[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KEY4TE7ZF5XRC41VRVZ2QH":3,"skill-same:01M6KEY4TE7ZF5XRC41VRVZ2QH":55},{"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":28,"linkOnly":29,"markdownContent":30,"markdownBody":31,"frontMatter":32,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":38,"ownerHtmlUrl":39,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":40,"install":41,"resources":54},"01M6KEY4TE7ZF5XRC41VRVZ2QH","custom-distance-metrics",null,"Custom distance metric implementation for use with clustering algorithms like DBSCAN.","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-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Fcustom-distance-metrics\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27],"metrics","custom","custom distance metrics","clustering","use","implementation","algorithms","with","for","metric","2026-11-10 10:12:30",false,"---\nname: custom-distance-metrics\ndescription: Custom distance metric implementation for use with clustering algorithms like DBSCAN.\n---\n\n# Custom Distance Metrics for Scikit-Learn\n\nWhen clustering spatial data, you often want to weigh dimensions differently or apply a non-standard metric.\n\n## Usage\n\nYou can implement custom metrics in `sklearn` or `scipy`. Scikit-learn's `DBSCAN` allows a custom distance metric passed as a callable.\n\nExample:\n```python\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\n\ndef custom_metric(x, y, weight=1.0):\n    dx = x[0] - y[0]\n    dy = x[1] - y[1]\n    return np.sqrt((weight * dx)**2 + ((2 - weight) * dy)**2)\n\n# Usage in DBSCAN\ndbscan = DBSCAN(eps=5, min_samples=3, metric=custom_metric, metric_params={'weight': 1.5})\nclusters = dbscan.fit_predict(data)\n```\nAlternatively, for performance, precompute the distance matrix or use `cdist`\u002F`pdist` with custom metrics if needed, but the callable approach is simplest.\n","# Custom Distance Metrics for Scikit-Learn\n\nWhen clustering spatial data, you often want to weigh dimensions differently or apply a non-standard metric.\n\n## Usage\n\nYou can implement custom metrics in `sklearn` or `scipy`. Scikit-learn's `DBSCAN` allows a custom distance metric passed as a callable.\n\nExample:\n```python\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\n\ndef custom_metric(x, y, weight=1.0):\n    dx = x[0] - y[0]\n    dy = x[1] - y[1]\n    return np.sqrt((weight * dx)**2 + ((2 - weight) * dy)**2)\n\n# Usage in DBSCAN\ndbscan = DBSCAN(eps=5, min_samples=3, metric=custom_metric, metric_params={'weight': 1.5})\nclusters = dbscan.fit_predict(data)\n```\nAlternatively, for performance, precompute the distance matrix or use `cdist`\u002F`pdist` with custom metrics if needed, but the callable approach is simplest.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Fcustom-distance-metrics",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":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill custom-distance-metrics",{"tool":47,"command":48},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill custom-distance-metrics",{"tool":50,"command":51},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill custom-distance-metrics","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\u002Fcustom-distance-metrics\u002FSKILL.md",true,[],[56,74,98,116,135,146],{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":67,"publishedAt":28},"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",[62],{"categoryNo":63,"categoryName":64,"taxonomyType":65,"taxonomyTypeDesc":66},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[68,69,70,71,72,73],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":83,"publishedAt":28},"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",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":65,"taxonomyTypeDesc":66},"01JBSEEDCAT000000000000023","Skill与智能体开发",[84,85,86,87,88,89,90,91,92,26,93,94,95,96,97],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":28},"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",[],[105,106,107,108,109,110,111,87,112,113,88,114,26,115,85],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":28},"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",[],[123,124,125,26,126,18,127,128,129,130,131,132,114,133,134],"glm output","output","glm","tasks","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":28},"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",[141],{"categoryNo":81,"categoryName":82,"taxonomyType":65,"taxonomyTypeDesc":66},[143,134,125,124,129,128,126,144,145,26,27],"glm calibration","setup","companion",{"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":28},"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",[],[153,125,128,154,143,134,26,124,155,156,157,22,158,159,126],"glm basics","inspect","relevant","files","good","how","Evaluation"]