[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KJCS67810E6QCS38GC1NR3":3,"skill-same:01M6KJCS67810E6QCS38GC1NR3":70},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6KJCS67810E6QCS38GC1NR3","bug-report-identification",null,"Identify bug reports by checking if any issue label contains the substring \"bug\" (case-insensitive matching). Use this skill when categorizing GitHub issues as bugs vs. features, filtering defects, or generating quality\u002Freliability metrics.","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\u002Fb4-skill-creator-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Fbug-report-identification\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"bug report","bug report identification","identification","bug","report","issues","quality","filtering","use","reliability","reports","issue","matching","as","github issues","case","bugs","defects","features","github","2026-11-10 10:12:30",false,"---\nname: bug-report-identification\ndescription: Identify bug reports by checking if any issue label contains the substring \"bug\" (case-insensitive matching). Use this skill when categorizing GitHub issues as bugs vs. features, filtering defects, or generating quality\u002Freliability metrics.\n---\n\n# Bug Report Identification\n\nIdentify issues classified as bug reports based on label analysis.\n\n## Definition\n\nA **bug report** is any issue where at least one label contains the substring `\"bug\"` (case-insensitive).\n\n**Examples of matching labels:**\n- `bug`\n- `type: bug`\n- `kind\u002Fbug`\n- `bug-regression`\n- `BUGFIX` (case-insensitive)\n- `p1-bug`\n\n**Non-matching labels:**\n- `feature`\n- `documentation`\n- `enhancement`\n\n## Algorithm\n\nFor each issue:\n1. Extract the `labels` array (list of label objects with `name` field)\n2. For each label, check if `\"bug\"` is a substring of the lowercased label name\n3. If any label matches, mark the issue as a bug report\n4. Count total bug reports and track which were closed during a specified period\n\n## Implementation (Python)\n\n```python\ndef is_bug_report(issue: Dict) -> bool:\n    \"\"\"\n    Args:\n        issue: Issue object from GitHub API with 'labels' field\n\n    Returns:\n        True if any label contains \"bug\" (case-insensitive)\n    \"\"\"\n    labels = issue.get('labels', [])\n    return any('bug' in label['name'].lower() for label in labels)\n\n\ndef count_bug_reports(issues: List[Dict]) -> int:\n    \"\"\"Count total issues classified as bug reports\"\"\"\n    return sum(1 for issue in issues if is_bug_report(issue))\n\n\ndef count_closed_bugs(issues: List[Dict], closed_during: str = None) -> int:\n    \"\"\"\n    Count bug reports that are closed.\n\n    Args:\n        issues: List of issue objects\n        closed_during: Optional date range to filter (e.g., \"2024-12-01..2024-12-31\")\n                      If None, counts all closed bugs regardless of close date\n\n    Returns:\n        Count of closed bug issues\n    \"\"\"\n    closed_bugs = []\n\n    for issue in issues:\n        if not is_bug_report(issue):\n            continue\n\n        if issue['state'] != 'CLOSED':\n            continue\n\n        # If no date filter, include\n        if closed_during is None:\n            closed_bugs.append(issue)\n            continue\n\n        # If date filter provided, check closedAt\n        closed_at = issue.get('closedAt')\n        if closed_at:\n            # Parse and compare (implementation depends on date range format)\n            closed_bugs.append(issue)\n\n    return len(closed_bugs)\n```\n\n## Implementation (Bash with jq)\n\n```bash\n# Count total bug reports\njq '[.[] | select(.labels[].name | ascii_downcase | contains(\"bug\"))] | length' issues.json\n\n# Count closed bug reports\njq '[.[] | select(.labels[].name | ascii_downcase | contains(\"bug\")) | select(.state == \"CLOSED\")] | length' issues.json\n```\n\n## Edge Cases\n\n- **No labels**: Issue has empty `labels` array → not a bug report\n- **Multiple bug-related labels**: Count as one bug report (not multiple)\n- **Label name case variation**: Always normalize to lowercase for matching\n- **Compound labels**: Labels like `type: bug` correctly match (substring match)\n\n## Verification Steps\n\n1. Manually inspect 5-10 issues marked as bugs and confirm they have relevant labels\n2. Spot-check 2-3 non-bugs to ensure they're excluded\n3. Count total bugs and closed bugs separately\n4. Ensure counts don't exceed total issue count\n\n## Special Note on Date Filtering\n\nIf filtering closed bugs by date (e.g., \"issues created in December, but closed when?\"):\n- **Created during period but closed anytime**: No date filter on `closedAt`\n- **Closed during the same period**: Compare `closedAt` against the same date range as creation\n\nThis skill provides the counting logic; the caller should specify which date comparison to use based on the metric definition.\n","# Bug Report Identification\n\nIdentify issues classified as bug reports based on label analysis.\n\n## Definition\n\nA **bug report** is any issue where at least one label contains the substring `\"bug\"` (case-insensitive).\n\n**Examples of matching labels:**\n- `bug`\n- `type: bug`\n- `kind\u002Fbug`\n- `bug-regression`\n- `BUGFIX` (case-insensitive)\n- `p1-bug`\n\n**Non-matching labels:**\n- `feature`\n- `documentation`\n- `enhancement`\n\n## Algorithm\n\nFor each issue:\n1. Extract the `labels` array (list of label objects with `name` field)\n2. For each label, check if `\"bug\"` is a substring of the lowercased label name\n3. If any label matches, mark the issue as a bug report\n4. Count total bug reports and track which were closed during a specified period\n\n## Implementation (Python)\n\n```python\ndef is_bug_report(issue: Dict) -> bool:\n    \"\"\"\n    Args:\n        issue: Issue object from GitHub API with 'labels' field\n\n    Returns:\n        True if any label contains \"bug\" (case-insensitive)\n    \"\"\"\n    labels = issue.get('labels', [])\n    return any('bug' in label['name'].lower() for label in labels)\n\n\ndef count_bug_reports(issues: List[Dict]) -> int:\n    \"\"\"Count total issues classified as bug reports\"\"\"\n    return sum(1 for issue in issues if is_bug_report(issue))\n\n\ndef count_closed_bugs(issues: List[Dict], closed_during: str = None) -> int:\n    \"\"\"\n    Count bug reports that are closed.\n\n    Args:\n        issues: List of issue objects\n        closed_during: Optional date range to filter (e.g., \"2024-12-01..2024-12-31\")\n                      If None, counts all closed bugs regardless of close date\n\n    Returns:\n        Count of closed bug issues\n    \"\"\"\n    closed_bugs = []\n\n    for issue in issues:\n        if not is_bug_report(issue):\n            continue\n\n        if issue['state'] != 'CLOSED':\n            continue\n\n        # If no date filter, include\n        if closed_during is None:\n            closed_bugs.append(issue)\n            continue\n\n        # If date filter provided, check closedAt\n        closed_at = issue.get('closedAt')\n        if closed_at:\n            # Parse and compare (implementation depends on date range format)\n            closed_bugs.append(issue)\n\n    return len(closed_bugs)\n```\n\n## Implementation (Bash with jq)\n\n```bash\n# Count total bug reports\njq '[.[] | select(.labels[].name | ascii_downcase | contains(\"bug\"))] | length' issues.json\n\n# Count closed bug reports\njq '[.[] | select(.labels[].name | ascii_downcase | contains(\"bug\")) | select(.state == \"CLOSED\")] | length' issues.json\n```\n\n## Edge Cases\n\n- **No labels**: Issue has empty `labels` array → not a bug report\n- **Multiple bug-related labels**: Count as one bug report (not multiple)\n- **Label name case variation**: Always normalize to lowercase for matching\n- **Compound labels**: Labels like `type: bug` correctly match (substring match)\n\n## Verification Steps\n\n1. Manually inspect 5-10 issues marked as bugs and confirm they have relevant labels\n2. Spot-check 2-3 non-bugs to ensure they're excluded\n3. Count total bugs and closed bugs separately\n4. Ensure counts don't exceed total issue count\n\n## Special Note on Date Filtering\n\nIf filtering closed bugs by date (e.g., \"issues created in December, but closed when?\"):\n- **Created during period but closed anytime**: No date filter on `closedAt`\n- **Closed during the same period**: Compare `closedAt` against the same date range as creation\n\nThis skill provides the counting logic; the caller should specify which date comparison to use based on the metric definition.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb4-skill-creator-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Fbug-report-identification",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":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill bug-report-identification",{"tool":62,"command":63},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill bug-report-identification",{"tool":65,"command":66},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill bug-report-identification","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb4-skill-creator-claude-haiku-4-5\u002Fgithub-repo-analytics\u002Fbug-report-identification\u002FSKILL.md",true,[],[71,89,114,132,151,163],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":82,"publishedAt":43},"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",[77],{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[83,84,85,86,87,88],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":98,"publishedAt":43},"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",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000023","Skill与智能体开发",[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":43},"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",[],[121,122,123,124,125,126,127,102,128,129,103,130,108,131,100],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"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":43},"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",[],[139,140,141,108,142,143,144,145,146,35,147,148,130,149,150],"glm output","output","glm","tasks","metrics","json","basics","guidance","verifier","processing","root","calibration",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":43},"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",[157],{"categoryNo":96,"categoryName":97,"taxonomyType":80,"taxonomyTypeDesc":81},[159,150,141,140,146,145,142,160,161,108,162],"glm calibration","setup","companion","metric",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":169,"publishedAt":43},"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",[],[170,141,145,171,159,150,108,140,172,173,174,31,175,176,142],"glm basics","inspect","relevant","files","good","how","Evaluation"]