[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDR8YB0XVDFZ72D8REH100":3,"skill-same:01M6KDR8YB0XVDFZ72D8REH100":60},{"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":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":43,"ownerHtmlUrl":44,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":45,"install":46,"resources":59},"01M6KDR8YB0XVDFZ72D8REH100","travel-data-querying",null,"Querying travel planning datasets (cities, restaurants, accommodations, attractions, distances) for itinerary generation.","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-claude-opus-4-6\u002Ftravel-planning\u002Ftravel-data-querying\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32],"data","travel","travel data querying","planning","datasets","travel planning","generation","restaurants","itinerary","for","2026-11-10 10:12:30",false,"---\nname: travel-data-querying\ndescription: Querying travel planning datasets (cities, restaurants, accommodations, attractions, distances) for itinerary generation.\n---\n\n# Travel Data Querying\n\n## Dataset Structure\n\nAll data lives under `\u002Fapp\u002Fdata\u002F` with these files:\n\n### Cities\n- `background\u002FcitySet_with_states.txt` — Tab-separated: `CityName\\tState`\n- `background\u002FcitySet.txt` — City names only\n- `background\u002FstateSet.txt` — State names only\n\n### Restaurants\n- `restaurants\u002Fclean_restaurant_2022.csv`\n- Columns: `index, Name, City, Cuisines, Average Cost, Aggregate Rating`\n- Cuisines is a comma-separated string (e.g., \"Tea, Pizza, Indian, Seafood\")\n- Average Cost is per meal (numeric)\n\n### Accommodations\n- `accommodations\u002Fclean_accommodations_2022.csv`\n- Columns: `index, NAME, room type, price, minimum nights, review rate number, house_rules, maximum occupancy, city`\n- `house_rules` contains constraints like \"No pets\", \"No smoking\", \"No parties\", \"No children under 10\", \"No visitors\"\n- Price is per night\n\n### Attractions\n- `attractions\u002Fattractions.csv`\n- Columns: `Name, Latitude, Longitude, Address, Phone, Website, City`\n\n### Distances\n- `googleDistanceMatrix\u002Fdistance.csv`\n- Columns: `origin, destination, cost, duration, distance`\n- `cost` column is empty for driving (only populated for flights)\n- Duration is human-readable (e.g., \"3 hours 11 mins\")\n- Distance in km\n\n### Flights\n- `flights\u002Fclean_Flights_2022.csv` — Not used when flights are excluded\n\n## Querying Patterns\n\n### Find cities in a state\n```bash\ngrep -i \"ohio\" data\u002Fbackground\u002FcitySet_with_states.txt\n```\n\n### Find restaurants by city and cuisine\n```bash\ngrep -i \"cleveland\" data\u002Frestaurants\u002Fclean_restaurant_2022.csv | grep -i \"italian\"\n```\n\n### Find pet-friendly accommodations (exclude \"No pets\")\n```bash\ngrep -i \"cleveland\" data\u002Faccommodations\u002Fclean_accommodations_2022.csv | grep -iv \"no pets\"\n```\n\n### Find driving distances between cities\n```bash\ngrep -E \"^Cleveland,Dayton,\" data\u002FgoogleDistanceMatrix\u002Fdistance.csv\n```\n","# Travel Data Querying\n\n## Dataset Structure\n\nAll data lives under `\u002Fapp\u002Fdata\u002F` with these files:\n\n### Cities\n- `background\u002FcitySet_with_states.txt` — Tab-separated: `CityName\\tState`\n- `background\u002FcitySet.txt` — City names only\n- `background\u002FstateSet.txt` — State names only\n\n### Restaurants\n- `restaurants\u002Fclean_restaurant_2022.csv`\n- Columns: `index, Name, City, Cuisines, Average Cost, Aggregate Rating`\n- Cuisines is a comma-separated string (e.g., \"Tea, Pizza, Indian, Seafood\")\n- Average Cost is per meal (numeric)\n\n### Accommodations\n- `accommodations\u002Fclean_accommodations_2022.csv`\n- Columns: `index, NAME, room type, price, minimum nights, review rate number, house_rules, maximum occupancy, city`\n- `house_rules` contains constraints like \"No pets\", \"No smoking\", \"No parties\", \"No children under 10\", \"No visitors\"\n- Price is per night\n\n### Attractions\n- `attractions\u002Fattractions.csv`\n- Columns: `Name, Latitude, Longitude, Address, Phone, Website, City`\n\n### Distances\n- `googleDistanceMatrix\u002Fdistance.csv`\n- Columns: `origin, destination, cost, duration, distance`\n- `cost` column is empty for driving (only populated for flights)\n- Duration is human-readable (e.g., \"3 hours 11 mins\")\n- Distance in km\n\n### Flights\n- `flights\u002Fclean_Flights_2022.csv` — Not used when flights are excluded\n\n## Querying Patterns\n\n### Find cities in a state\n```bash\ngrep -i \"ohio\" data\u002Fbackground\u002FcitySet_with_states.txt\n```\n\n### Find restaurants by city and cuisine\n```bash\ngrep -i \"cleveland\" data\u002Frestaurants\u002Fclean_restaurant_2022.csv | grep -i \"italian\"\n```\n\n### Find pet-friendly accommodations (exclude \"No pets\")\n```bash\ngrep -i \"cleveland\" data\u002Faccommodations\u002Fclean_accommodations_2022.csv | grep -iv \"no pets\"\n```\n\n### Find driving distances between cities\n```bash\ngrep -E \"^Cleveland,Dayton,\" data\u002FgoogleDistanceMatrix\u002Fdistance.csv\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-opus-4-6\u002Ftravel-planning\u002Ftravel-data-querying",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":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill travel-data-querying",{"tool":52,"command":53},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill travel-data-querying",{"tool":55,"command":56},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill travel-data-querying","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-opus-4-6\u002Ftravel-planning\u002Ftravel-data-querying\u002FSKILL.md",true,[],[61,79,100,117,137,149],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":72,"publishedAt":33},"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",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[73,74,75,76,77,78],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":88,"publishedAt":33},"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",[85],{"categoryNo":86,"categoryName":87,"taxonomyType":70,"taxonomyTypeDesc":71},"01JBSEEDCAT000000000000023","Skill与智能体开发",[89,26,90,24,28,91,92,93,94,32,95,96,97,98,99],"workflow","planning workflow","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":33},"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",[],[107,108,109,110,30,111,112,24,113,114,28,115,32,116,26],"search","tool","dataset","bundled dataset search tool","Tools","up","look","flights","you","database",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":33},"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",[],[124,125,126,32,127,128,129,130,131,132,133,134,115,135,136],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":144,"publishedAt":33},"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",[143],{"categoryNo":86,"categoryName":87,"taxonomyType":70,"taxonomyTypeDesc":71},[145,136,126,125,131,130,127,146,147,32,148],"glm calibration","setup","companion","metric",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":33},"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",[],[156,126,130,157,145,136,32,125,158,159,160,161,162,163,127],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]