[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDB2ZDM9SMB4QYZ719K4T7":3,"skill-same:01M6KDB2ZDM9SMB4QYZ719K4T7":53},{"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":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":36,"ownerHtmlUrl":37,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":38,"install":39,"resources":52},"01M6KDB2ZDM9SMB4QYZ719K4T7","route-planning",null,"Plan multi-city road trip routes using distance matrices and 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-claude-haiku-4-5\u002Ftravel-planning\u002Froute-planning\u002FSKILL.md",[],[18,19,20,21,22,23,24,25],"planning","route","route planning","constraints","plan","matrices","multi","trip","2026-11-10 10:12:30",false,"---\nname: route-planning\ndescription: Plan multi-city road trip routes using distance matrices and constraints\n---\n\n# Route Planning Skill\n\n## Overview\nPlan efficient multi-city road trips with constraints like starting point, number of days, and no-fly requirements.\n\n## Core Strategy\n\n### Distance-Based Route Optimization\n- Use distance matrix to calculate travel times\u002Fdistances\n- Minimize backtracking between cities\n- Allocate days based on travel time and activities per city\n\n### Day Allocation Strategy\n- 1 day for initial travel to first city\n- Remaining days distributed across 3 Ohio cities\n- Typical allocation: 2-2-2 days or 2-2-3 depending on distances\n- Last day may include return travel (not full return in this case)\n\n## Python Code Example\n\n```python\nfrom typing import List, Dict, Tuple\nfrom itertools import permutations\n\ndef find_best_route(\n    start_city: str,\n    num_cities: int,\n    available_cities: List[str],\n    distance_matrix: Dict[str, Dict[str, float]]\n) -> Tuple[List[str], float]:\n    \"\"\"\n    Find optimal route visiting num_cities starting from start_city.\n    Returns (route, total_distance)\n    \"\"\"\n    best_route = None\n    best_distance = float('inf')\n\n    # Try all permutations of available cities\n    for perm in permutations(available_cities[:num_cities]):\n        route = [start_city] + list(perm)\n        total_distance = calculate_route_distance(route, distance_matrix)\n\n        if total_distance \u003C best_distance:\n            best_distance = total_distance\n            best_route = route\n\n    return best_route, best_distance\n\ndef calculate_route_distance(route: List[str], distance_matrix: Dict) -> float:\n    \"\"\"Calculate total distance for a route\"\"\"\n    total = 0\n    for i in range(len(route) - 1):\n        from_city = route[i]\n        to_city = route[i + 1]\n\n        if from_city in distance_matrix and to_city in distance_matrix[from_city]:\n            total += float(distance_matrix[from_city][to_city])\n        else:\n            return float('inf')  # Invalid route\n\n    return total\n\ndef allocate_days_to_cities(\n    num_days: int,\n    num_cities: int\n) -> List[int]:\n    \"\"\"\n    Allocate days across cities.\n    First city gets 1 day (travel day), remaining split among other cities.\n    \"\"\"\n    # First city gets 1 day for arrival\u002Ftravel\n    remaining_days = num_days - 1\n    remaining_cities = num_cities\n\n    days_per_city = [1]  # First city\n\n    # Distribute remaining days\n    base_days = remaining_days \u002F\u002F remaining_cities\n    extra_days = remaining_days % remaining_cities\n\n    for i in range(remaining_cities):\n        days = base_days + (1 if i \u003C extra_days else 0)\n        days_per_city.append(days)\n\n    return days_per_city\n\ndef estimate_travel_time(distance: float, mph: float = 60) -> float:\n    \"\"\"Estimate driving time in hours\"\"\"\n    return distance \u002F mph\n```\n\n## Route Selection Considerations\n\n1. **Starting Point**: Minneapolis, Minnesota\n2. **Destination**: 3 Ohio cities (Cleveland, Columbus, Cincinnati are major options)\n3. **No Flights**: Must use self-driving\u002Fground transport\n4. **Distance Budget**: 7-day trip with reasonable daily driving (6-8 hours max)\n5. **Circular vs Linear**: Consider if returning to Minneapolis or ending in Ohio\n\n## Typical Route Pattern\n\n```\nDay 1: Minneapolis → City 1 (travel day, possibly long drive)\nDay 2-3: City 1 (explore attractions, dining)\nDay 4: City 1 → City 2 (short\u002Fmedium drive)\nDay 5-6: City 2 (explore attractions, dining)\nDay 7: City 2 → City 3 OR City 2 (final day)\n```\n\n## Usage Example\n\n```python\nroute, distance = find_best_route(\n    start_city='Minneapolis',\n    num_cities=3,\n    available_cities=['Cleveland', 'Columbus', 'Cincinnati'],\n    distance_matrix=distances\n)\n\ndays_allocation = allocate_days_to_cities(\n    num_days=7,\n    num_cities=3\n)\n```\n","# Route Planning Skill\n\n## Overview\nPlan efficient multi-city road trips with constraints like starting point, number of days, and no-fly requirements.\n\n## Core Strategy\n\n### Distance-Based Route Optimization\n- Use distance matrix to calculate travel times\u002Fdistances\n- Minimize backtracking between cities\n- Allocate days based on travel time and activities per city\n\n### Day Allocation Strategy\n- 1 day for initial travel to first city\n- Remaining days distributed across 3 Ohio cities\n- Typical allocation: 2-2-2 days or 2-2-3 depending on distances\n- Last day may include return travel (not full return in this case)\n\n## Python Code Example\n\n```python\nfrom typing import List, Dict, Tuple\nfrom itertools import permutations\n\ndef find_best_route(\n    start_city: str,\n    num_cities: int,\n    available_cities: List[str],\n    distance_matrix: Dict[str, Dict[str, float]]\n) -> Tuple[List[str], float]:\n    \"\"\"\n    Find optimal route visiting num_cities starting from start_city.\n    Returns (route, total_distance)\n    \"\"\"\n    best_route = None\n    best_distance = float('inf')\n\n    # Try all permutations of available cities\n    for perm in permutations(available_cities[:num_cities]):\n        route = [start_city] + list(perm)\n        total_distance = calculate_route_distance(route, distance_matrix)\n\n        if total_distance \u003C best_distance:\n            best_distance = total_distance\n            best_route = route\n\n    return best_route, best_distance\n\ndef calculate_route_distance(route: List[str], distance_matrix: Dict) -> float:\n    \"\"\"Calculate total distance for a route\"\"\"\n    total = 0\n    for i in range(len(route) - 1):\n        from_city = route[i]\n        to_city = route[i + 1]\n\n        if from_city in distance_matrix and to_city in distance_matrix[from_city]:\n            total += float(distance_matrix[from_city][to_city])\n        else:\n            return float('inf')  # Invalid route\n\n    return total\n\ndef allocate_days_to_cities(\n    num_days: int,\n    num_cities: int\n) -> List[int]:\n    \"\"\"\n    Allocate days across cities.\n    First city gets 1 day (travel day), remaining split among other cities.\n    \"\"\"\n    # First city gets 1 day for arrival\u002Ftravel\n    remaining_days = num_days - 1\n    remaining_cities = num_cities\n\n    days_per_city = [1]  # First city\n\n    # Distribute remaining days\n    base_days = remaining_days \u002F\u002F remaining_cities\n    extra_days = remaining_days % remaining_cities\n\n    for i in range(remaining_cities):\n        days = base_days + (1 if i \u003C extra_days else 0)\n        days_per_city.append(days)\n\n    return days_per_city\n\ndef estimate_travel_time(distance: float, mph: float = 60) -> float:\n    \"\"\"Estimate driving time in hours\"\"\"\n    return distance \u002F mph\n```\n\n## Route Selection Considerations\n\n1. **Starting Point**: Minneapolis, Minnesota\n2. **Destination**: 3 Ohio cities (Cleveland, Columbus, Cincinnati are major options)\n3. **No Flights**: Must use self-driving\u002Fground transport\n4. **Distance Budget**: 7-day trip with reasonable daily driving (6-8 hours max)\n5. **Circular vs Linear**: Consider if returning to Minneapolis or ending in Ohio\n\n## Typical Route Pattern\n\n```\nDay 1: Minneapolis → City 1 (travel day, possibly long drive)\nDay 2-3: City 1 (explore attractions, dining)\nDay 4: City 1 → City 2 (short\u002Fmedium drive)\nDay 5-6: City 2 (explore attractions, dining)\nDay 7: City 2 → City 3 OR City 2 (final day)\n```\n\n## Usage Example\n\n```python\nroute, distance = find_best_route(\n    start_city='Minneapolis',\n    num_cities=3,\n    available_cities=['Cleveland', 'Columbus', 'Cincinnati'],\n    distance_matrix=distances\n)\n\ndays_allocation = allocate_days_to_cities(\n    num_days=7,\n    num_cities=3\n)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-haiku-4-5\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":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill route-planning",{"tool":45,"command":46},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill route-planning",{"tool":48,"command":49},"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-claude-haiku-4-5\u002Ftravel-planning\u002Froute-planning\u002FSKILL.md",true,[],[54,72,95,113,133,145],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":65,"publishedAt":26},"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",[60],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[66,67,68,69,70,71],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":81,"publishedAt":26},"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",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000023","Skill与智能体开发",[82,18,83,84,85,86,87,88,22,89,90,91,92,93,94],"workflow","planning workflow","travel","travel planning","travel planning workflow","structured","performing","for","user","it","requirements","essential","itineraries",{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":26},"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",[],[102,103,104,105,106,107,108,84,109,110,85,111,89,112,18],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":26},"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",[],[120,121,122,89,123,124,125,126,127,128,129,130,111,131,132],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":26},"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",[139],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},[141,132,122,121,127,126,123,142,143,89,144],"glm calibration","setup","companion","metric",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":26},"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",[],[152,122,126,153,141,132,89,121,154,155,156,157,158,159,123],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]