[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"creator:01M6XCS9K1V51EY3JMB7DGMX24":3,"creator-list:01M6XCS9K1V51EY3JMB7DGMX24:stars:1:":19},{"accountNo":4,"login":5,"displayName":5,"avatarUrl":6,"accountType":7,"accountTypeDesc":8,"profileDescription":9,"skillCount":10,"repoCount":11,"professionCount":12,"scenarioCount":13,"totalStars":14,"updatedAt":15,"htmlUrl":16,"blogUrl":17,"company":18,"location":17,"followersCount":14,"publicReposCount":11},"01M6XCS9K1V51EY3JMB7DGMX24","Waaaalnut","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F103995656?v=4","USER","个人作者","student of Zhejiang University",397,1,6,7,0,"2026-04-21 23:16:23","https:\u002F\u002Fgithub.com\u002FWaaaalnut",null,"Zhejiang University",{"records":20,"total":10,"current":11,"size":338},[21,39,54,82,104,123,143,160,175,189,205,224,241,262,282,297,314,327],{"skillNo":22,"name":23,"nameZh":17,"description":17,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":27,"categories":28,"keywords":34,"publishedAt":38},"01M6XEWMJ6VY3ZC53MPDCPG3GH","data-reconciliation","Waaaalnut\u002Fskillsbench","01M6XCS9Q4WPW88W62C55G1H44","Apache-2.0","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fxlsx-recover-data\u002Fenvironment\u002Fskills\u002Fdata-reconciliation\u002FSKILL.md",[29],{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[35,36,37],"data","reconciliation","data reconciliation","2026-11-10 10:12:30",{"skillNo":40,"name":41,"nameZh":17,"description":42,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":43,"categories":44,"keywords":45,"publishedAt":38},"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",[],[46,47,48,49,50,51,52,53],"speech","text","speech to text","transcribe","Video","model","whisper","transcribe video",{"skillNo":55,"name":56,"nameZh":17,"description":57,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":58,"categories":59,"keywords":65,"publishedAt":38},"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",[60],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[66,50,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81],"processor","video processor","create","batch","process","remove","use","ffmpeg","filter","segment","you","videos","reels","segments","parts","when",{"skillNo":83,"name":84,"nameZh":17,"description":85,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":86,"categories":87,"keywords":89,"publishedAt":38},"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",[88],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},[90,91,92,93,94,50,95,96,97,72,98,99,100,76,79,101,102,103,81],"detector","silence detector","setup","start","analysis","Audio","slides","find","recordings","time","detect","at","energy","title",{"skillNo":105,"name":106,"nameZh":17,"description":107,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":38},"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",[],[75,111,112,113,114,50,115,116,117,72,118,119,76,79,120,121,122,81],"segment combiner","merge","prepare","detection","video processing","list","results","consolidate","processing","combine","for","outputs",{"skillNo":124,"name":125,"nameZh":17,"description":126,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":127,"categories":128,"keywords":132,"publishedAt":38},"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",[129],{"categoryNo":130,"categoryName":131,"taxonomyType":32,"taxonomyTypeDesc":33},"01JBSEEDCAT000000000000034","行政与办公人员",[133,134,135,68,136,137,50,138,115,72,139,140,141,75,119,142,76,121,81],"report","report generator","generator","generate","compression","json","reports","structured","statistics","with",{"skillNo":144,"name":145,"nameZh":17,"description":146,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":38},"01M6XEVENVQRR0KHK0WZZM7ZTW","pause-detector","Detect pauses and silence in audio using local dynamic thresholds. Use when you need to find natural pauses in lectures, board-writing silences, or breaks between sections. Uses local context comparison to avoid false positives from volume variation.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fpause-detector\u002FSKILL.md",[],[90,150,151,152,95,97,153,154,72,155,156,157,158,100,159,76,81],"pause","pause detector","writing","board","context","volume","local","false","thresholds","comparison",{"skillNo":161,"name":162,"nameZh":17,"description":163,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":38},"01M6XEVA1ZWSXWKZV1M1NGTY2F","energy-calculator","Calculate per-second RMS energy from audio files. Use when you need to analyze audio volume patterns, prepare data for silence\u002Fpause detection, or create an energy profile for audio analysis tasks.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Fenergy-calculator\u002FSKILL.md",[],[167,102,168,169,68,35,114,94,95,170,171,72,172,173,155,76,121,150,174,81],"calculator","energy calculator","tasks","analyze","calculate","files","patterns","second",{"skillNo":176,"name":177,"nameZh":17,"description":178,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":179,"categories":180,"keywords":182,"publishedAt":38},"01M6XEV6NHJP5TKAGEVR57JQMS","audio-extractor","Extract audio from video files to WAV format. Use when you need to analyze audio from video, prepare audio for energy calculation, or convert video audio to standard format for processing.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-silence-remover\u002Fenvironment\u002Fskills\u002Faudio-extractor\u002FSKILL.md",[181],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},[95,183,113,50,184,185,170,186,72,172,119,187,76,121,102,188,81],"audio extractor","convert","format","extract","calculation","standard",{"skillNo":190,"name":191,"nameZh":17,"description":192,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":193,"categories":194,"keywords":196,"publishedAt":38},"01M6XEV3DVX266KV6XH2FKWNAB","whisper-transcription","Transcribe audio\u002Fvideo to text with word-level timestamps using OpenAI Whisper. Use when you need speech-to-text with accurate timing information for each word.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-filler-word-remover\u002Fenvironment\u002Fskills\u002Fwhisper-transcription\u002FSKILL.md",[195],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},[52,197,198,199,200,46,49,50,95,201,47,72,41,202,203,142,76,204,121,81],"transcription","whisper transcription","openai whisper","openai","Word","timing","information","timestamps",{"skillNo":206,"name":207,"nameZh":17,"description":208,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":209,"categories":210,"keywords":215,"publishedAt":38},"01M6XEV01E9803ZGQ9T0K45T43","filler-word-processing","Process filler word annotations to generate video edit lists. Use when working with timestamp annotations for removing speech disfluencies (um, uh, like, you know) from audio\u002Fvideo content.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-filler-word-remover\u002Fenvironment\u002Fskills\u002Ffiller-word-processing\u002FSKILL.md",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},[201,119,216,217,136,46,218,50,95,219,220,221,70,72,222,142,76,121,223,81],"filler word processing","generate video","content","annotations","timestamp","edit","video content","video edit",{"skillNo":225,"name":226,"nameZh":17,"description":227,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":228,"categories":229,"keywords":231,"publishedAt":38},"01M6XETWDHRK06DD2DHMK82QFR","ffmpeg-video-editing","Video editing with ffmpeg including cutting, trimming, concatenating segments, and re-encoding. Use when working with video files (.mp4, .mkv, .avi) for: removing segments, joining clips, extracting portions, or any video manipulation task.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Fvideo-filler-word-remover\u002Fenvironment\u002Fskills\u002Fffmpeg-video-editing\u002FSKILL.md",[230],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},[50,232,233,73,234,235,236,72,172,237,142,238,239,79,121,240,81],"video editing","editing","FFmpeg Video Editing","encoding","mp4","task","extracting","re","trimming",{"skillNo":242,"name":243,"nameZh":17,"description":244,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":245,"categories":246,"keywords":247,"publishedAt":38},"01M6XETRFKS41H50WGX666HR26","time_series_anomaly_detection","Detect anomalies in time series data using Prophet Framework (Meta), which frames the seasonality, trend holiday effect and other needed regressors into its model, to identify unusual surges or slumps in trends. This is a general methodology analyst can use for understanding what changes of their tracking metrics are manifesting anomalies pattern.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftrend-anomaly-causal-inference\u002Fenvironment\u002Fskills\u002Ftime_series_anomaly_detection\u002FSKILL.md",[],[248,114,99,249,250,251,252,253,51,254,72,255,256,257,258,100,259,260,121,261],"time series","series","anomaly detection","time series anomaly detection","anomaly","Meta","effect","general","prophet","trend","trends","which","can","changes",{"skillNo":263,"name":264,"nameZh":17,"description":265,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":266,"categories":267,"keywords":268,"publishedAt":38},"01M6XETN040VGWR60XX42SS9VR","feature_engineering","Engineer dataset features before ML or Causal Inference. Methods include encoding categorical variables, scaling numerics, creating interactions, and selecting relevant features.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftrend-anomaly-causal-inference\u002Fenvironment\u002Fskills\u002Ffeature_engineering\u002FSKILL.md",[],[269,270,271,272,273,274,235,275,276,277,278,279,280,281],"feature engineering","engineering","feature","inference","causal inference","ml","engineer","methods","scaling","dataset","relevant","Variables","features",{"skillNo":283,"name":284,"nameZh":17,"description":285,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":286,"categories":287,"keywords":288,"publishedAt":38},"01M6XETGQ1N8HXRN4R6K99AR2Z","did_causal_analysis","Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A\u002FB testing, or policy evaluation.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftrend-anomaly-causal-inference\u002Fenvironment\u002Fskills\u002Fdid_causal_analysis\u002FSKILL.md",[],[94,289,290,291,292,72,293,294,142,121,261,295,296],"did causal analysis","testing","policy","Evaluation","behavioral","event","effects","value",{"skillNo":298,"name":299,"nameZh":17,"description":300,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":301,"categories":302,"keywords":304,"publishedAt":38},"01M6XETDDN3EXP7GF2KH71E638","data_cleaning","Clean messy tabular datasets with deduplication, missing value imputation, outlier handling, and text processing. Use when dealing with dirty data that has duplicates, nulls, or inconsistent formatting.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftrend-anomaly-causal-inference\u002Fenvironment\u002Fskills\u002Fdata_cleaning\u002FSKILL.md",[303],{"categoryNo":30,"categoryName":31,"taxonomyType":32,"taxonomyTypeDesc":33},[35,305,306,47,72,307,308,309,310,311,312,119,142,296,313,81],"data cleaning","deduplication","duplicates","datasets","clean","formatting","missing","imputation","tabular",{"skillNo":315,"name":316,"nameZh":17,"description":317,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":318,"categories":319,"keywords":320,"publishedAt":38},"01M6XET982DN9P10EA96W1YW1N","search-restaurants","Retrieve restaurants by city from the bundled dataset. Use this skill when recommending places to eat or validating dining options for a destination.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftravel-planning\u002Fenvironment\u002Fskills\u002Fsearch-restaurants\u002FSKILL.md",[],[321,322,323,72,324,325,278,121,326,81],"search","restaurants","search restaurants","places","options","eat",{"skillNo":328,"name":329,"nameZh":17,"description":330,"descriptionZh":17,"repositoryFullName":24,"ownerLogin":5,"ownerAvatarUrl":6,"ownerAccountNo":4,"ownerDisplayName":5,"repositoryNo":25,"starsCount":14,"licenseSpdxId":26,"licenseFileUrl":17,"sourceHtmlUrl":331,"categories":332,"keywords":333,"publishedAt":38},"01M6XET6HPCFGJB0EQM2FWWFJA","search-flights","Search flights by origin, destination, and departure date using the bundled flights dataset. Use this skill when proposing flight options or checking whether a route\u002Fdate combination exists.","https:\u002F\u002Fgithub.com\u002FWaaaalnut\u002Fskillsbench\u002Fblob\u002Fmain\u002Ftasks\u002Ftravel-planning\u002Fenvironment\u002Fskills\u002Fsearch-flights\u002FSKILL.md",[],[321,334,335,72,336,325,337,278,81],"flights","search flights","route","origin",18]