[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFCNEBSXH6RT3219YSHCP2":3,"skill-same:01M6KFCNEBSXH6RT3219YSHCP2":58},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":41,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M6KFCNEBSXH6RT3219YSHCP2","run2_itinerary_optimization",null,"Optimized itinerary building with cuisine distribution, cost tracking, and multi-city routing for pet-friendly travel.","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\u002Fb2-self-feedback-claude-haiku-4-5\u002Ftravel-planning\u002Frun2_itinerary_optimization\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30],"Optimization","itinerary","run2 itinerary optimization","routing","tracking","cost","travel","distribution","cost tracking","PET","multi","with","for","2026-11-10 10:12:30",false,"---\nname: run2_itinerary_optimization\ndescription: Optimized itinerary building with cuisine distribution, cost tracking, and multi-city routing for pet-friendly travel.\n---\n\n# Optimized Multi-City Itinerary Planning\n\n## Overview\nThis skill addresses:\n- Balanced cuisine distribution across 7 days\n- Cost tracking and budget adherence\n- Optimal city sequencing and travel timing\n- Pet-friendly accommodation prioritization\n- Realistic meal and attraction planning\n\n## Route Optimization for 3 Ohio Cities\n\n### Recommended Route\nMinneapolis → Cleveland → Columbus → Cincinnati → Minneapolis\n\n**Rationale:**\n- Cleveland is north, good entry point\n- Columbus is central, on route to Cincinnati\n- Cincinnati is southwest, good final city before returning\n- Minimizes backtracking\n\n### Day Allocation\n- Day 1: Travel Minneapolis → Cleveland (arrival dinner)\n- Days 2-3: Cleveland (full exploration)\n- Day 4: Travel Cleveland → Columbus (arrival dinner)\n- Day 5: Columbus (full exploration)\n- Day 6: Travel Columbus → Cincinnati (arrival dinner)\n- Day 7: Cincinnati & return to Minneapolis (depart dinner\u002Flunch only)\n\n## Cuisine Distribution Strategy\n\n### Assignment Pattern\nAcross 7 days, ensure all 4 cuisines appear:\n- **American**: 2-3 occurrences (breakfast Day 1, lunch Day 5, dinner Day 7)\n- **Italian**: 2 occurrences (dinner Days 2, 6)\n- **Chinese**: 2 occurrences (lunch Days 3, 7)\n- **Mediterranean**: 1 occurrence (lunch Day 4)\n\n### Implementation\n```python\ncuisine_schedule = {\n    1: {\"breakfast\": \"American\", \"lunch\": \"American\", \"dinner\": \"Chinese\"},\n    2: {\"breakfast\": \"Italian\", \"lunch\": \"Mediterranean\", \"dinner\": \"Italian\"},\n    3: {\"breakfast\": \"Chinese\", \"lunch\": \"American\", \"dinner\": \"American\"},\n    4: {\"breakfast\": \"Mediterranean\", \"lunch\": \"Mediterranean\", \"dinner\": \"Italian\"},\n    5: {\"breakfast\": \"American\", \"lunch\": \"Italian\", \"dinner\": \"Chinese\"},\n    6: {\"breakfast\": \"Italian\", \"lunch\": \"Chinese\", \"dinner\": \"Italian\"},\n    7: {\"breakfast\": \"American\", \"lunch\": \"Chinese\", \"dinner\": \"-\"}\n}\n```\n\n## Cost Tracking\n\n### Budget Breakdown ($5,100 for 2 people, 7 days)\n- **Accommodations**: 6 nights × $120-180\u002Fnight = $720-1,080\n- **Meals**: 7 days × $80-120\u002Fday = $560-840\n- **Attractions**: $200-300 total (entry fees)\n- **Gas\u002FTransportation**: $150-200 (est. 1,000 miles round trip)\n- **Buffer**: $200-500\n\n### Daily Budgets\n```python\ndaily_budget = {\n    \"accommodation_per_night\": 150,  # per night for 2 people\n    \"meals_per_day\": 100,  # ~$17 per person per meal\n    \"attractions_per_day\": 30-50,\n    \"transport_per_day\": 20-30\n}\n```\n\n## Meal Planning Constraints\n\n1. **No Restaurants Repeated Across Days**\n   - Even if good, pick different one next time\n   - Keep cuisine diversity high\n\n2. **Format Rule**\n   - Include restaurant name + cuisine type + city\n   - Example: \"Italian at Via Cento, Cleveland\"\n   - Never: Just restaurant name or just cuisine name\n\n3. **Travel Days**\n   - Breakfast: lighter meal in departure city\n   - Lunch: en route or arrival city\n   - Dinner: arrival city (establish evening routine)\n\n## Attraction Selection\n\n### Strategy\n- 2-3 attractions per day minimum\n- Mix types: museums, parks, historic sites, food markets\n- Prioritize based on:\n  1. Availability in city\n  2. Pet-friendly (where applicable - parks over indoor museums)\n  3. Rating\u002Fpopularity\n  4. Distance from accommodation\n\n### Format\n- List attractions separated by semicolon\n- **Must end with semicolon**: \"Attraction1;Attraction2;\"\n- Never end without semicolon or without enough attractions\n\n## Accommodation Selection Priority\n\n1. Pet-friendly verification (explicitly no \"No pets\")\n2. Price within budget ($100-200\u002Fnight)\n3. Rating > 4.0\n4. Room type: Entire home\u002Fapt > Private room > Shared room\n5. Minimum nights requirement = 1\n\n### Naming Convention\n- Include: \"Pet-friendly\" + accommodation type + city name\n- Example: \"Pet-friendly Studio Apartment, Cleveland\"\n- Avoid: Excessive special characters, Unicode issues\n\n## Travel Day Optimization\n\n```python\ndef plan_travel_day(origin_city, dest_city):\n    \"\"\"Structure for travel days\"\"\"\n    return {\n        \"current_city\": f\"from {origin_city} to {dest_city}\",\n        \"transportation\": f\"Self-driving: from {origin_city} to {dest_city}\",\n        \"breakfast\": \"meal in origin city\",\n        \"lunch\": \"meal en route or early arrival\",\n        \"dinner\": \"meal in destination city\",\n        \"attraction\": \"light attractions in destination\",\n        \"accommodation\": \"in destination city\"\n    }\n```\n\n## Quality Assurance Checklist\n\nBefore finalizing each day:\n- [ ] Exactly one city or \"from X to Y\" format\n- [ ] Transportation includes direction and mode\n- [ ] All meal fields filled (no blank values)\n- [ ] Cuisines match assigned schedule\n- [ ] Attractions string ends with semicolon\n- [ ] Accommodation marked pet-friendly\n- [ ] No restaurant repeats within 2 days\n- [ ] Budget for day stays within limits\n","# Optimized Multi-City Itinerary Planning\n\n## Overview\nThis skill addresses:\n- Balanced cuisine distribution across 7 days\n- Cost tracking and budget adherence\n- Optimal city sequencing and travel timing\n- Pet-friendly accommodation prioritization\n- Realistic meal and attraction planning\n\n## Route Optimization for 3 Ohio Cities\n\n### Recommended Route\nMinneapolis → Cleveland → Columbus → Cincinnati → Minneapolis\n\n**Rationale:**\n- Cleveland is north, good entry point\n- Columbus is central, on route to Cincinnati\n- Cincinnati is southwest, good final city before returning\n- Minimizes backtracking\n\n### Day Allocation\n- Day 1: Travel Minneapolis → Cleveland (arrival dinner)\n- Days 2-3: Cleveland (full exploration)\n- Day 4: Travel Cleveland → Columbus (arrival dinner)\n- Day 5: Columbus (full exploration)\n- Day 6: Travel Columbus → Cincinnati (arrival dinner)\n- Day 7: Cincinnati & return to Minneapolis (depart dinner\u002Flunch only)\n\n## Cuisine Distribution Strategy\n\n### Assignment Pattern\nAcross 7 days, ensure all 4 cuisines appear:\n- **American**: 2-3 occurrences (breakfast Day 1, lunch Day 5, dinner Day 7)\n- **Italian**: 2 occurrences (dinner Days 2, 6)\n- **Chinese**: 2 occurrences (lunch Days 3, 7)\n- **Mediterranean**: 1 occurrence (lunch Day 4)\n\n### Implementation\n```python\ncuisine_schedule = {\n    1: {\"breakfast\": \"American\", \"lunch\": \"American\", \"dinner\": \"Chinese\"},\n    2: {\"breakfast\": \"Italian\", \"lunch\": \"Mediterranean\", \"dinner\": \"Italian\"},\n    3: {\"breakfast\": \"Chinese\", \"lunch\": \"American\", \"dinner\": \"American\"},\n    4: {\"breakfast\": \"Mediterranean\", \"lunch\": \"Mediterranean\", \"dinner\": \"Italian\"},\n    5: {\"breakfast\": \"American\", \"lunch\": \"Italian\", \"dinner\": \"Chinese\"},\n    6: {\"breakfast\": \"Italian\", \"lunch\": \"Chinese\", \"dinner\": \"Italian\"},\n    7: {\"breakfast\": \"American\", \"lunch\": \"Chinese\", \"dinner\": \"-\"}\n}\n```\n\n## Cost Tracking\n\n### Budget Breakdown ($5,100 for 2 people, 7 days)\n- **Accommodations**: 6 nights × $120-180\u002Fnight = $720-1,080\n- **Meals**: 7 days × $80-120\u002Fday = $560-840\n- **Attractions**: $200-300 total (entry fees)\n- **Gas\u002FTransportation**: $150-200 (est. 1,000 miles round trip)\n- **Buffer**: $200-500\n\n### Daily Budgets\n```python\ndaily_budget = {\n    \"accommodation_per_night\": 150,  # per night for 2 people\n    \"meals_per_day\": 100,  # ~$17 per person per meal\n    \"attractions_per_day\": 30-50,\n    \"transport_per_day\": 20-30\n}\n```\n\n## Meal Planning Constraints\n\n1. **No Restaurants Repeated Across Days**\n   - Even if good, pick different one next time\n   - Keep cuisine diversity high\n\n2. **Format Rule**\n   - Include restaurant name + cuisine type + city\n   - Example: \"Italian at Via Cento, Cleveland\"\n   - Never: Just restaurant name or just cuisine name\n\n3. **Travel Days**\n   - Breakfast: lighter meal in departure city\n   - Lunch: en route or arrival city\n   - Dinner: arrival city (establish evening routine)\n\n## Attraction Selection\n\n### Strategy\n- 2-3 attractions per day minimum\n- Mix types: museums, parks, historic sites, food markets\n- Prioritize based on:\n  1. Availability in city\n  2. Pet-friendly (where applicable - parks over indoor museums)\n  3. Rating\u002Fpopularity\n  4. Distance from accommodation\n\n### Format\n- List attractions separated by semicolon\n- **Must end with semicolon**: \"Attraction1;Attraction2;\"\n- Never end without semicolon or without enough attractions\n\n## Accommodation Selection Priority\n\n1. Pet-friendly verification (explicitly no \"No pets\")\n2. Price within budget ($100-200\u002Fnight)\n3. Rating > 4.0\n4. Room type: Entire home\u002Fapt > Private room > Shared room\n5. Minimum nights requirement = 1\n\n### Naming Convention\n- Include: \"Pet-friendly\" + accommodation type + city name\n- Example: \"Pet-friendly Studio Apartment, Cleveland\"\n- Avoid: Excessive special characters, Unicode issues\n\n## Travel Day Optimization\n\n```python\ndef plan_travel_day(origin_city, dest_city):\n    \"\"\"Structure for travel days\"\"\"\n    return {\n        \"current_city\": f\"from {origin_city} to {dest_city}\",\n        \"transportation\": f\"Self-driving: from {origin_city} to {dest_city}\",\n        \"breakfast\": \"meal in origin city\",\n        \"lunch\": \"meal en route or early arrival\",\n        \"dinner\": \"meal in destination city\",\n        \"attraction\": \"light attractions in destination\",\n        \"accommodation\": \"in destination city\"\n    }\n```\n\n## Quality Assurance Checklist\n\nBefore finalizing each day:\n- [ ] Exactly one city or \"from X to Y\" format\n- [ ] Transportation includes direction and mode\n- [ ] All meal fields filled (no blank values)\n- [ ] Cuisines match assigned schedule\n- [ ] Attractions string ends with semicolon\n- [ ] Accommodation marked pet-friendly\n- [ ] No restaurant repeats within 2 days\n- [ ] Budget for day stays within limits\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-haiku-4-5\u002Ftravel-planning\u002Frun2_itinerary_optimization",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":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_itinerary_optimization",{"tool":50,"command":51},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_itinerary_optimization",{"tool":53,"command":54},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_itinerary_optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-haiku-4-5\u002Ftravel-planning\u002Frun2_itinerary_optimization\u002FSKILL.md",true,[],[59,77,100,118,138,150],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":70,"publishedAt":31},"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",[65],{"categoryNo":66,"categoryName":67,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[71,72,73,74,75,76],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":86,"publishedAt":31},"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",[83],{"categoryNo":84,"categoryName":85,"taxonomyType":68,"taxonomyTypeDesc":69},"01JBSEEDCAT000000000000023","Skill与智能体开发",[87,88,89,24,90,91,92,93,94,30,95,96,97,98,99],"workflow","planning","planning workflow","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":31},"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",[],[107,108,109,110,111,112,113,24,114,115,90,116,30,117,88],"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":31},"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",[],[125,126,127,30,128,129,130,131,132,133,134,135,116,136,137],"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":31},"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":84,"categoryName":85,"taxonomyType":68,"taxonomyTypeDesc":69},[146,137,127,126,132,131,128,147,148,30,149],"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":31},"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",[],[157,127,131,158,146,137,30,126,159,160,161,162,163,164,128],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]