[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KF01YP7JB43MP65BEV2TJM":3,"skill-same:01M6KF01YP7JB43MP65BEV2TJM":57},{"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":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":40,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M6KF01YP7JB43MP65BEV2TJM","python-datetime-metrics",null,"Parse datetime strings and compute time durations\u002Faverages for datasets (like PR merge times) in Python.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fgithub-repo-analytics\u002Fjson-processing-python\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29],"python","metrics","datetime","python datetime metrics","merge","pr","datasets","compute","time","pr merge","strings","for","2026-11-10 10:12:30",false,"---\nname: python-datetime-metrics\ndescription: Parse datetime strings and compute time durations\u002Faverages for datasets (like PR merge times) in Python.\n---\n\n# Python Datetime Metrics\n\nWhen working with GitHub APIs or any JSON data, you often need to parse ISO-8601 formatted datetime strings and compute derived metrics, such as \"average time to merge\" or \"issue resolution duration\".\n\n## Installation \u002F Setup\nBuilt-in `datetime` module is all you need.\n\n## Key Concepts\n- `datetime.strptime()` or `datetime.fromisoformat()` can convert strings to datetime objects.\n- Subtracting two `datetime` objects yields a `timedelta` object.\n- A `timedelta` object can be converted to numeric seconds or days using `.total_seconds()` or `.days`.\n\n## Code Example\n\n```python\nfrom datetime import datetime\n\ndef calculate_avg_merge_days(prs):\n    merge_times = []\n    \n    for pr in prs:\n        # Check if the PR was actually merged\n        if pr.get('pull_request', {}).get('merged_at'):\n            created_at_str = pr['created_at']\n            merged_at_str = pr['pull_request']['merged_at']\n            \n            # Use replace('Z', '+00:00') to handle standard ISO-8601 strings from APIs\n            created_at = datetime.fromisoformat(created_at_str.replace('Z', '+00:00'))\n            merged_at = datetime.fromisoformat(merged_at_str.replace('Z', '+00:00'))\n            \n            # Calculate difference in days (as a float)\n            diff_days = (merged_at - created_at).total_seconds() \u002F (24 * 3600)\n            merge_times.append(diff_days)\n            \n    if not merge_times:\n        return 0.0\n        \n    avg_days = sum(merge_times) \u002F len(merge_times)\n    # Round to 1 decimal place\n    return round(avg_days, 1)\n\n# Example output:\n# avg_days = calculate_avg_merge_days(pr_list)\n# print(f\"Average merge time: {avg_days} days\")\n```\n\n## Best Practices\n- Consider timezone-aware parsing. `replace('Z', '+00:00')` works well for basic UTC strings from GitHub.\n- Guard against divide-by-zero errors. Always check `if not data_points` before averaging.\n- Remember `merged_at` vs `closed_at`: closed does not mean merged. Ensure you pick the right metric!\n","# Python Datetime Metrics\n\nWhen working with GitHub APIs or any JSON data, you often need to parse ISO-8601 formatted datetime strings and compute derived metrics, such as \"average time to merge\" or \"issue resolution duration\".\n\n## Installation \u002F Setup\nBuilt-in `datetime` module is all you need.\n\n## Key Concepts\n- `datetime.strptime()` or `datetime.fromisoformat()` can convert strings to datetime objects.\n- Subtracting two `datetime` objects yields a `timedelta` object.\n- A `timedelta` object can be converted to numeric seconds or days using `.total_seconds()` or `.days`.\n\n## Code Example\n\n```python\nfrom datetime import datetime\n\ndef calculate_avg_merge_days(prs):\n    merge_times = []\n    \n    for pr in prs:\n        # Check if the PR was actually merged\n        if pr.get('pull_request', {}).get('merged_at'):\n            created_at_str = pr['created_at']\n            merged_at_str = pr['pull_request']['merged_at']\n            \n            # Use replace('Z', '+00:00') to handle standard ISO-8601 strings from APIs\n            created_at = datetime.fromisoformat(created_at_str.replace('Z', '+00:00'))\n            merged_at = datetime.fromisoformat(merged_at_str.replace('Z', '+00:00'))\n            \n            # Calculate difference in days (as a float)\n            diff_days = (merged_at - created_at).total_seconds() \u002F (24 * 3600)\n            merge_times.append(diff_days)\n            \n    if not merge_times:\n        return 0.0\n        \n    avg_days = sum(merge_times) \u002F len(merge_times)\n    # Round to 1 decimal place\n    return round(avg_days, 1)\n\n# Example output:\n# avg_days = calculate_avg_merge_days(pr_list)\n# print(f\"Average merge time: {avg_days} days\")\n```\n\n## Best Practices\n- Consider timezone-aware parsing. `replace('Z', '+00:00')` works well for basic UTC strings from GitHub.\n- Guard against divide-by-zero errors. Always check `if not data_points` before averaging.\n- Remember `merged_at` vs `closed_at`: closed does not mean merged. Ensure you pick the right metric!\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fgithub-repo-analytics\u002Fjson-processing-python",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":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill python-datetime-metrics",{"tool":49,"command":50},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill python-datetime-metrics",{"tool":52,"command":53},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill python-datetime-metrics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fgithub-repo-analytics\u002Fjson-processing-python\u002FSKILL.md",true,[],[58,76,100,118,137,149],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":69,"publishedAt":30},"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",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[70,71,72,73,74,75],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":85,"publishedAt":30},"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",[82],{"categoryNo":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发",[86,87,88,89,90,91,92,93,94,29,95,96,97,98,99],"workflow","planning","planning workflow","travel","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":30},"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",[],[107,108,109,110,111,112,113,89,114,115,90,116,29,117,87],"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":30},"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",[],[125,126,127,29,128,19,129,130,131,132,133,134,116,135,136],"glm output","output","glm","tasks","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":30},"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":83,"categoryName":84,"taxonomyType":67,"taxonomyTypeDesc":68},[145,136,127,126,131,130,128,146,147,29,148],"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":30},"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",[],[156,127,130,157,145,136,29,126,158,159,160,161,162,163,128],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]