[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KM51TMVXG79MMPCS6Y8G86":3,"skill-same:01M6KM51TMVXG79MMPCS6Y8G86":65},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow",null,"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.","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\u002Ftravel-planning-workflow\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],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","plan","user","itineraries","structured","essential","requirements","it","for","performing","2026-11-10 10:12:30",false,"---\nname: travel-planning-workflow\ndescription: 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.\n---\n\n# Overview\n\n**This skill provides a travel planning workflow. It is important to follow this workflow.**\n**IMPORTANT:Do not forget to perform final validation and refinement.**\n\n---\n\n\n# Travel Planning Workflow\n\n## Step 1: User Preference and Constraint Analysis\n\nExtract the user's requirements and implicit preferences, including: Trip duration, Number of travelers, Budget constraints, Transportation constraints (e.g., no flights), Special requirements (e.g., pet-friendly accommodations), Food preferences (e.g., American, Mediterranean, Chinese, Italian)\n\n**Default assumption:**\nIf not explicitly specified, each day should include at least one activity (i.e., no empty days).\n\n---\n\n## Step 2: Global Planning and Sequential Scheduling\n\n### 2.1 Global Skeleton Construction\n\n- Select candidate cities based on user constraints and dataset availability\n- Construct a preliminary travel route (considering distance and feasibility)\n- Allocate the total number of days across selected cities as a draft plan\n\n---\n\n### 2.2 Sequential Day-by-Day Planning\n\nGenerate the itinerary in chronological order:\n\nUsing the provided tools, and for each day:\n- **Assign one attraction per day**, unless the user explicitly indicates that they do not want to go out.\n- **Assign meals** based on cuisine preferences\n- **Assign accommodation** (must satisfy constraints such as pet-friendly)\n\n---\n\n### 2.3 Iterative Adjustment and Backtracking\n\nDuring planning, continuously validate feasibility. For example:\n\n- If a city does not have enough attractions → reduce its allocated days\n- If all cities combined still lack sufficient attractions → reselect cities\n- If constraints are violated (budget, accomodations, etc.) → adjust previous steps\n\nThis step introduces a **feedback loop**, allowing the system to revise earlier decisions.\n\n---\n\n## Step 3: Important!! Final Validation and Refinement\nBefore returning the final itinerary, the agent must perform an explicit final validation pass. The validation should check:\n- **All user constraints must be satisfied**, including transportation constraints (e.g., no flights), special requirements (e.g., pet-friendly accommodations, which should be explicitly reflected using keywords such as pet or animal), and food preferences (e.g., American, Mediterranean, Chinese, and Italian cuisines).\n- **Each day includes at least one attraction, including both the first day and last day** (unless the user explicitly indicates that they do not want to go out.)\n- **Travel transitions between cities are feasible**\n- **The itinerary format is complete and consistent**, with no missing or contradictory information.\n\n**Please refine the itinerary if any violations or inconsistencies are detected**\n\n\n\n\n","# Overview\n\n**This skill provides a travel planning workflow. It is important to follow this workflow.**\n**IMPORTANT:Do not forget to perform final validation and refinement.**\n\n---\n\n\n# Travel Planning Workflow\n\n## Step 1: User Preference and Constraint Analysis\n\nExtract the user's requirements and implicit preferences, including: Trip duration, Number of travelers, Budget constraints, Transportation constraints (e.g., no flights), Special requirements (e.g., pet-friendly accommodations), Food preferences (e.g., American, Mediterranean, Chinese, Italian)\n\n**Default assumption:**\nIf not explicitly specified, each day should include at least one activity (i.e., no empty days).\n\n---\n\n## Step 2: Global Planning and Sequential Scheduling\n\n### 2.1 Global Skeleton Construction\n\n- Select candidate cities based on user constraints and dataset availability\n- Construct a preliminary travel route (considering distance and feasibility)\n- Allocate the total number of days across selected cities as a draft plan\n\n---\n\n### 2.2 Sequential Day-by-Day Planning\n\nGenerate the itinerary in chronological order:\n\nUsing the provided tools, and for each day:\n- **Assign one attraction per day**, unless the user explicitly indicates that they do not want to go out.\n- **Assign meals** based on cuisine preferences\n- **Assign accommodation** (must satisfy constraints such as pet-friendly)\n\n---\n\n### 2.3 Iterative Adjustment and Backtracking\n\nDuring planning, continuously validate feasibility. For example:\n\n- If a city does not have enough attractions → reduce its allocated days\n- If all cities combined still lack sufficient attractions → reselect cities\n- If constraints are violated (budget, accomodations, etc.) → adjust previous steps\n\nThis step introduces a **feedback loop**, allowing the system to revise earlier decisions.\n\n---\n\n## Step 3: Important!! Final Validation and Refinement\nBefore returning the final itinerary, the agent must perform an explicit final validation pass. The validation should check:\n- **All user constraints must be satisfied**, including transportation constraints (e.g., no flights), special requirements (e.g., pet-friendly accommodations, which should be explicitly reflected using keywords such as pet or animal), and food preferences (e.g., American, Mediterranean, Chinese, and Italian cuisines).\n- **Each day includes at least one attraction, including both the first day and last day** (unless the user explicitly indicates that they do not want to go out.)\n- **Travel transitions between cities are feasible**\n- **The itinerary format is complete and consistent**, with no missing or contradictory information.\n\n**Please refine the itinerary if any violations or inconsistencies are detected**\n\n\n\n\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow",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":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill travel-planning-workflow",{"tool":57,"command":58},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill travel-planning-workflow",{"tool":60,"command":61},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill travel-planning-workflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",true,[],[66,82,100,120,132,147],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":75,"publishedAt":38},"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",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[76,77,78,79,80,81],"object counter","object","Computer Vision","computer","vision","image",{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":38},"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",[],[89,90,91,92,27,36,24,93,94,95,96,97,26,98,99],"search","tool","dataset","bundled dataset search tool","database","restaurants","Tools","you","up","look","flights",{"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":38},"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",[],[107,108,109,110,111,112,113,114,115,116,117,118,96,36,119],"glm","output","glm output","basics","processing","verifier","matching","guidance","root","json","metrics","tasks","calibration",{"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":127,"publishedAt":38},"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",[126],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[128,119,107,129,36,108,114,110,118,130,131],"glm calibration","companion","setup","metric",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":38},"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",[],[139,107,110,118,140,119,128,36,141,108,142,143,144,145,146],"glm basics","Evaluation","inspect","relevant","files","good","use","how",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":156,"publishedAt":38},"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",[153],{"categoryNo":154,"categoryName":155,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化",[157,158,143,159,160,161,108,162,163,164,165,166,145,167,168,169,170,171,172,118],"d3js visualization","visualization","data-driven","standalone","with","SVG","d3","local","deterministic","html","png","visualizations","Charts","data","network","generate"]