[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XEBDBB8X3CC1RWXN1E3GGY":3,"skill-same:01M6XEBDBB8X3CC1RWXN1E3GGY":60},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":39,"commitSha":40,"skillDirectory":41,"forksCount":13,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6XEBDBB8X3CC1RWXN1E3GGY","meteorology-driver-classification",null,"Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.","Waaaalnut\u002Fskillsbench","Waaaalnut","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F103995656?v=4","01M6XCS9K1V51EY3JMB7DGMX24","01M6XCS9Q4WPW88W62C55G1H44",0,"MIT","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Flake-warming-attribution\u002Fenvironment\u002Fskills\u002Fmeteorology-driver-classification\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"classification","driver","meteorology","meteorology driver classification","attribution","analysis","use","environmental","classify","factor","Variables","you","categories","attribution analysis","for","when","2026-11-10 10:12:30",false,"---\nname: meteorology-driver-classification\ndescription: Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.\nlicense: MIT\n---\n\n# Driver Classification Guide\n\n## Overview\n\nWhen analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning.\n\n## Common Driver Categories\n\n### Heat\nVariables related to thermal energy and radiation:\n- Air temperature\n- Shortwave radiation\n- Longwave radiation\n- Net radiation (shortwave + longwave)\n- Surface temperature\n- Humidity\n- Cloud cover\n\n### Flow\nVariables related to water movement:\n- Precipitation\n- Inflow\n- Outflow\n- Streamflow\n- Evaporation\n- Runoff\n- Groundwater flux\n\n### Wind\nVariables related to atmospheric circulation:\n- Wind speed\n- Wind direction\n- Gust speed\n- Atmospheric pressure\n\n### Human\nVariables related to anthropogenic activities:\n- Developed area\n- Agriculture area\n- Impervious surface\n- Population density\n- Industrial output\n- Land use change rate\n\n## Derived Variables\n\nSometimes raw variables need to be combined before analysis:\n```python\n# Combine radiation components into net radiation\ndf['NetRadiation'] = df['Longwave'] + df['Shortwave']\n```\n\n## Grouping Strategy\n\n1. Identify all available variables in your dataset\n2. Assign each variable to a category based on physical meaning\n3. Create derived variables if needed\n4. Variables in the same category should be correlated\n\n## Validation\n\nAfter statistical grouping, verify that:\n- Variables load on expected components\n- Groupings make physical sense\n- Categories are mutually exclusive\n\n## Best Practices\n\n- Use domain knowledge to define categories\n- Combine related sub-variables before analysis\n- Keep number of categories manageable (3-5 typically)\n- Document your classification decisions\n","# Driver Classification Guide\n\n## Overview\n\nWhen analyzing what drives changes in an environmental system, it is useful to group individual variables into broader categories based on their physical meaning.\n\n## Common Driver Categories\n\n### Heat\nVariables related to thermal energy and radiation:\n- Air temperature\n- Shortwave radiation\n- Longwave radiation\n- Net radiation (shortwave + longwave)\n- Surface temperature\n- Humidity\n- Cloud cover\n\n### Flow\nVariables related to water movement:\n- Precipitation\n- Inflow\n- Outflow\n- Streamflow\n- Evaporation\n- Runoff\n- Groundwater flux\n\n### Wind\nVariables related to atmospheric circulation:\n- Wind speed\n- Wind direction\n- Gust speed\n- Atmospheric pressure\n\n### Human\nVariables related to anthropogenic activities:\n- Developed area\n- Agriculture area\n- Impervious surface\n- Population density\n- Industrial output\n- Land use change rate\n\n## Derived Variables\n\nSometimes raw variables need to be combined before analysis:\n```python\n# Combine radiation components into net radiation\ndf['NetRadiation'] = df['Longwave'] + df['Shortwave']\n```\n\n## Grouping Strategy\n\n1. Identify all available variables in your dataset\n2. Assign each variable to a category based on physical meaning\n3. Create derived variables if needed\n4. Variables in the same category should be correlated\n\n## Validation\n\nAfter statistical grouping, verify that:\n- Variables load on expected components\n- Groupings make physical sense\n- Categories are mutually exclusive\n\n## Best Practices\n\n- Use domain knowledge to define categories\n- Combine related sub-variables before analysis\n- Keep number of categories manageable (3-5 typically)\n- Document your classification decisions\n",{"name":5,"license":14,"description":7},"main","7eeb258ada72b7f58119b0e7a2c00ea89d9beedd","tasks\u002Flake-warming-attribution\u002Fenvironment\u002Fskills\u002Fmeteorology-driver-classification","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench","https:\u002F\u002Fgithub.com\u002FWaaaalnut","student of Zhejiang University","2026-04-21 23:16:23",{"source":8,"skillName":5,"commands":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add Waaaalnut\u002Fskillsbench --skill meteorology-driver-classification",{"tool":52,"command":53},"bunx","bunx skills add Waaaalnut\u002Fskillsbench --skill meteorology-driver-classification",{"tool":55,"command":56},"pnpm","pnpm dlx skills add Waaaalnut\u002Fskillsbench --skill meteorology-driver-classification","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Flake-warming-attribution\u002Fenvironment\u002Fskills\u002Fmeteorology-driver-classification\u002FSKILL.md",true,[],[61,76,91,116,137,155],{"skillNo":62,"name":63,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":72,"publishedAt":34},"01M6XEWMJ6VY3ZC53MPDCPG3GH","data-reconciliation","Apache-2.0","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fxlsx-recover-data\u002Fenvironment\u002Fskills\u002Fdata-reconciliation\u002FSKILL.md",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[73,74,75],"data reconciliation","reconciliation","data",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":34},"01M6XEW8MNPMF1KRXV6ATGFRQF","speech-to-text","Transcribe video to timestamped text using Whisper tiny model (pre-installed).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-tutorial-indexer\u002Fenvironment\u002Fskills\u002Fspeech-to-text\u002FSKILL.md",[],[83,84,85,86,87,88,89,90],"speech","text","speech to text","transcribe video","whisper","model","Video","transcribe",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":34},"01M6XEW3FQZP0T1RKQQJFXVY9T","video-processor","Process videos by removing segments and concatenating remaining parts. Use when you need to remove detected pauses\u002Fopenings from videos, create highlight reels, or batch process segment removals using ffmpeg filter_complex.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fvideo-processor\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[89,103,104,105,106,107,33,108,109,110,111,29,112,113,114,24,115],"processor","video processor","batch","create","reels","parts","segments","process","videos","segment","filter","ffmpeg","remove",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":34},"01M6XEVXJ5KZ0NTY3XZKRY4AS1","silence-detector","Detect initial silence segments in audio\u002Fvideo using energy-based analysis. Use when you need to find low-energy periods at the start of recordings (title slides, setup time, pre-roll silence).","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsilence-detector\u002FSKILL.md",[122],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[124,125,126,127,33,29,128,129,130,131,132,109,133,134,89,23,135,136,24],"detector","silence detector","title","slides","time","energy","at","recordings","detect","find","Audio","start","setup",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":143,"publishedAt":34},"01M6XEVT3NGRN1FT7JPMAPXS6M","segment-combiner","Combine multiple segment detection results into a unified list. Use when you need to merge segments from different detectors, prepare removal lists for video processing, or consolidate detection outputs.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fsegment-combiner\u002FSKILL.md",[],[112,144,89,145,146,147,148,32,33,149,150,109,29,151,152,24,153,154],"segment combiner","detection","prepare","merge","video processing","outputs","combine","processing","consolidate","results","list",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":64,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":34},"01M6XEVMPVASGS2KX86QK4FMT7","report-generator","Generate compression reports for video processing. Use when you need to create structured JSON reports with duration statistics, compression ratios, and segment details after video processing.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Freport-generator\u002FSKILL.md",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000034","行政与办公人员",[165,166,167,112,33,32,29,168,151,169,170,171,24,148,172,89,173,174,106],"generator","report generator","report","with","statistics","structured","reports","json","compression","generate"]