[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KE6RF37FBTX75TQ59GGV97":3,"skill-same:01M6KE6RF37FBTX75TQ59GGV97":54},{"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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":32,"commitSha":33,"skillDirectory":34,"forksCount":35,"repositoryHtmlUrl":36,"repositoryHomepageUrl":37,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":39,"install":40,"resources":53},"01M6KE6RF37FBTX75TQ59GGV97","excel-weighted-mean",null,"Calculating GDP-weighted means and other weighted statistics in Excel using SUMPRODUCT, including net exports as percent of GDP.","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\u002Fb1-one-shot-claude-sonnet-4-6\u002Fweighted-gdp-calculation\u002Fexcel-weighted-mean\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],"xlsx","excel weighted mean","statistics","as","2026-11-10 10:12:30",false,"---\nname: excel-weighted-mean\ndescription: Calculating GDP-weighted means and other weighted statistics in Excel using SUMPRODUCT, including net exports as percent of GDP.\n---\n\n# Excel Weighted Mean with SUMPRODUCT\n\n## Overview\n\nA weighted mean assigns different weights to each observation. In economic analysis, GDP-weighted averages give more influence to larger economies.\n\n## Formula\n\n```excel\n=SUMPRODUCT(values_range, weights_range) \u002F SUM(weights_range)\n```\n\n## Example: GDP-Weighted Net Exports % GDP\n\n```excel\n=ROUND(SUMPRODUCT(H35:H40, H26:H31) \u002F SUM(H26:H31), 1)\n```\n\nWhere:\n- `H35:H40` — net exports as % of GDP for each country (already in percentage form, e.g., 12.3 means 12.3%)\n- `H26:H31` — GDP values in billions (used as weights)\n- Result is automatically in the same scale as the input values (12.3 form, not 0.123)\n\n## Scale Consistency\n\nIf values in H35:H40 are stored as 12.3 (percent form), the weighted mean formula returns the result in the same 12.3 form — no additional multiplication by 100 is needed.\n\n## Net Exports as Percent of GDP\n\n```excel\n=ROUND((Exports - Imports) \u002F GDP * 100, 1)\n```\n\nExample where exports are in row 12, imports in row 19, GDP in row 26:\n```excel\n=ROUND((H12 - H19) \u002F H26 * 100, 1)\n```\n\n## Related Statistical Functions\n\nAll rounded to 1 decimal for consistency:\n\n```excel\n=ROUND(MIN(H35:H40), 1)                    ' Minimum\n=ROUND(MAX(H35:H40), 1)                    ' Maximum\n=ROUND(MEDIAN(H35:H40), 1)                 ' Median\n=ROUND(AVERAGE(H35:H40), 1)               ' Simple mean\n=ROUND(PERCENTILE(H35:H40, 0.25), 1)      ' 25th percentile\n=ROUND(PERCENTILE(H35:H40, 0.75), 1)      ' 75th percentile\n=ROUND(SUMPRODUCT(H35:H40, H26:H31) \u002F SUM(H26:H31), 1)  ' GDP-weighted mean\n```\n\n## SUMPRODUCT for Weighted Mean vs Simple Mean\n\n| Formula | Description |\n|---------|-------------|\n| `=AVERAGE(H35:H40)` | Simple mean (equal weights) |\n| `=SUMPRODUCT(H35:H40, H26:H31)\u002FSUM(H26:H31)` | GDP-weighted mean |\n\nThe weighted mean will differ from the simple mean because larger economies pull the average toward their values.\n\n## Setting in Python (openpyxl)\n\n```python\nfrom openpyxl import load_workbook\n\nwb = load_workbook('file.xlsx')\nws = wb['Task']\n\n# Weighted mean formula\nws['H50'] = '=ROUND(SUMPRODUCT(H35:H40,H26:H31)\u002FSUM(H26:H31),1)'\n\n# Statistics\nws['H42'] = '=ROUND(MIN(H35:H40),1)'\nws['H43'] = '=ROUND(MAX(H35:H40),1)'\nws['H44'] = '=ROUND(MEDIAN(H35:H40),1)'\nws['H45'] = '=ROUND(AVERAGE(H35:H40),1)'\nws['H46'] = '=ROUND(PERCENTILE(H35:H40,0.25),1)'\nws['H47'] = '=ROUND(PERCENTILE(H35:H40,0.75),1)'\n\nwb.save('file.xlsx')\n```\n","# Excel Weighted Mean with SUMPRODUCT\n\n## Overview\n\nA weighted mean assigns different weights to each observation. In economic analysis, GDP-weighted averages give more influence to larger economies.\n\n## Formula\n\n```excel\n=SUMPRODUCT(values_range, weights_range) \u002F SUM(weights_range)\n```\n\n## Example: GDP-Weighted Net Exports % GDP\n\n```excel\n=ROUND(SUMPRODUCT(H35:H40, H26:H31) \u002F SUM(H26:H31), 1)\n```\n\nWhere:\n- `H35:H40` — net exports as % of GDP for each country (already in percentage form, e.g., 12.3 means 12.3%)\n- `H26:H31` — GDP values in billions (used as weights)\n- Result is automatically in the same scale as the input values (12.3 form, not 0.123)\n\n## Scale Consistency\n\nIf values in H35:H40 are stored as 12.3 (percent form), the weighted mean formula returns the result in the same 12.3 form — no additional multiplication by 100 is needed.\n\n## Net Exports as Percent of GDP\n\n```excel\n=ROUND((Exports - Imports) \u002F GDP * 100, 1)\n```\n\nExample where exports are in row 12, imports in row 19, GDP in row 26:\n```excel\n=ROUND((H12 - H19) \u002F H26 * 100, 1)\n```\n\n## Related Statistical Functions\n\nAll rounded to 1 decimal for consistency:\n\n```excel\n=ROUND(MIN(H35:H40), 1)                    ' Minimum\n=ROUND(MAX(H35:H40), 1)                    ' Maximum\n=ROUND(MEDIAN(H35:H40), 1)                 ' Median\n=ROUND(AVERAGE(H35:H40), 1)               ' Simple mean\n=ROUND(PERCENTILE(H35:H40, 0.25), 1)      ' 25th percentile\n=ROUND(PERCENTILE(H35:H40, 0.75), 1)      ' 75th percentile\n=ROUND(SUMPRODUCT(H35:H40, H26:H31) \u002F SUM(H26:H31), 1)  ' GDP-weighted mean\n```\n\n## SUMPRODUCT for Weighted Mean vs Simple Mean\n\n| Formula | Description |\n|---------|-------------|\n| `=AVERAGE(H35:H40)` | Simple mean (equal weights) |\n| `=SUMPRODUCT(H35:H40, H26:H31)\u002FSUM(H26:H31)` | GDP-weighted mean |\n\nThe weighted mean will differ from the simple mean because larger economies pull the average toward their values.\n\n## Setting in Python (openpyxl)\n\n```python\nfrom openpyxl import load_workbook\n\nwb = load_workbook('file.xlsx')\nws = wb['Task']\n\n# Weighted mean formula\nws['H50'] = '=ROUND(SUMPRODUCT(H35:H40,H26:H31)\u002FSUM(H26:H31),1)'\n\n# Statistics\nws['H42'] = '=ROUND(MIN(H35:H40),1)'\nws['H43'] = '=ROUND(MAX(H35:H40),1)'\nws['H44'] = '=ROUND(MEDIAN(H35:H40),1)'\nws['H45'] = '=ROUND(AVERAGE(H35:H40),1)'\nws['H46'] = '=ROUND(PERCENTILE(H35:H40,0.25),1)'\nws['H47'] = '=ROUND(PERCENTILE(H35:H40,0.75),1)'\n\nwb.save('file.xlsx')\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fweighted-gdp-calculation\u002Fexcel-weighted-mean",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":41,"prompt":51,"zipAvailable":52},[42,45,48],{"tool":43,"command":44},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill excel-weighted-mean",{"tool":46,"command":47},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill excel-weighted-mean",{"tool":49,"command":50},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill excel-weighted-mean","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fweighted-gdp-calculation\u002Fexcel-weighted-mean\u002FSKILL.md",true,[],[55,71,96,114,134,146],{"skillNo":56,"name":57,"nameZh":6,"description":58,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":59,"categories":60,"keywords":64,"publishedAt":27},"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",[61],{"categoryNo":62,"categoryName":63,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[65,66,67,68,69,70],"object counter","object","vision","computer","Computer Vision","image",{"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":80,"publishedAt":27},"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",[77],{"categoryNo":78,"categoryName":79,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[81,82,83,84,85,86,87,88,89,90,91,92,93,94,95],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":27},"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",[],[103,104,105,106,107,108,109,84,110,111,85,112,90,113,82],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"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":27},"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",[],[121,122,123,90,124,125,126,127,128,129,130,131,112,132,133],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":141,"publishedAt":27},"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",[140],{"categoryNo":78,"categoryName":79,"taxonomyType":20,"taxonomyTypeDesc":21},[142,133,123,122,128,127,124,143,144,90,145],"glm calibration","setup","companion","metric",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":27},"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",[],[153,123,127,154,142,133,90,122,155,156,157,158,159,160,124],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]