[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGVYYSD7WB3EZBKTKA2QFK":3,"skill-same:01M6KGVYYSD7WB3EZBKTKA2QFK":60},{"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":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":43,"ownerHtmlUrl":44,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":45,"install":46,"resources":59},"01M6KGVYYSD7WB3EZBKTKA2QFK","Track and Verify File Organization",null,"Use this skill to systematically track all files before and after sorting to ensure no files are lost, duplicated, or left out during the organization process.","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\u002Forganize-messy-files\u002Frun3_Track-and-Verify-File-Organization\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,5,28,29,30,31,32],"file","verify","organization","file organization","track","before and after","process","use","files","sorting","2026-11-10 10:12:30",false,"---\nname: Track and Verify File Organization\ndescription: Use this skill to systematically track all files before and after sorting to ensure no files are lost, duplicated, or left out during the organization process.\n---\n\n## When to use this skill\n- Before starting the sorting process: scan and count all source files\n- During sorting: log each file as it is processed and moved\n- After sorting: verify that all files are accounted for in the destination folders\n- If discrepancies are found: locate missing or misplaced files\n\n## File tracking process\n\n### Pre-sorting inventory\n```python\nimport os\nfrom pathlib import Path\n\ndef inventory_source_files(source_directory):\n    file_list = []\n    file_count = 0\n    \n    for root, dirs, files in os.walk(source_directory):\n        for file in files:\n            if file.endswith(('.pdf', '.pptx', '.docx')):\n                full_path = os.path.join(root, file)\n                file_list.append({\n                    'filename': file,\n                    'path': full_path,\n                    'size': os.path.getsize(full_path),\n                    'processed': False,\n                    'destination': None\n                })\n                file_count += 1\n    \n    return file_list, file_count\n```\n\n### Tracking during sorting\n- Create a log file (CSV or JSON) with columns: filename, source_path, destination_folder, classification_confidence, keywords_matched\n- Update the log as each file is processed\n- Record any files that are difficult to classify\n\n### Post-sorting verification\n```python\ndef verify_sorted_files(destination_dirs, original_count):\n    total_sorted = 0\n    folder_counts = {}\n    \n    for folder_name, folder_path in destination_dirs.items():\n        count = len([f for f in os.listdir(folder_path) \n                     if f.endswith(('.pdf', '.pptx', '.docx'))])\n        folder_counts[folder_name] = count\n        total_sorted += count\n    \n    if total_sorted == original_count:\n        return True, folder_counts\n    else:\n        return False, folder_counts\n```\n\n### Verification checklist\n- [ ] Original file count recorded\n- [ ] Source directory fully scanned for .pdf, .pptx, .docx files\n- [ ] Processing log created and updated for each file\n- [ ] All files moved to destination folders\n- [ ] Final count matches original count\n- [ ] No files remain in source directory (except non-supported file types)\n- [ ] No duplicate files in destination folders\n- [ ] File names unchanged, file contents unchanged\n\n### If discrepancies found\n1. **Files missing from destinations**: Check source directory again, verify processing log\n2. **Count mismatch**: Review processing log to find which files were skipped\n3. **Extra files in destinations**: Verify these files were part of original inventory\n4. **Duplicates**: Check if file was processed twice with different names\n\n### Create summary report\nDocument:\n- Total files processed\n- Count per folder\n- Any files requiring manual review\n- Classification confidence statistics","## When to use this skill\n- Before starting the sorting process: scan and count all source files\n- During sorting: log each file as it is processed and moved\n- After sorting: verify that all files are accounted for in the destination folders\n- If discrepancies are found: locate missing or misplaced files\n\n## File tracking process\n\n### Pre-sorting inventory\n```python\nimport os\nfrom pathlib import Path\n\ndef inventory_source_files(source_directory):\n    file_list = []\n    file_count = 0\n    \n    for root, dirs, files in os.walk(source_directory):\n        for file in files:\n            if file.endswith(('.pdf', '.pptx', '.docx')):\n                full_path = os.path.join(root, file)\n                file_list.append({\n                    'filename': file,\n                    'path': full_path,\n                    'size': os.path.getsize(full_path),\n                    'processed': False,\n                    'destination': None\n                })\n                file_count += 1\n    \n    return file_list, file_count\n```\n\n### Tracking during sorting\n- Create a log file (CSV or JSON) with columns: filename, source_path, destination_folder, classification_confidence, keywords_matched\n- Update the log as each file is processed\n- Record any files that are difficult to classify\n\n### Post-sorting verification\n```python\ndef verify_sorted_files(destination_dirs, original_count):\n    total_sorted = 0\n    folder_counts = {}\n    \n    for folder_name, folder_path in destination_dirs.items():\n        count = len([f for f in os.listdir(folder_path) \n                     if f.endswith(('.pdf', '.pptx', '.docx'))])\n        folder_counts[folder_name] = count\n        total_sorted += count\n    \n    if total_sorted == original_count:\n        return True, folder_counts\n    else:\n        return False, folder_counts\n```\n\n### Verification checklist\n- [ ] Original file count recorded\n- [ ] Source directory fully scanned for .pdf, .pptx, .docx files\n- [ ] Processing log created and updated for each file\n- [ ] All files moved to destination folders\n- [ ] Final count matches original count\n- [ ] No files remain in source directory (except non-supported file types)\n- [ ] No duplicate files in destination folders\n- [ ] File names unchanged, file contents unchanged\n\n### If discrepancies found\n1. **Files missing from destinations**: Check source directory again, verify processing log\n2. **Count mismatch**: Review processing log to find which files were skipped\n3. **Extra files in destinations**: Verify these files were part of original inventory\n4. **Duplicates**: Check if file was processed twice with different names\n\n### Create summary report\nDocument:\n- Total files processed\n- Count per folder\n- Any files requiring manual review\n- Classification confidence statistics",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Forganize-messy-files\u002Frun3_Track-and-Verify-File-Organization",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":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill \"Track and Verify File Organization\"",{"tool":52,"command":53},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill \"Track and Verify File Organization\"",{"tool":55,"command":56},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill \"Track and Verify File Organization\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Forganize-messy-files\u002Frun3_Track-and-Verify-File-Organization\u002FSKILL.md",true,[],[61,77,100,118,138,150],{"skillNo":62,"name":63,"nameZh":6,"description":64,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":65,"categories":66,"keywords":70,"publishedAt":33},"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",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[71,72,73,74,75,76],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":84,"publishedAt":33},"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",[83],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","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":33},"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",[],[107,108,109,110,111,112,113,88,114,115,89,116,94,117,86],"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":33},"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",[],[125,126,127,94,128,129,130,131,132,133,134,135,116,136,137],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":145,"publishedAt":33},"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",[144],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[146,137,127,126,132,131,128,147,148,94,149],"glm calibration","setup","companion","metric",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":33},"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",[],[157,127,131,158,146,137,94,126,159,31,160,30,161,162,128],"glm basics","inspect","relevant","good","how","Evaluation"]