[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718DP1D2TRCNNH2ZV1MSPMJ":3,"skill-same:01M718DP1D2TRCNNH2ZV1MSPMJ":67},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M718DP1D2TRCNNH2ZV1MSPMJ","equity-research",null,"Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.","anthropics\u002Ffinancial-services","anthropics","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F76263028?v=4","01M697XGDPXJAVQ8391V2BCYTE","Anthropic","01M7188Y8CSYMMZ40A4B4N518V",41229,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fpartner-built\u002Flseg\u002Fskills\u002Fequity-research\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"equity","research","equity research","financials","consensus","prices","company","use","snapshots","when","generate","analyst","analyzing","investment","stocks","context","2026-11-10 10:12:30",false,"---\nname: equity-research\ndescription: Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.\n---\n\n# Equity Research Analysis\n\nYou are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.\n\n## Core Principles\n\nEvery piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.\n\n## Available MCP Tools\n\n- **`qa_ibes_consensus`** — IBES analyst consensus estimates and actuals. Returns median\u002Fmean estimates, analyst count, high\u002Flow range, dispersion. Supports EPS, Revenue, EBITDA, DPS.\n- **`qa_company_fundamentals`** — Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.\n- **`qa_historical_equity_price`** — Historical equity prices with OHLCV, total returns, and beta.\n- **`tscc_historical_pricing_summaries`** — Historical pricing summaries (daily, weekly, monthly). Alternative\u002Fsupplement for price history.\n- **`qa_macroeconomic`** — Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.\n\n## Tool Chaining Workflow\n\n1. **Consensus Snapshot:** Call `qa_ibes_consensus` for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.\n2. **Historical Fundamentals:** Call `qa_company_fundamentals` for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).\n3. **Price Performance:** Call `qa_historical_equity_price` for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.\n4. **Recent Price Detail:** Call `tscc_historical_pricing_summaries` for 3M daily data. Assess volume trends and recent momentum.\n5. **Macro Context:** Call `qa_macroeconomic` for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.\n6. **Synthesize:** Combine into a research note with consensus tables, financials summary, valuation metrics (forward P\u002FE from price \u002F consensus EPS), and macro backdrop.\n\n## Output Format\n\n### Consensus Estimates\n| Metric | FY1 | FY2 | # Analysts | Dispersion |\n|--------|-----|-----|------------|------------|\n| EPS | ... | ... | ... | ...% |\n| Revenue (M) | ... | ... | ... | ...% |\n| EBITDA (M) | ... | ... | ... | ...% |\n\n### Financials Summary\n| Metric | FY-2 | FY-1 | FY0 (LTM) | Trend |\n|--------|------|------|-----------|-------|\n| Revenue (M) | ... | ... | ... | ... |\n| Gross Margin | ... | ... | ... | ... |\n| Operating Margin | ... | ... | ... | ... |\n| ROE | ... | ... | ... | ... |\n| Net Debt\u002FEBITDA | ... | ... | ... | ... |\n\n### Valuation Summary\n| Metric | Current | Context |\n|--------|---------|---------|\n| Forward P\u002FE | ... | vs sector\u002Fhistory |\n| EV\u002FEBITDA | ... | vs sector\u002Fhistory |\n| Dividend Yield | ... | ... |\n\n### Investment Thesis\nConclude with: recommendation (buy\u002Fhold\u002Fsell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high\u002Fmedium\u002Flow).\n","# Equity Research Analysis\n\nYou are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.\n\n## Core Principles\n\nEvery piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.\n\n## Available MCP Tools\n\n- **`qa_ibes_consensus`** — IBES analyst consensus estimates and actuals. Returns median\u002Fmean estimates, analyst count, high\u002Flow range, dispersion. Supports EPS, Revenue, EBITDA, DPS.\n- **`qa_company_fundamentals`** — Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.\n- **`qa_historical_equity_price`** — Historical equity prices with OHLCV, total returns, and beta.\n- **`tscc_historical_pricing_summaries`** — Historical pricing summaries (daily, weekly, monthly). Alternative\u002Fsupplement for price history.\n- **`qa_macroeconomic`** — Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.\n\n## Tool Chaining Workflow\n\n1. **Consensus Snapshot:** Call `qa_ibes_consensus` for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.\n2. **Historical Fundamentals:** Call `qa_company_fundamentals` for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).\n3. **Price Performance:** Call `qa_historical_equity_price` for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.\n4. **Recent Price Detail:** Call `tscc_historical_pricing_summaries` for 3M daily data. Assess volume trends and recent momentum.\n5. **Macro Context:** Call `qa_macroeconomic` for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.\n6. **Synthesize:** Combine into a research note with consensus tables, financials summary, valuation metrics (forward P\u002FE from price \u002F consensus EPS), and macro backdrop.\n\n## Output Format\n\n### Consensus Estimates\n| Metric | FY1 | FY2 | # Analysts | Dispersion |\n|--------|-----|-----|------------|------------|\n| EPS | ... | ... | ... | ...% |\n| Revenue (M) | ... | ... | ... | ...% |\n| EBITDA (M) | ... | ... | ... | ...% |\n\n### Financials Summary\n| Metric | FY-2 | FY-1 | FY0 (LTM) | Trend |\n|--------|------|------|-----------|-------|\n| Revenue (M) | ... | ... | ... | ... |\n| Gross Margin | ... | ... | ... | ... |\n| Operating Margin | ... | ... | ... | ... |\n| ROE | ... | ... | ... | ... |\n| Net Debt\u002FEBITDA | ... | ... | ... | ... |\n\n### Valuation Summary\n| Metric | Current | Context |\n|--------|---------|---------|\n| Forward P\u002FE | ... | vs sector\u002Fhistory |\n| EV\u002FEBITDA | ... | vs sector\u002Fhistory |\n| Dividend Yield | ... | ... |\n\n### Investment Thesis\nConclude with: recommendation (buy\u002Fhold\u002Fsell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high\u002Fmedium\u002Flow).\n",{"name":5,"description":7},"main","60565064557ca77b12f62aa7877f365d111c129d","plugins\u002Fpartner-built\u002Flseg\u002Fskills\u002Fequity-research",5744,"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":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add anthropics\u002Ffinancial-services --skill equity-research",{"tool":59,"command":60},"bunx","bunx skills add anthropics\u002Ffinancial-services --skill equity-research",{"tool":62,"command":63},"pnpm","pnpm dlx skills add anthropics\u002Ffinancial-services --skill equity-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanthropics\u002Ffinancial-services\u002Fblob\u002Fmain\u002Fplugins\u002Fpartner-built\u002Flseg\u002Fskills\u002Fequity-research\u002FSKILL.md",true,[],[68,93,117,139,164,181],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":40},"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",[],[75,76,77,78,79,80,81,82,83,84,85,86,87,31,88,33,89,90,91,92],"value","creation","value creation plan","plan","operating","ready","bridge","planning","frameworks","kpi","triggers","execution","plans","roadmap","materials","with","cost","acquisition",{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":40},"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",[],[100,101,102,103,104,105,106,107,85,108,31,33,109,110,111,112,113,114,115,116],"unit","economics","unit economics","cohort analysis","recurring","essential","for","retention","recurring revenue","saas","analysis","analyze","targets","arr","customer","revenue","cohort",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":40},"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",[],[124,110,125,126,127,128,129,106,130,131,132,85,133,31,33,134,135,136,137,138],"returns analysis","returns","back","leverage","quick","model","sensitivity","testing","up","exit","growth","across","deal","build","models",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":151,"publishedAt":40},"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",[145,148],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000004","电子表格",[152,153,154,27,155,156,157,30,85,31,33,158,159,111,89,160,161,78,162,163],"portfolio monitoring","monitoring","portfolio","PDF","xlsx","performance","review","check","packages","track","covenant","how",{"skillNo":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":170,"publishedAt":40},"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",[],[171,172,173,80,174,106,175,132,85,176,31,177,33,110,89,178,136,179,180,37],"ic memo","memo","recommendation memo","professional","recommendation","writing","document","draft","write","financial analysis",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":40},"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",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",[191,192,136,193,194,195,106,30,85,196,197,198,31,199,33,200,201,159,178,202],"sourcing","deal sourcing","target","discover","email","emails","prospecting","new","Existing","workflow","founders","find"]