[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KJAFXMHVCWMB8DT9XQZRBN":3,"skill-same:01M6KJAFXMHVCWMB8DT9XQZRBN":57},{"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":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":40,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M6KJAFXMHVCWMB8DT9XQZRBN","mars-custom-distance-metric",null,"Implement weighted custom distance metric for DBSCAN clustering. Use this skill when working with DBSCAN on Mars cloud data where distances need to be weighted differently across x and y axes using a shape_weight parameter (w). The custom metric is d(a,b) = sqrt((w*Δx)² + ((2-w)*Δy)²), controlling whether x-distances or y-distances are attenuated.","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-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Fmars-custom-distance-metric\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29],"metric","custom","mars custom distance metric","implement","cloud","data","clustering","use","shape","across","with","for","2026-11-10 10:12:30",false,"---\nname: mars-custom-distance-metric\ndescription: Implement weighted custom distance metric for DBSCAN clustering. Use this skill when working with DBSCAN on Mars cloud data where distances need to be weighted differently across x and y axes using a shape_weight parameter (w). The custom metric is d(a,b) = sqrt((w*Δx)² + ((2-w)*Δy)²), controlling whether x-distances or y-distances are attenuated.\n---\n\n# Mars Custom Distance Metric for DBSCAN\n\n## Overview\n\nThe Mars cloud clustering task requires a custom distance metric that lets you control the relative weight of x-distances vs y-distances:\n\n```\nd(a, b) = sqrt((w * Δx)² + ((2 - w) * Δy)²)\n```\n\nWhere `w` (shape_weight) ranges from 0.9 to 1.9:\n- w = 1.0: Standard Euclidean distance\n- w > 1.0: Attenuates y-distances (prioritizes x-variation)\n- w \u003C 1.0: Attenuates x-distances (prioritizes y-variation)\n\n## Implementation with scipy.spatial.distance\n\nUse `scipy.spatial.distance.cdist` with a custom callable metric:\n\n```python\nfrom scipy.spatial.distance import cdist\nimport numpy as np\n\ndef shape_weighted_distance(u, v, w):\n    \"\"\"\n    Compute shape-weighted distance between two points.\n\n    u, v: 1D arrays of coordinates [x, y]\n    w: shape_weight parameter (0.9 to 1.9)\n    \"\"\"\n    dx = (w * (u[0] - v[0]))**2\n    dy = ((2 - w) * (u[1] - v[1]))**2\n    return np.sqrt(dx + dy)\n\n# For use with scipy cdist (requires callable metric):\nfrom functools import partial\n\nmetric = partial(shape_weighted_distance, w=shape_weight_value)\ndistance_matrix = cdist(points, points, metric=metric)\n```\n\n## Using with DBSCAN\n\nsklearn's DBSCAN accepts a precomputed distance matrix:\n\n```python\nfrom sklearn.cluster import DBSCAN\nfrom scipy.spatial.distance import pdist, squareform\nfrom functools import partial\n\ndef shape_weighted_distance(u, v, w):\n    dx = (w * (u[0] - v[0]))**2\n    dy = ((2 - w) * (u[1] - v[1]))**2\n    return np.sqrt(dx + dy)\n\n# Compute full distance matrix with custom metric\nmetric_func = partial(shape_weighted_distance, w=shape_weight)\ndistance_matrix = cdist(points, points, metric=metric_func)\n\n# Run DBSCAN with precomputed metric\nclustering = DBSCAN(eps=epsilon, min_samples=min_samples, metric='precomputed')\nlabels = clustering.fit_predict(distance_matrix)\n```\n\n## Key Points\n\n1. **Two-point vs pairwise distances**: Use `cdist` for pairwise distances (all points vs all points), not `shape_weighted_distance` directly on individual pairs\n2. **DBSCAN precomputed mode**: Pass `metric='precomputed'` to DBSCAN and provide the full distance matrix\n3. **Standard Euclidean for matching**: When matching cluster centroids to expert points later, always use standard Euclidean distance (w=1.0), not the custom metric\n4. **Parameter range**: shape_weight typically ranges 0.9–1.9 in grid search\n","# Mars Custom Distance Metric for DBSCAN\n\n## Overview\n\nThe Mars cloud clustering task requires a custom distance metric that lets you control the relative weight of x-distances vs y-distances:\n\n```\nd(a, b) = sqrt((w * Δx)² + ((2 - w) * Δy)²)\n```\n\nWhere `w` (shape_weight) ranges from 0.9 to 1.9:\n- w = 1.0: Standard Euclidean distance\n- w > 1.0: Attenuates y-distances (prioritizes x-variation)\n- w \u003C 1.0: Attenuates x-distances (prioritizes y-variation)\n\n## Implementation with scipy.spatial.distance\n\nUse `scipy.spatial.distance.cdist` with a custom callable metric:\n\n```python\nfrom scipy.spatial.distance import cdist\nimport numpy as np\n\ndef shape_weighted_distance(u, v, w):\n    \"\"\"\n    Compute shape-weighted distance between two points.\n\n    u, v: 1D arrays of coordinates [x, y]\n    w: shape_weight parameter (0.9 to 1.9)\n    \"\"\"\n    dx = (w * (u[0] - v[0]))**2\n    dy = ((2 - w) * (u[1] - v[1]))**2\n    return np.sqrt(dx + dy)\n\n# For use with scipy cdist (requires callable metric):\nfrom functools import partial\n\nmetric = partial(shape_weighted_distance, w=shape_weight_value)\ndistance_matrix = cdist(points, points, metric=metric)\n```\n\n## Using with DBSCAN\n\nsklearn's DBSCAN accepts a precomputed distance matrix:\n\n```python\nfrom sklearn.cluster import DBSCAN\nfrom scipy.spatial.distance import pdist, squareform\nfrom functools import partial\n\ndef shape_weighted_distance(u, v, w):\n    dx = (w * (u[0] - v[0]))**2\n    dy = ((2 - w) * (u[1] - v[1]))**2\n    return np.sqrt(dx + dy)\n\n# Compute full distance matrix with custom metric\nmetric_func = partial(shape_weighted_distance, w=shape_weight)\ndistance_matrix = cdist(points, points, metric=metric_func)\n\n# Run DBSCAN with precomputed metric\nclustering = DBSCAN(eps=epsilon, min_samples=min_samples, metric='precomputed')\nlabels = clustering.fit_predict(distance_matrix)\n```\n\n## Key Points\n\n1. **Two-point vs pairwise distances**: Use `cdist` for pairwise distances (all points vs all points), not `shape_weighted_distance` directly on individual pairs\n2. **DBSCAN precomputed mode**: Pass `metric='precomputed'` to DBSCAN and provide the full distance matrix\n3. **Standard Euclidean for matching**: When matching cluster centroids to expert points later, always use standard Euclidean distance (w=1.0), not the custom metric\n4. **Parameter range**: shape_weight typically ranges 0.9–1.9 in grid search\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Fmars-custom-distance-metric",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":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill mars-custom-distance-metric",{"tool":49,"command":50},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill mars-custom-distance-metric",{"tool":52,"command":53},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill mars-custom-distance-metric","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-claude-haiku-4-5\u002Fdbscan-parameter-tuning\u002Fmars-custom-distance-metric\u002FSKILL.md",true,[],[58,76,100,118,138,149],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":69,"publishedAt":30},"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",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[70,71,72,73,74,75],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":85,"publishedAt":30},"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",[82],{"categoryNo":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发",[86,87,88,89,90,91,92,93,94,29,95,96,97,98,99],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":30},"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",[],[107,108,109,110,111,112,113,89,114,115,90,116,29,117,87],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":30},"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",[],[125,126,127,29,128,129,130,131,132,133,134,135,116,136,137],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":145,"publishedAt":30},"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",[144],{"categoryNo":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},[146,137,127,126,132,131,128,147,148,29,18],"glm calibration","setup","companion",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":30},"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",[],[156,127,131,157,146,137,29,126,158,159,160,25,161,162,128],"glm basics","inspect","relevant","files","good","how","Evaluation"]