[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KKQWF72ZNCCD62KFSPS3SZ":3,"skill-same:01M6KKQWF72ZNCCD62KFSPS3SZ":66},{"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":22,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6KKQWF72ZNCCD62KFSPS3SZ","itinerary-planning",null,"How to construct a 7-day, pet-friendly travel itinerary for three Ohio cities starting from Minneapolis without using flights. Use this skill for planning, organizing the itinerary data, and formatting the final JSON output.","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\u002Fb4-skill-creator-gemini-3.1-flash-lite-preview\u002Ftravel-planning\u002Fitinerary-planning\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"planning","itinerary","itinerary planning","JSON Output","data","how","json","use","travel","flights","three","formatting","how to","PET","output","for","2026-11-10 10:12:30",false,"---\nname: itinerary-planning\ndescription: How to construct a 7-day, pet-friendly travel itinerary for three Ohio cities starting from Minneapolis without using flights. Use this skill for planning, organizing the itinerary data, and formatting the final JSON output.\n---\n# Itinerary Planning Skill\n\n## Overview\nThis skill outlines the process for building a 7-day, non-flight travel itinerary across three cities in Ohio, starting from Minneapolis.\n\n## Planning Steps\n1. **Identify Cities:** Select three cities in Ohio from `data\u002Fbackground\u002FcitySet.txt` or `citySet_with_states.txt`.\n2. **Timeline:** Plan the 7 days (March 17th - March 23rd, 2022).\n3. **Route:** Plan the driving route from Minneapolis to the first Ohio city and between subsequent Ohio cities.\n4. **Logistics:** Ensure accommodations are marked as pet-friendly (check `data\u002Faccommodations\u002F`).\n5. **Cuisine:** Plan daily meals (American, Mediterranean, Chinese, Italian).\n6. **Activities:** Select attractions from `data\u002Fattractions\u002F`.\n\n## JSON Format\nYour final output must be a single JSON object at `\u002Fapp\u002Foutput\u002Fitinerary.json` with:\n- `plan`: Array of 7 day objects (day, current_city, transportation, breakfast, lunch, dinner, attraction, accommodation).\n- `data_sources`: Array of file paths used for the plan.\n\n## Constraints\n- **NO FLIGHTS:** Use road travel only.\n- **BUDGET:** Maximum $5,100.\n- **PETS:** Accommodations MUST be pet-friendly.\n- **DURATION:** 7 days.\n- **LOCATIONS:** Three cities in Ohio.\n","# Itinerary Planning Skill\n\n## Overview\nThis skill outlines the process for building a 7-day, non-flight travel itinerary across three cities in Ohio, starting from Minneapolis.\n\n## Planning Steps\n1. **Identify Cities:** Select three cities in Ohio from `data\u002Fbackground\u002FcitySet.txt` or `citySet_with_states.txt`.\n2. **Timeline:** Plan the 7 days (March 17th - March 23rd, 2022).\n3. **Route:** Plan the driving route from Minneapolis to the first Ohio city and between subsequent Ohio cities.\n4. **Logistics:** Ensure accommodations are marked as pet-friendly (check `data\u002Faccommodations\u002F`).\n5. **Cuisine:** Plan daily meals (American, Mediterranean, Chinese, Italian).\n6. **Activities:** Select attractions from `data\u002Fattractions\u002F`.\n\n## JSON Format\nYour final output must be a single JSON object at `\u002Fapp\u002Foutput\u002Fitinerary.json` with:\n- `plan`: Array of 7 day objects (day, current_city, transportation, breakfast, lunch, dinner, attraction, accommodation).\n- `data_sources`: Array of file paths used for the plan.\n\n## Constraints\n- **NO FLIGHTS:** Use road travel only.\n- **BUDGET:** Maximum $5,100.\n- **PETS:** Accommodations MUST be pet-friendly.\n- **DURATION:** 7 days.\n- **LOCATIONS:** Three cities in Ohio.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-gemini-3.1-flash-lite-preview\u002Ftravel-planning\u002Fitinerary-planning",8,"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":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill itinerary-planning",{"tool":58,"command":59},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill itinerary-planning",{"tool":61,"command":62},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill itinerary-planning","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-gemini-3.1-flash-lite-preview\u002Ftravel-planning\u002Fitinerary-planning\u002FSKILL.md",true,[],[67,83,103,120,138,150],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":76,"publishedAt":39},"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",[73],{"categoryNo":74,"categoryName":75,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[77,78,79,80,81,82],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":90,"publishedAt":39},"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",[89],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[91,23,92,31,93,94,95,96,97,38,98,99,100,101,102],"workflow","planning workflow","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":39},"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",[],[110,111,112,113,114,115,116,31,117,32,93,118,38,119,23],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","you","database",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":39},"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",[],[127,37,128,38,129,130,29,131,132,133,134,135,118,136,137],"glm output","glm","tasks","metrics","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":39},"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":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[146,137,128,37,132,131,129,147,148,38,149],"glm calibration","setup","companion","metric",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":39},"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",[],[157,128,131,158,146,137,38,37,159,160,161,30,28,162,129],"glm basics","inspect","relevant","files","good","Evaluation"]