[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KM4DY820XHS9YTSK5QP5HD":3,"skill-same:01M6KM4DY820XHS9YTSK5QP5HD":86},{"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":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},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool",null,"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.","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\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32],"search","tool","dataset","bundled dataset search tool","planning","database","Tools","up","travel","look","flights","travel planning","you","restaurants","for","2026-11-10 10:12:30",false,"---\nname: bundled-dataset-search-tool\ndescription: 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.\n---\n\n# Overview\n\nThis skill provides six tools for searching the bundled dataset, always first use these tool scripts than writing code by yourself. \n\n# Installation\n\n**before using the tools, please install:**\n\n```bash\npip install pandas numpy requests\n```\n\n## Tool 1 :Search Accommodations\n\nFind accommodations for a specific city. The bundled script is located at scripts\u002Fsearch_accommodations.py. An Example:\n\n```python\nfrom search_accommodations import Accommodations\n\nacc = Accommodations()\nresult = acc.run(\"Seattle\")\nprint(result)\n```\n\n\n## Tool 2: Search Attractions\n\nQuery attractions for a given city.  The bundled script is located at scripts\u002Fsearch_attractions.py. An Example:\n\n```python\nfrom search_attractions import Attractions\n\nattractions = Attractions()\nprint(attractions.run(\"New York\"))\n```\n\n## Tool 3: Search Cities\n\nMap states to their cities from the background text file.  The bundled script is located at scripts\u002Fsearch_cities.py. An Example:\n\n```python\nfrom search_cities import Cities\n\ncities = Cities()\nprint(cities.run(\"California\"))\n```\n## Tool 4: Search Driving Distance\n\nLook up distance\u002Fduration between an origin and destination. The bundled script is located at scripts\u002Fsearch_driving_distance.py. An Example:\n\n```python\nfrom search_driving_distance import GoogleDistanceMatrix\n\nmatrix = GoogleDistanceMatrix()\nprint(matrix.run(\"Seattle\", \"Portland\", mode=\"driving\"))\n```\n\n## Tool 5: Search Flights\n\nFilter the cleaned flights CSV for specific routes and dates. The bundled script is located at scripts\u002Fsearch_flights.py. An Example:\n\n```python\nfrom search_flights import Flights\n\nflights = Flights()\nprint(flights.run(\"New York\", \"Los Angeles\", \"2022-01-15\"))\n```\n\n## Tool 6: Search Restaurants\n\nQuery restaurants for a given city. The bundled script is located at scripts\u002Fsearch_restaurants.py. An example:\n```python\nfrom search_restaurants import Restaurants\n\nrestaurants = Restaurants()\nprint(restaurants.run(\"San Francisco\"))\n```","# Overview\n\nThis skill provides six tools for searching the bundled dataset, always first use these tool scripts than writing code by yourself. \n\n# Installation\n\n**before using the tools, please install:**\n\n```bash\npip install pandas numpy requests\n```\n\n## Tool 1 :Search Accommodations\n\nFind accommodations for a specific city. The bundled script is located at scripts\u002Fsearch_accommodations.py. An Example:\n\n```python\nfrom search_accommodations import Accommodations\n\nacc = Accommodations()\nresult = acc.run(\"Seattle\")\nprint(result)\n```\n\n\n## Tool 2: Search Attractions\n\nQuery attractions for a given city.  The bundled script is located at scripts\u002Fsearch_attractions.py. An Example:\n\n```python\nfrom search_attractions import Attractions\n\nattractions = Attractions()\nprint(attractions.run(\"New York\"))\n```\n\n## Tool 3: Search Cities\n\nMap states to their cities from the background text file.  The bundled script is located at scripts\u002Fsearch_cities.py. An Example:\n\n```python\nfrom search_cities import Cities\n\ncities = Cities()\nprint(cities.run(\"California\"))\n```\n## Tool 4: Search Driving Distance\n\nLook up distance\u002Fduration between an origin and destination. The bundled script is located at scripts\u002Fsearch_driving_distance.py. An Example:\n\n```python\nfrom search_driving_distance import GoogleDistanceMatrix\n\nmatrix = GoogleDistanceMatrix()\nprint(matrix.run(\"Seattle\", \"Portland\", mode=\"driving\"))\n```\n\n## Tool 5: Search Flights\n\nFilter the cleaned flights CSV for specific routes and dates. The bundled script is located at scripts\u002Fsearch_flights.py. An Example:\n\n```python\nfrom search_flights import Flights\n\nflights = Flights()\nprint(flights.run(\"New York\", \"Los Angeles\", \"2022-01-15\"))\n```\n\n## Tool 6: Search Restaurants\n\nQuery restaurants for a given city. The bundled script is located at scripts\u002Fsearch_restaurants.py. An example:\n```python\nfrom search_restaurants import Restaurants\n\nrestaurants = Restaurants()\nprint(restaurants.run(\"San Francisco\"))\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool",7,"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 bundled-dataset-search-tool",{"tool":52,"command":53},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill bundled-dataset-search-tool",{"tool":55,"command":56},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill bundled-dataset-search-tool","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",true,[60,66,70,74,78,82],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"scripts\u002Fsearch_accommodations.py","SCRIPT","脚本",3525,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_accommodations.py",{"path":67,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":68,"sourceHtmlUrl":69},"scripts\u002Fsearch_attractions.py",2992,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_attractions.py",{"path":71,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":72,"sourceHtmlUrl":73},"scripts\u002Fsearch_cities.py",3133,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_cities.py",{"path":75,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":76,"sourceHtmlUrl":77},"scripts\u002Fsearch_driving_distance.py",5781,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_driving_distance.py",{"path":79,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002Fsearch_flights.py",4345,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_flights.py",{"path":83,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fsearch_restaurants.py",3597,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002Fscripts\u002Fsearch_restaurants.py",[87,105,126,146,158,173],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":98,"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",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[99,100,101,102,103,104],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":114,"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",[111],{"categoryNo":112,"categoryName":113,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000023","Skill与智能体开发",[22,115,116,26,29,117,118,119,32,120,121,122,123,124,125],"workflow","planning workflow","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"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",[],[133,134,135,30,136,137,138,139,140,141,142,143,144,32,145],"glm","output","glm output","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"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":153,"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",[152],{"categoryNo":112,"categoryName":113,"taxonomyType":96,"taxonomyTypeDesc":97},[154,145,133,155,32,134,140,139,136,156,157],"glm calibration","companion","setup","metric",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"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",[],[165,133,139,136,166,145,154,32,167,134,168,169,170,171,172],"glm basics","Evaluation","inspect","relevant","files","good","use","how",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":182,"publishedAt":33},"01M6KM3NKG0TNJGKN0HTYQ34GX","d3js-visualization","Build deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML\u002FSVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes\u002Fscales, legends, tooltips, or data-driven SVG output.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fstock-data-visualization\u002Fd3-visualization\u002FSKILL.md",[179],{"categoryNo":180,"categoryName":181,"taxonomyType":96,"taxonomyTypeDesc":97},"01JBSEEDCAT000000000000013","分析与可视化",[183,184,169,185,186,187,134,188,189,190,191,192,171,193,194,195,196,197,198,136],"d3js visualization","visualization","data-driven","standalone","with","SVG","d3","local","deterministic","html","png","visualizations","Charts","data","network","generate"]