[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFNNCKA79BYC4VT8MNC79H":3,"skill-same:01M6KFNNCKA79BYC4VT8MNC79H":62},{"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":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":45,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M6KFNNCKA79BYC4VT8MNC79H","run2_excel-index-match",null,"INDEX&MATCH for two-condition lookups in Excel, with correct absolute\u002Fmixed references for fill-down\u002Ffill-right","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\u002Fb2-self-feedback-claude-opus-4-6\u002Fweighted-gdp-calculation\u002Frun2_excel-index-match\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34],"xlsx","index","match","excel index match","run2 excel index match","correct","references","condition","with","for","fill","down","2026-11-10 10:12:30",false,"---\nname: run2_excel-index-match\ndescription: INDEX&MATCH for two-condition lookups in Excel, with correct absolute\u002Fmixed references for fill-down\u002Ffill-right\n---\n\n# INDEX & MATCH Two-Condition Lookup\n\n## Formula Pattern\n```\n=INDEX(data_range, MATCH(row_criteria, row_lookup_range, 0), MATCH(col_criteria, col_lookup_range, 0))\n```\n\n## Two-Condition Lookup (series code + year)\nGiven:\n- Data range: `Data!$H$21:$M$40` (all values, absolute)\n- Row lookup: `Data!$B$21:$B$40` (series codes, absolute)\n- Col lookup: `Data!$H$4:$M$4` (years, absolute)\n- Row criteria: `$D12` (series code in Task sheet - lock column, allow row to change)\n- Col criteria: `H$9` (year in Task sheet - lock row, allow column to change)\n\n```\n=INDEX(Data!$H$21:$M$40, MATCH($D12, Data!$B$21:$B$40, 0), MATCH(H$9, Data!$H$4:$M$4, 0))\n```\n\n## Reference Locking Strategy\n| Reference | Lock | Why |\n|-----------|------|-----|\n| `$D12` | Column locked | Series code always in col D; row changes per country |\n| `H$9` | Row locked | Year always in row 9; column changes per year |\n| Data ranges | Fully absolute | Never changes |\n\n## openpyxl Implementation\n```python\nfrom openpyxl import load_workbook\nwb = load_workbook('file.xlsx')\nws = wb['Task']\n\n# Fill H12:L17 (and similar ranges) with lookup formulas\nfor row in range(12, 18):\n    for col in ['H', 'I', 'J', 'K', 'L']:\n        ws[f'{col}{row}'] = f'=INDEX(Data!$H$21:$M$40,MATCH($D{row},Data!$B$21:$B$40,0),MATCH({col}$9,Data!$H$4:$M$4,0))'\n```\n\n## Important Notes\n- Skip gap rows between sections (e.g., row 18, 25 if ranges are 12-17, 19-24, 26-31)\n- MATCH with 0 = exact match; returns #N\u002FA if not found\n- Verify series codes in Task!D column exactly match Data!B column\n","# INDEX & MATCH Two-Condition Lookup\n\n## Formula Pattern\n```\n=INDEX(data_range, MATCH(row_criteria, row_lookup_range, 0), MATCH(col_criteria, col_lookup_range, 0))\n```\n\n## Two-Condition Lookup (series code + year)\nGiven:\n- Data range: `Data!$H$21:$M$40` (all values, absolute)\n- Row lookup: `Data!$B$21:$B$40` (series codes, absolute)\n- Col lookup: `Data!$H$4:$M$4` (years, absolute)\n- Row criteria: `$D12` (series code in Task sheet - lock column, allow row to change)\n- Col criteria: `H$9` (year in Task sheet - lock row, allow column to change)\n\n```\n=INDEX(Data!$H$21:$M$40, MATCH($D12, Data!$B$21:$B$40, 0), MATCH(H$9, Data!$H$4:$M$4, 0))\n```\n\n## Reference Locking Strategy\n| Reference | Lock | Why |\n|-----------|------|-----|\n| `$D12` | Column locked | Series code always in col D; row changes per country |\n| `H$9` | Row locked | Year always in row 9; column changes per year |\n| Data ranges | Fully absolute | Never changes |\n\n## openpyxl Implementation\n```python\nfrom openpyxl import load_workbook\nwb = load_workbook('file.xlsx')\nws = wb['Task']\n\n# Fill H12:L17 (and similar ranges) with lookup formulas\nfor row in range(12, 18):\n    for col in ['H', 'I', 'J', 'K', 'L']:\n        ws[f'{col}{row}'] = f'=INDEX(Data!$H$21:$M$40,MATCH($D{row},Data!$B$21:$B$40,0),MATCH({col}$9,Data!$H$4:$M$4,0))'\n```\n\n## Important Notes\n- Skip gap rows between sections (e.g., row 18, 25 if ranges are 12-17, 19-24, 26-31)\n- MATCH with 0 = exact match; returns #N\u002FA if not found\n- Verify series codes in Task!D column exactly match Data!B column\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-opus-4-6\u002Fweighted-gdp-calculation\u002Frun2_excel-index-match",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":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_excel-index-match",{"tool":54,"command":55},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_excel-index-match",{"tool":57,"command":58},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_excel-index-match","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-opus-4-6\u002Fweighted-gdp-calculation\u002Frun2_excel-index-match\u002FSKILL.md",true,[],[63,79,103,121,141,153],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":72,"publishedAt":35},"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",[69],{"categoryNo":70,"categoryName":71,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[73,74,75,76,77,78],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":88,"publishedAt":35},"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",[85],{"categoryNo":86,"categoryName":87,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[89,90,91,92,93,94,95,96,97,32,98,99,100,101,102],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":35},"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",[],[110,111,112,113,114,115,116,92,117,118,93,119,32,120,90],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"publishedAt":35},"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",[],[128,129,130,32,131,132,133,134,135,136,137,138,119,139,140],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":148,"publishedAt":35},"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",[147],{"categoryNo":86,"categoryName":87,"taxonomyType":20,"taxonomyTypeDesc":21},[149,140,130,129,135,134,131,150,151,32,152],"glm calibration","setup","companion","metric",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"publishedAt":35},"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",[],[160,130,134,161,149,140,32,129,162,163,164,165,166,167,131],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]