_efficient] == c, axis=1)\n            is_efficient[i] = True  # And keep self\n    return is_efficient\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Fpareto-optimization",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":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill pareto-optimization",{"tool":41,"command":42},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill pareto-optimization",{"tool":44,"command":45},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill pareto-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Fpareto-optimization\u002FSKILL.md",true,[],[50,68,93,111,131,143],{"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",[78,79,80,81,82,83,84,85,86,87,88,89,90,91,92],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"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",[],[100,101,102,103,104,105,106,81,107,108,82,109,87,110,79],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"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",[],[118,119,120,87,121,122,123,124,125,126,127,128,109,129,130],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":138,"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",[137],{"categoryNo":75,"categoryName":76,"taxonomyType":59,"taxonomyTypeDesc":60},[139,130,120,119,125,124,121,140,141,87,142],"glm calibration","setup","companion","metric",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":149,"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",[],[150,120,124,151,139,130,87,119,152,153,154,155,156,157,121],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]