[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718FSWQA891SSCXDKDD40J6":3,"skill-same:01M718FSWQA891SSCXDKDD40J6":71},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M718FSWQA891SSCXDKDD40J6","clean-data-xls",null,"Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on \"clean this data\", \"clean up this sheet\", \"normalize this data\", \"fix formatting\", \"dedupe\", \"standardize this column\", \"this data is messy\".","anthropics\u002Ffinancial-services","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188Y8CSYMMZ40A4B4N518V",41213,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Ffinancial-analysis\u002Fskills\u002Fclean-data-xls\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"clean data xls","xls","data","clean","type","analysis","convert","when","use","trim","whitespace","triggers","dates","fix","up","remove","spreadsheet","duplicates","as","normalize","2026-11-10 10:12:30",false,"---\nname: clean-data-xls\ndescription: Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on \"clean this data\", \"clean up this sheet\", \"normalize this data\", \"fix formatting\", \"dedupe\", \"standardize this column\", \"this data is messy\".\n---\n\n# Clean Data\n\nClean messy data in the active sheet or a specified range.\n\n## Environment\n\n- **If running inside Excel (Office Add-in \u002F Office JS):** Use Office JS directly (`Excel.run(async (context) => {...})`). Read via `range.values`, write helper-column formulas via `range.formulas = [[\"=TRIM(A2)\"]]`. The in-place vs helper-column decision still applies.\n- **If operating on a standalone .xlsx file:** Use Python\u002Fopenpyxl.\n\n## Workflow\n\n### Step 1: Scope\n\n- If a range is given (e.g. `A1:F200`), use it\n- Otherwise use the full used range of the active sheet\n- Profile each column: detect its dominant type (text \u002F number \u002F date) and identify outliers\n\n### Step 2: Detect issues\n\n| Issue | What to look for |\n|---|---|\n| Whitespace | leading\u002Ftrailing spaces, double spaces |\n| Casing | inconsistent casing in categorical columns (`usa` \u002F `USA` \u002F `Usa`) |\n| Number-as-text | numeric values stored as text; stray `$`, `,`, `%` in number cells |\n| Dates | mixed formats in the same column (`3\u002F8\u002F26`, `2026-03-08`, `March 8 2026`) |\n| Duplicates | exact-duplicate rows and near-duplicates (case\u002Fwhitespace differences) |\n| Blanks | empty cells in otherwise-populated columns |\n| Mixed types | a column that's 98% numbers but has 3 text entries |\n| Encoding | mojibake (`é`, `’`), non-printing characters |\n| Errors | `#REF!`, `#N\u002FA`, `#VALUE!`, `#DIV\u002F0!` |\n\n### Step 3: Propose fixes\n\nShow a summary table before changing anything:\n\n| Column | Issue | Count | Proposed Fix |\n|---|---|---|---|\n\n### Step 4: Apply\n\n- **Prefer formulas over hardcoded cleaned values** — where the cleaned output can be expressed as a formula (e.g. `=TRIM(A2)`, `=VALUE(SUBSTITUTE(B2,\"$\",\"\"))`, `=UPPER(C2)`, `=DATEVALUE(D2)`), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.\n- Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding\u002Fmojibake repair)\n- For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first\n- After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category\n- Report a before\u002Fafter summary of what changed\n","# Clean Data\n\nClean messy data in the active sheet or a specified range.\n\n## Environment\n\n- **If running inside Excel (Office Add-in \u002F Office JS):** Use Office JS directly (`Excel.run(async (context) => {...})`). Read via `range.values`, write helper-column formulas via `range.formulas = [[\"=TRIM(A2)\"]]`. The in-place vs helper-column decision still applies.\n- **If operating on a standalone .xlsx file:** Use Python\u002Fopenpyxl.\n\n## Workflow\n\n### Step 1: Scope\n\n- If a range is given (e.g. `A1:F200`), use it\n- Otherwise use the full used range of the active sheet\n- Profile each column: detect its dominant type (text \u002F number \u002F date) and identify outliers\n\n### Step 2: Detect issues\n\n| Issue | What to look for |\n|---|---|\n| Whitespace | leading\u002Ftrailing spaces, double spaces |\n| Casing | inconsistent casing in categorical columns (`usa` \u002F `USA` \u002F `Usa`) |\n| Number-as-text | numeric values stored as text; stray `$`, `,`, `%` in number cells |\n| Dates | mixed formats in the same column (`3\u002F8\u002F26`, `2026-03-08`, `March 8 2026`) |\n| Duplicates | exact-duplicate rows and near-duplicates (case\u002Fwhitespace differences) |\n| Blanks | empty cells in otherwise-populated columns |\n| Mixed types | a column that's 98% numbers but has 3 text entries |\n| Encoding | mojibake (`é`, `’`), non-printing characters |\n| Errors | `#REF!`, `#N\u002FA`, `#VALUE!`, `#DIV\u002F0!` |\n\n### Step 3: Propose fixes\n\nShow a summary table before changing anything:\n\n| Column | Issue | Count | Proposed Fix |\n|---|---|---|---|\n\n### Step 4: Apply\n\n- **Prefer formulas over hardcoded cleaned values** — where the cleaned output can be expressed as a formula (e.g. `=TRIM(A2)`, `=VALUE(SUBSTITUTE(B2,\"$\",\"\"))`, `=UPPER(C2)`, `=DATEVALUE(D2)`), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.\n- Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding\u002Fmojibake repair)\n- For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first\n- After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category\n- Report a before\u002Fafter summary of what changed\n",{"name":5,"description":7},"main","60565064557ca77b12f62aa7877f365d111c129d","plugins\u002Fvertical-plugins\u002Ffinancial-analysis\u002Fskills\u002Fclean-data-xls",5739,"https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services","https:\u002F\u002Fgithub.com\u002Fanthropics","https:\u002F\u002Fanthropic.com","2026-10-29 01:23:24",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add anthropics\u002Ffinancial-services --skill clean-data-xls",{"tool":63,"command":64},"bunx","bunx skills add anthropics\u002Ffinancial-services --skill clean-data-xls",{"tool":66,"command":67},"pnpm","pnpm dlx skills add anthropics\u002Ffinancial-services --skill clean-data-xls","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Ffinancial-analysis\u002Fskills\u002Fclean-data-xls\u002FSKILL.md",true,[],[72,96,119,140,169,187],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":44},"01M718K18RVV0Z7T7JR4DDYCR9","value-creation-plan","Structure post-acquisition value creation plans with revenue, cost, and operational levers mapped to an EBITDA bridge. Includes 100-day priorities, KPI targets, and accountability frameworks. Use when planning post-close execution, preparing operating partner materials, or building a board-ready value creation roadmap. Triggers on \"value creation plan\", \"100-day plan\", \"post-close plan\", \"EBITDA bridge\", \"operating plan\", or \"value creation levers\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Fvalue-creation-plan\u002FSKILL.md",[],[79,80,81,82,83,84,85,86,87,88,35,89,90,32,91,31,92,93,94,95],"value","creation","value creation plan","plan","operating","ready","bridge","planning","frameworks","kpi","execution","plans","roadmap","materials","with","cost","acquisition",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":44},"01M718JXVXRBT0736V7KDW3EHC","unit-economics","Analyze unit economics for PE targets — ARR cohorts, LTV\u002FCAC, net retention, payback periods, revenue quality, and margin waterfall. Essential for software\u002FSaaS, recurring revenue, and subscription businesses. Use when evaluating revenue quality, building a cohort analysis, or assessing customer economics. Triggers on \"unit economics\", \"cohort analysis\", \"ARR analysis\", \"LTV CAC\", \"net retention\", \"revenue quality\", or \"customer economics\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Funit-economics\u002FSKILL.md",[],[103,104,105,106,107,108,109,110,35,111,32,31,112,29,113,114,115,116,117,118],"unit","economics","unit economics","cohort analysis","recurring","essential","for","retention","recurring revenue","saas","analyze","targets","arr","customer","revenue","cohort",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":44},"01M718JV1463SCF8XND52PRN03","returns-analysis","Build quick IRR\u002FMOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on \"returns analysis\", \"IRR sensitivity\", \"MOIC table\", \"what's the return at\", \"model the returns\", or \"back of the envelope\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Freturns-analysis\u002FSKILL.md",[],[126,29,127,128,129,130,131,109,132,133,38,35,134,32,31,135,136,137,138,139],"returns analysis","returns","back","leverage","quick","model","sensitivity","testing","exit","growth","across","deal","build","models",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":154,"publishedAt":44},"01M718JRJYHENXRTAS2WES2J4E","portfolio-monitoring","Track and analyze portfolio company performance against plan. Ingests monthly\u002Fquarterly financial packages (Excel, PDF), extracts KPIs, flags variances to budget, and produces summary dashboards. Use when reviewing portfolio company financials, preparing board materials, or monitoring covenant compliance. Triggers on \"review portfolio company\", \"monthly financials\", \"how is [company] performing\", \"covenant check\", or \"portfolio update\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Fportfolio-monitoring\u002FSKILL.md",[146,151],{"categoryNo":147,"categoryName":148,"taxonomyType":149,"taxonomyTypeDesc":150},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":152,"categoryName":153,"taxonomyType":149,"taxonomyTypeDesc":150},"01JBSEEDCAT000000000000004","电子表格",[155,156,157,158,159,160,161,162,35,32,31,163,164,113,92,165,166,82,167,168],"portfolio monitoring","monitoring","portfolio","financials","PDF","xlsx","performance","company","review","check","packages","track","covenant","how",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":44},"01M718JPCC86RSTX0ZH4XK26J7","ic-memo","Draft a structured investment committee memo for PE deal approval. Synthesizes due diligence findings, financial analysis, and deal terms into a professional IC-ready document. Use when preparing for investment committee, writing up a deal, or creating a formal recommendation. Triggers on \"write IC memo\", \"investment committee memo\", \"deal write-up\", \"prepare IC materials\", or \"recommendation memo\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Fic-memo\u002FSKILL.md",[],[176,177,178,84,179,109,180,38,35,181,32,182,31,29,92,183,137,184,185,186],"ic memo","memo","recommendation memo","professional","recommendation","writing","document","draft","write","financial analysis","investment",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":196,"publishedAt":44},"01M718JK2B01WZQPFHVHKY25PD","deal-sourcing","PE deal sourcing workflow — discover target companies, check CRM for existing relationships, and draft personalized founder outreach emails. Use when sourcing new deals, prospecting companies in a sector, or reaching out to founders. Triggers on \"find companies\", \"source deals\", \"draft founder email\", \"check if we've seen this company\", or \"outreach to founder\".","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fvertical-plugins\u002Fprivate-equity\u002Fskills\u002Fdeal-sourcing\u002FSKILL.md",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":149,"taxonomyTypeDesc":150},"01JBSEEDCAT000000000000005","邮件与沟通",[197,198,137,199,200,201,109,162,35,202,203,204,32,205,31,206,207,164,183,208],"sourcing","deal sourcing","target","discover","email","emails","prospecting","new","Existing","workflow","founders","find"]