[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KF2GZB16WXY24BQ8K2STRX":3,"skill-same:01M6KF2GZB16WXY24BQ8K2STRX":54},{"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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":32,"commitSha":33,"skillDirectory":34,"forksCount":35,"repositoryHtmlUrl":36,"repositoryHomepageUrl":37,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":39,"install":40,"resources":53},"01M6KF2GZB16WXY24BQ8K2STRX","route-planning",null,"Algorithmic planning of itineraries avoiding specific transport modes and allocating time based on budget constraints.","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\u002Ftravel-planning\u002Froute-planning\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26],"planning","route","route planning","Budget","constraints","itineraries","time","algorithmic","modes","2026-11-10 10:12:30",false,"---\nname: route-planning\ndescription: Algorithmic planning of itineraries avoiding specific transport modes and allocating time based on budget constraints.\n---\n\n# Route Planning and Itinerary Construction\n\nThis skill focuses on sequentially constructing travel plans by selecting daily activities and accommodations, avoiding certain travel modes (e.g., no flights), and managing budget constraints.\n\n## Key Concepts\n\n- Distance\u002FTravel time estimation without flights (driving or ground transportation only).\n- Day-by-day scheduling of activities (Meals, Attractions).\n- Maintaining budget variables iteratively across multiple days.\n\n## Implementation Details\n\n- Identify starting point and sequential destinations.\n- Use a driving distance matrix to calculate time\u002Fcosts.\n- Iteratively assign activities and ensure costs are kept under constraints.\n- Output final structured format (e.g., JSON).\n\n```python\nimport json\n\nitinerary = {\n    'plan': [],\n    'data_sources': ['data\u002Fdistance.csv', 'data\u002Fattractions.csv']\n}\n\n# Example daily assignment\nday_plan = {\n    'day': 1,\n    'current_city': 'Minneapolis',\n    'transportation': 'Self-driving: from Minneapolis to Cleveland',\n    'breakfast': 'Local Cafe',\n    'lunch': 'Bistro',\n    'dinner': 'Fine Dining',\n    'attraction': 'City Park;Museum;',\n    'accommodation': 'Pet-Friendly Inn'\n}\n\nitinerary['plan'].append(day_plan)\n\nwith open('output\u002Fitinerary.json', 'w') as f:\n    json.dump(itinerary, f, indent=2)\n```\n","# Route Planning and Itinerary Construction\n\nThis skill focuses on sequentially constructing travel plans by selecting daily activities and accommodations, avoiding certain travel modes (e.g., no flights), and managing budget constraints.\n\n## Key Concepts\n\n- Distance\u002FTravel time estimation without flights (driving or ground transportation only).\n- Day-by-day scheduling of activities (Meals, Attractions).\n- Maintaining budget variables iteratively across multiple days.\n\n## Implementation Details\n\n- Identify starting point and sequential destinations.\n- Use a driving distance matrix to calculate time\u002Fcosts.\n- Iteratively assign activities and ensure costs are kept under constraints.\n- Output final structured format (e.g., JSON).\n\n```python\nimport json\n\nitinerary = {\n    'plan': [],\n    'data_sources': ['data\u002Fdistance.csv', 'data\u002Fattractions.csv']\n}\n\n# Example daily assignment\nday_plan = {\n    'day': 1,\n    'current_city': 'Minneapolis',\n    'transportation': 'Self-driving: from Minneapolis to Cleveland',\n    'breakfast': 'Local Cafe',\n    'lunch': 'Bistro',\n    'dinner': 'Fine Dining',\n    'attraction': 'City Park;Museum;',\n    'accommodation': 'Pet-Friendly Inn'\n}\n\nitinerary['plan'].append(day_plan)\n\nwith open('output\u002Fitinerary.json', 'w') as f:\n    json.dump(itinerary, f, indent=2)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Ftravel-planning\u002Froute-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":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill route-planning",{"tool":46,"command":47},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill route-planning",{"tool":49,"command":50},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill route-planning","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\u002Ftravel-planning\u002Froute-planning\u002FSKILL.md",true,[],[55,73,96,114,134,146],{"skillNo":56,"name":57,"nameZh":6,"description":58,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":59,"categories":60,"keywords":66,"publishedAt":27},"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",[61],{"categoryNo":62,"categoryName":63,"taxonomyType":64,"taxonomyTypeDesc":65},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[67,68,69,70,71,72],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":82,"publishedAt":27},"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",[79],{"categoryNo":80,"categoryName":81,"taxonomyType":64,"taxonomyTypeDesc":65},"01JBSEEDCAT000000000000023","Skill与智能体开发",[83,18,84,85,86,87,88,89,90,91,92,93,94,95,23],"workflow","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":27},"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",[],[103,104,105,106,107,108,109,85,110,111,86,112,91,113,18],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":27},"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",[],[121,122,123,91,124,125,126,127,128,129,130,131,112,132,133],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"publishedAt":27},"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",[140],{"categoryNo":80,"categoryName":81,"taxonomyType":64,"taxonomyTypeDesc":65},[142,133,123,122,128,127,124,143,144,91,145],"glm calibration","setup","companion","metric",{"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":27},"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",[],[153,123,127,154,142,133,91,122,155,156,157,158,159,160,124],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]