[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KKCMJ4DRNNJ2V1JS28FSR6":3,"skill-same:01M6KKCMJ4DRNNJ2V1JS28FSR6":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":17,"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},"01M6KKCMJ4DRNNJ2V1JS28FSR6","community-pulse-reporter",null,"Professional methodology for transforming raw repository activity into structured community pulse reports. Use this skill when you need to calculate metrics like merge times, contributor rankings, and bug resolution rates for meeting-ready summaries.","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\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fgithub-repo-analytics\u002Fcommunity-pulse-reporter\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"community pulse reporter","pulse","reporter","community","structured","summaries","for","meeting","you","activity","professional","resolution","merge","reports","bug","ready","use","calculate","methodology","repository","2026-11-10 10:12:30",false,"---\nname: community-pulse-reporter\ndescription: Professional methodology for transforming raw repository activity into structured community pulse reports. Use this skill when you need to calculate metrics like merge times, contributor rankings, and bug resolution rates for meeting-ready summaries.\n---\n\n# Community Pulse Reporter\n\nThis skill details the logic and transformations required to generate a standardized community pulse report from raw data.\n\n## Metric Calculation Logic\n\n### 1. PR Merge Time\nCalculate the average time (in days) from PR creation to merge for all PRs that were merged.\n- **Formula**: `(merge_timestamp - creation_timestamp) \u002F (24 * 3600)`\n- **Rounding**: Round to one decimal place.\n\n### 2. Top Contributor\nIdentify the individual who opened the most PRs within the reporting period.\n- **Logic**: Count PRs grouped by author login. In case of a tie, the first alphabetical login is usually acceptable unless specified otherwise.\n\n### 3. Bug Resolution Rate\nIdentify issues labeled as \"bug\" and track their closure status.\n- **Bug Definition**: Any issue where at least one label contains the case-insensitive substring \"bug\".\n- **Resolved Bugs**: Issues meeting the bug definition that were closed during the reporting period.\n\n## Output Structure (report.json)\n\nThe report must follow this exact schema:\n\n```json\n{\n  \"pr\": {\n    \"total\": \u003Cint>,\n    \"merged\": \u003Cint>,\n    \"closed\": \u003Cint>,\n    \"avg_merge_days\": \u003Cfloat>,\n    \"top_contributor\": \u003Cstr>\n  },\n  \"issue\": {\n    \"total\": \u003Cint>,\n    \"bug\": \u003Cint>,\n    \"resolved_bugs\": \u003Cint>\n  }\n}\n```\n\n## Processing with Python\nPython is recommended for complex calculations and JSON generation.\n\n### Skeleton Processing Script\n```python\nimport json\nfrom datetime import datetime\n\ndef calculate_days(start_str, end_str):\n    start = datetime.fromisoformat(start_str.replace(\"Z\", \"+00:00\"))\n    end = datetime.fromisoformat(end_str.replace(\"Z\", \"+00:00\"))\n    return (end - start).total_seconds() \u002F (24 * 3600)\n\n# Load data, compute metrics, and write JSON\n```\n","# Community Pulse Reporter\n\nThis skill details the logic and transformations required to generate a standardized community pulse report from raw data.\n\n## Metric Calculation Logic\n\n### 1. PR Merge Time\nCalculate the average time (in days) from PR creation to merge for all PRs that were merged.\n- **Formula**: `(merge_timestamp - creation_timestamp) \u002F (24 * 3600)`\n- **Rounding**: Round to one decimal place.\n\n### 2. Top Contributor\nIdentify the individual who opened the most PRs within the reporting period.\n- **Logic**: Count PRs grouped by author login. In case of a tie, the first alphabetical login is usually acceptable unless specified otherwise.\n\n### 3. Bug Resolution Rate\nIdentify issues labeled as \"bug\" and track their closure status.\n- **Bug Definition**: Any issue where at least one label contains the case-insensitive substring \"bug\".\n- **Resolved Bugs**: Issues meeting the bug definition that were closed during the reporting period.\n\n## Output Structure (report.json)\n\nThe report must follow this exact schema:\n\n```json\n{\n  \"pr\": {\n    \"total\": \u003Cint>,\n    \"merged\": \u003Cint>,\n    \"closed\": \u003Cint>,\n    \"avg_merge_days\": \u003Cfloat>,\n    \"top_contributor\": \u003Cstr>\n  },\n  \"issue\": {\n    \"total\": \u003Cint>,\n    \"bug\": \u003Cint>,\n    \"resolved_bugs\": \u003Cint>\n  }\n}\n```\n\n## Processing with Python\nPython is recommended for complex calculations and JSON generation.\n\n### Skeleton Processing Script\n```python\nimport json\nfrom datetime import datetime\n\ndef calculate_days(start_str, end_str):\n    start = datetime.fromisoformat(start_str.replace(\"Z\", \"+00:00\"))\n    end = datetime.fromisoformat(end_str.replace(\"Z\", \"+00:00\"))\n    return (end - start).total_seconds() \u002F (24 * 3600)\n\n# Load data, compute metrics, and write JSON\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fgithub-repo-analytics\u002Fcommunity-pulse-reporter",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 community-pulse-reporter",{"tool":57,"command":58},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill community-pulse-reporter",{"tool":60,"command":61},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill community-pulse-reporter","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-gemini-3-flash-preview\u002Fgithub-repo-analytics\u002Fcommunity-pulse-reporter\u002FSKILL.md",true,[],[66,84,107,124,144,156],{"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":77,"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":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[78,79,80,81,82,83],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":93,"publishedAt":38},"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",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":75,"taxonomyTypeDesc":76},"01JBSEEDCAT000000000000023","Skill与智能体开发",[94,95,96,97,98,99,22,100,101,24,102,103,104,105,106],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":113,"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",[],[114,115,116,117,118,119,120,97,121,122,98,26,24,123,95],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","database",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"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",[],[131,132,133,24,134,135,136,137,138,139,140,141,26,142,143],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"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",[150],{"categoryNo":91,"categoryName":92,"taxonomyType":75,"taxonomyTypeDesc":76},[152,143,133,132,138,137,134,153,154,24,155],"glm calibration","setup","companion","metric",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"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",[],[163,133,137,164,152,143,24,132,165,166,167,34,168,169,134],"glm basics","inspect","relevant","files","good","how","Evaluation"]