[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGRVGYDRVM1F9WRBSMEGAJ":3,"skill-same:01M6KGRVGYDRVM1F9WRBSMEGAJ":59},{"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":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":42,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":44,"install":45,"resources":58},"01M6KGRVGYDRVM1F9WRBSMEGAJ","Calculate Average PR Merge Time",null,"Compute the average number of days from PR creation to merge for all merged PRs in December 2024, using correct timestamp parsing and filtering.","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\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Frun3_Calculate-Average-PR-Merge-Time\u002FSKILL.md",[],[18,19,20,21,22,5,23,24,25,26,27,28,29,30,31],"merge","pr","calculate","time","pr merge","parsing","correct","timestamp","filtering","compute","pr creation","creation","number","for","2026-11-10 10:12:30",false,"---\nname: Calculate Average PR Merge Time\ndescription: Compute the average number of days from PR creation to merge for all merged PRs in December 2024, using correct timestamp parsing and filtering.\n---\n\n# Calculate Average PR Merge Time\n\n## Overview\nCalculate the average time-to-merge in days (rounded to 1 decimal place) for PRs that were successfully merged during December 2024.\n\n## Step 1: Filter Merged PRs Only\nFrom the PR query results, identify only PRs where:\n- `mergedAt` is NOT null\n- `state` equals `\"MERGED\"` (verify this is the actual state value)\n\nDo NOT include closed-but-unmerged or open PRs in this calculation.\n\n## Step 2: Parse Timestamps Correctly\nFor each merged PR, extract:\n- `createdAt`: The PR creation timestamp (ISO-8601 format, e.g., `2024-12-15T10:30:45Z`)\n- `mergedAt`: The PR merge timestamp (ISO-8601 format)\n\n**Critical:** Use the **creation date** as the start point, not any other date field.\n\n## Step 3: Convert to Days\nFor each PR:\n1. Parse both timestamps as datetime objects (handle timezone info — typically UTC)\n2. Calculate the difference: `mergedAt - createdAt`\n3. Convert to total days (may be a float with decimal)\n4. Example: If a PR was created on 2024-12-01 at 00:00:00 and merged on 2024-12-02 at 12:00:00, that's 1.5 days\n\n## Step 4: Compute Average\n- Sum all day values from step 3\n- Divide by the count of merged PRs\n- Round the result to exactly 1 decimal place (e.g., 1.2, 15.8, 0.5)\n\n## Step 5: Validation Check\nIf your average is significantly higher than expected (e.g., 16+ days):\n- Verify you're only including merged PRs (check `mergedAt != null`)\n- Confirm you're measuring from `createdAt` (not some other field)\n- Check if any timestamp parsing errors are inflating the values\n- Inspect a few sample PRs manually to spot-check the calculation\n\n## Example Pseudocode\n```\nmerged_prs = [pr for pr in prs if pr.mergedAt is not null and pr.state == \"MERGED\"]\ndays_list = []\nfor pr in merged_prs:\n  created = parse_datetime(pr.createdAt)\n  merged = parse_datetime(pr.mergedAt)\n  days = (merged - created).total_seconds() \u002F 86400\n  days_list.append(days)\navg = sum(days_list) \u002F len(days_list)\nrounded_avg = round(avg, 1)\n```","# Calculate Average PR Merge Time\n\n## Overview\nCalculate the average time-to-merge in days (rounded to 1 decimal place) for PRs that were successfully merged during December 2024.\n\n## Step 1: Filter Merged PRs Only\nFrom the PR query results, identify only PRs where:\n- `mergedAt` is NOT null\n- `state` equals `\"MERGED\"` (verify this is the actual state value)\n\nDo NOT include closed-but-unmerged or open PRs in this calculation.\n\n## Step 2: Parse Timestamps Correctly\nFor each merged PR, extract:\n- `createdAt`: The PR creation timestamp (ISO-8601 format, e.g., `2024-12-15T10:30:45Z`)\n- `mergedAt`: The PR merge timestamp (ISO-8601 format)\n\n**Critical:** Use the **creation date** as the start point, not any other date field.\n\n## Step 3: Convert to Days\nFor each PR:\n1. Parse both timestamps as datetime objects (handle timezone info — typically UTC)\n2. Calculate the difference: `mergedAt - createdAt`\n3. Convert to total days (may be a float with decimal)\n4. Example: If a PR was created on 2024-12-01 at 00:00:00 and merged on 2024-12-02 at 12:00:00, that's 1.5 days\n\n## Step 4: Compute Average\n- Sum all day values from step 3\n- Divide by the count of merged PRs\n- Round the result to exactly 1 decimal place (e.g., 1.2, 15.8, 0.5)\n\n## Step 5: Validation Check\nIf your average is significantly higher than expected (e.g., 16+ days):\n- Verify you're only including merged PRs (check `mergedAt != null`)\n- Confirm you're measuring from `createdAt` (not some other field)\n- Check if any timestamp parsing errors are inflating the values\n- Inspect a few sample PRs manually to spot-check the calculation\n\n## Example Pseudocode\n```\nmerged_prs = [pr for pr in prs if pr.mergedAt is not null and pr.state == \"MERGED\"]\ndays_list = []\nfor pr in merged_prs:\n  created = parse_datetime(pr.createdAt)\n  merged = parse_datetime(pr.mergedAt)\n  days = (merged - created).total_seconds() \u002F 86400\n  days_list.append(days)\navg = sum(days_list) \u002F len(days_list)\nrounded_avg = round(avg, 1)\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Frun3_Calculate-Average-PR-Merge-Time",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":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill \"Calculate Average PR Merge Time\"",{"tool":51,"command":52},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill \"Calculate Average PR Merge Time\"",{"tool":54,"command":55},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill \"Calculate Average PR Merge Time\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Frun3_Calculate-Average-PR-Merge-Time\u002FSKILL.md",true,[],[60,78,102,120,140,152],{"skillNo":61,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":71,"publishedAt":32},"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",[66],{"categoryNo":67,"categoryName":68,"taxonomyType":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[72,73,74,75,76,77],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":87,"publishedAt":32},"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",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000023","Skill与智能体开发",[88,89,90,91,92,93,94,95,96,31,97,98,99,100,101],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":32},"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",[],[109,110,111,112,113,114,115,91,116,117,92,118,31,119,89],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"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":126,"publishedAt":32},"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",[],[127,128,129,31,130,131,132,133,134,135,136,137,118,138,139],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":32},"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",[146],{"categoryNo":85,"categoryName":86,"taxonomyType":69,"taxonomyTypeDesc":70},[148,139,129,128,134,133,130,149,150,31,151],"glm calibration","setup","companion","metric",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":32},"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",[],[159,129,133,160,148,139,31,128,161,162,163,164,165,166,130],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]