[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-comp-sheet":3,"mdc-2ejvkj-key":36,"related-org-openai-comp-sheet":1297,"related-repo-openai-comp-sheet":1504},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"comp-sheet","build industry comp sheet models","Build an industry comp sheet Excel model with deep operational KPIs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Financial Modeling","financial-modeling","tag",{"name":17,"slug":18,"type":15},"Excel","excel",{"name":20,"slug":21,"type":15},"KPI","kpi",{"name":23,"slug":24,"type":15},"Spreadsheets","spreadsheets",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-06-30T19:00:57.102",null,465,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Fdaloopa\u002Fskills\u002Fcomp-sheet","---\nname: comp-sheet\ndescription: Build an industry comp sheet Excel model with deep operational KPIs\n---\n\nBuild a multi-company industry comp sheet Excel model for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.\n\nThis produces an interactive `.xlsx` workbook — the kind of comp sheet every analyst on a coverage team maintains. Multi-company, multi-tab, with deep operational KPIs alongside standard financials.\n\n**Before starting, read `..\u002Fdata-access.md` for data access methods and `..\u002Fdesign-system.md` for formatting conventions.** Follow the data access detection logic and design system throughout this skill.\n\nFollow these steps:\n\n## 1. Company & Peer Setup\n\nLook up the target company by ticker using `discover_companies`. Capture `company_id`, `latest_calendar_quarter` (anchor for all period calculations — see `..\u002Fdata-access.md` Section 1.5), and `latest_fiscal_quarter`. Note the firm name for report attribution (default: \"Daloopa\") — see `..\u002Fdata-access.md` Section 4.5.\n\nThen identify 6-10 comparable companies using the same logic as the comps skill:\n- **Direct competitors** in the same market\n- **Business model peers** (similar revenue model)\n- **Size peers** (similar market cap range)\n- **Growth profile peers** (similar growth rate)\n\nLook up all peer company_ids via Daloopa. If a peer isn't available in Daloopa, include it with market data only and note the limitation.\n\nList the full peer group with brief justification for each.\n\n## 2. Deep Data Gathering\n\nFor each company (target + all peers), pull from Daloopa:\n\n**Calculate 8 quarters backward from `latest_calendar_quarter`. Pull financials:**\n- Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS\n- Operating Cash Flow, Capital Expenditures, D&A\n- Free Cash Flow (compute as OCF - CapEx)\n- R&D Expense, SG&A (where available)\n\n**Segment revenue breakdown** (all available segments, 8 quarters)\n\n**Company-specific operational KPIs** — use the 9-sector taxonomy to know what to search for:\n- **SaaS\u002FCloud**: ARR, net revenue retention, RPO\u002FcRPO, customers >$100K, cloud gross margin\n- **Consumer Tech**: DAU\u002FMAU, ARPU, engagement metrics, installed base, paid subscribers\n- **E-commerce\u002FMarketplace**: GMV, take rate, active buyers\u002Fsellers, order frequency\n- **Retail**: same-store sales, store count, average ticket, transactions\n- **Telecom\u002FMedia**: subscribers, churn, ARPU, content spend\n- **Hardware**: units shipped, ASP, attach rate, installed base\n- **Financial Services**: AUM, NIM, loan growth, credit quality metrics, fee income ratio\n- **Pharma\u002FBiotech**: pipeline stage, patient starts, scripts, market share\n- **Industrials\u002FEnergy**: backlog, book-to-bill, utilization, production volumes, reserves\n\n**Stock prices & valuation multiples:**\nUse `get_stock_prices` (see `..\u002Fdata-access.md` Section 1.7) to pull prices for ALL companies in a single batch call. Get:\n- Current price: `dates` = 3 most recent calendar days for all company_ids\n- Quarter-end prices: `dates` = quarter-end dates matching the financial periods (for historical multiples)\n\nThen compute valuation metrics by combining stock prices with Daloopa fundamentals:\n- **Market Cap** = Close price × Diluted shares outstanding\n- **Enterprise Value** = Market Cap + Total Debt - Cash\n- **P\u002FE (trailing)** = Market Cap \u002F Net Income (trailing 4Q)\n- **EV\u002FEBITDA** = EV \u002F EBITDA (trailing 4Q)\n- **P\u002FS** = Market Cap \u002F Revenue (trailing 4Q)\n- **P\u002FB** = Market Cap \u002F Total Equity\n- **EV\u002FFCF** = EV \u002F Free Cash Flow (trailing 4Q)\n- **FCF Yield** = FCF (trailing 4Q) \u002F Market Cap\n- **Dividend Yield** = Dividends Paid (trailing 4Q) \u002F Market Cap\n\nFor beta, use web search (see `..\u002Fdata-access.md` Section 2). For forward multiples, use consensus estimates if available (Section 3).\n\n## 3. KPI Discovery & Mapping\n\nAfter pulling data, build the KPI mapping:\n- Which KPIs are available for which companies? Build a coverage matrix.\n- Group KPIs into categories:\n  - **Segment Revenue**: product\u002Fservice line breakdowns\n  - **Growth KPIs**: subscriber growth, unit growth, same-store sales growth\n  - **Unit Economics**: ARPU, ASP, take rate, retention\n  - **Efficiency**: R&D % of revenue, SBC % of revenue, CapEx % of revenue\n  - **Engagement**: DAU\u002FMAU, retention, churn\n- Flag KPIs that are comparable across peers vs company-specific\n\n## 4. Compute Derived Metrics\n\nFor each company, calculate:\n\n**Margins:**\n- Gross Margin, Operating Margin, Net Margin, FCF Margin (each quarter)\n\n**Growth rates:**\n- Revenue YoY, EPS YoY, segment revenue YoY (each quarter where year-ago data exists)\n\n**Capital metrics:**\n- Net Debt (Total Debt - Cash)\n- Net Debt\u002FEBITDA\n- Shareholder Yield (Buybacks + Dividends) \u002F Market Cap\n\n**Historical multiples (from quarter-end prices pulled in Section 2):**\n- Compute P\u002FE, EV\u002FEBITDA, P\u002FS, EV\u002FFCF at each quarter-end to show how multiples have trended\n- This lets the reader see whether the current multiple is elevated or depressed vs. the company's own history\n\n**Implied valuation:**\n- For each valuation methodology (P\u002FE, EV\u002FEBITDA, P\u002FS, EV\u002FFCF):\n  - Peer median multiple × target metric = implied value\n  - Convert to implied share price\n- Compute median implied price across methodologies\n\n## 5. Build Excel Workbook\n\nGenerate the Excel workbook directly as a local `.xlsx` file. For Codex, prefer bundled spreadsheet tooling or Python\u002Fopenpyxl when available.\n\nThe workbook must contain 8 tabs with the following structure:\n\n### Tab 1: Comp Summary\nOne-page overview with all companies side-by-side:\n- Company name, ticker, price, market cap\n- All valuation multiples (P\u002FE, EV\u002FEBITDA, P\u002FS, P\u002FB, EV\u002FFCF, div yield)\n- Latest quarter revenue, EBITDA, net income\n- Growth rates (revenue YoY, EPS YoY)\n- Key margins (gross, operating, net, FCF)\n- Implied valuation for target (median across methodologies)\n- Premium\u002Fdiscount vs peers\n\n### Tab 2: Revenue Drivers\nUnit economics decomposition per company (trailing 4 quarters):\n- Total revenue (4Q sum)\n- Segment revenue breakdown (% of total)\n- Key unit economics: units × ASP, or subscribers × ARPU, etc.\n- Growth trajectory by segment\n\n### Tab 3: Operating KPIs\nCross-company KPI comparison matrix:\n- Rows = KPIs (grouped by category from step 3)\n- Columns = companies\n- Show latest quarter value + YoY change where applicable\n- Highlight cells where data is unavailable (sparse matrix)\n\n### Tab 4: Financial Summary\nSide-by-side income statements (trailing 4 quarters):\n- Revenue, COGS, Gross Profit\n- R&D, SG&A, Operating Income\n- Interest, Tax, Net Income\n- Diluted EPS\n- Compute 4Q sums for each line item\n\n### Tab 5: Growth & Margins\nTrend analysis (up to 8 quarters):\n- Revenue growth YoY (%)\n- EPS growth YoY (%)\n- Gross margin (%)\n- Operating margin (%)\n- Net margin (%)\n- FCF margin (%)\n- Show trends across all periods for each company\n\n### Tab 6: Valuation Detail\nImplied prices by methodology:\n- P\u002FE implied (peer median P\u002FE × target EPS)\n- EV\u002FEBITDA implied\n- P\u002FS implied\n- EV\u002FFCF implied\n- Median implied price\n- Current price\n- Premium\u002Fdiscount (%)\n\n### Tab 7: Balance Sheet & Capital\nLeverage and capital returns:\n- Total Debt, Cash, Net Debt\n- Net Debt\u002FEBITDA\n- Trailing 4Q: OCF, CapEx, FCF\n- FCF Yield\n- Shareholder Yield (buybacks + dividends)\n\n### Tab 8: Raw Data\nFull quarterly appendix for each company:\n- All 8 quarters of financial data\n- All KPIs by quarter\n- All growth rates and margins by quarter\n- Complete data backing the summary tabs\n\n**Styling requirements:**\n- Apply the design system color palette (Navy #1B2A4A headers, Steel Blue #4A6FA5 accents)\n- Number formatting per `..\u002Fdesign-system.md` conventions\n- Bold headers, freeze panes on all tabs\n- Conditional formatting: green for positive growth, red for negative\n- Auto-adjust column widths\n\nThe workbook generation should:\n1. Use the best available spreadsheet-generation library\n2. Construct all 8 worksheets programmatically\n3. Apply styling (bold headers, number formats, colors)\n4. Generate the `.xlsx` file\n5. Save the workbook as `reports\u002F{TARGET_TICKER}_comp_sheet_{DATE}.xlsx`\n\n## 6. Output Summary\n\nAfter generating the Excel workbook, provide a concise summary highlighting:\n\n**Target positioning vs peers**:\n- Where does it rank on growth, margins, and valuation?\n- Quartile positioning across key metrics\n\n**Most differentiated KPIs**:\n- Which operational metrics set the target apart (positive or negative)?\n- Notable outliers in the KPI matrix\n\n**Implied valuation range**:\n- What does the peer group suggest the stock is worth?\n- Premium\u002Fdiscount vs current price\n- Which methodology drives the highest\u002Flowest implied value?\n\n**Key risk**:\n- What's the biggest vulnerability the comp sheet reveals (e.g., premium valuation with decelerating KPIs, margins below peers, concentration risk)?\n\nAll financial figures in the summary must use Daloopa citation format: [$X.XX million](https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id})\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,49,63,90,95,102,153,158,203,208,213,219,224,239,262,272,282,375,400,428,433,526,538,544,549,620,626,631,639,647,655,663,671,689,697,710,718,744,750,762,767,774,779,817,823,828,851,857,862,885,891,896,924,930,935,973,979,984,1022,1028,1033,1059,1065,1070,1093,1101,1136,1141,1183,1189,1194,1204,1217,1226,1239,1248,1266,1275,1283],{"type":42,"tag":43,"props":44,"children":45},"element","p",{},[46],{"type":47,"value":48},"text","Build a multi-company industry comp sheet Excel model for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.",{"type":42,"tag":43,"props":50,"children":51},{},[52,54,61],{"type":47,"value":53},"This produces an interactive ",{"type":42,"tag":55,"props":56,"children":58},"code",{"className":57},[],[59],{"type":47,"value":60},".xlsx",{"type":47,"value":62}," workbook — the kind of comp sheet every analyst on a coverage team maintains. Multi-company, multi-tab, with deep operational KPIs alongside standard financials.",{"type":42,"tag":43,"props":64,"children":65},{},[66,88],{"type":42,"tag":67,"props":68,"children":69},"strong",{},[70,72,78,80,86],{"type":47,"value":71},"Before starting, read ",{"type":42,"tag":55,"props":73,"children":75},{"className":74},[],[76],{"type":47,"value":77},"..\u002Fdata-access.md",{"type":47,"value":79}," for data access methods and ",{"type":42,"tag":55,"props":81,"children":83},{"className":82},[],[84],{"type":47,"value":85},"..\u002Fdesign-system.md",{"type":47,"value":87}," for formatting conventions.",{"type":47,"value":89}," Follow the data access detection logic and design system throughout this skill.",{"type":42,"tag":43,"props":91,"children":92},{},[93],{"type":47,"value":94},"Follow these steps:",{"type":42,"tag":96,"props":97,"children":99},"h2",{"id":98},"_1-company-peer-setup",[100],{"type":47,"value":101},"1. Company & Peer Setup",{"type":42,"tag":43,"props":103,"children":104},{},[105,107,113,115,121,123,129,131,136,138,144,146,151],{"type":47,"value":106},"Look up the target company by ticker using ",{"type":42,"tag":55,"props":108,"children":110},{"className":109},[],[111],{"type":47,"value":112},"discover_companies",{"type":47,"value":114},". Capture ",{"type":42,"tag":55,"props":116,"children":118},{"className":117},[],[119],{"type":47,"value":120},"company_id",{"type":47,"value":122},", ",{"type":42,"tag":55,"props":124,"children":126},{"className":125},[],[127],{"type":47,"value":128},"latest_calendar_quarter",{"type":47,"value":130}," (anchor for all period calculations — see ",{"type":42,"tag":55,"props":132,"children":134},{"className":133},[],[135],{"type":47,"value":77},{"type":47,"value":137}," Section 1.5), and ",{"type":42,"tag":55,"props":139,"children":141},{"className":140},[],[142],{"type":47,"value":143},"latest_fiscal_quarter",{"type":47,"value":145},". Note the firm name for report attribution (default: \"Daloopa\") — see ",{"type":42,"tag":55,"props":147,"children":149},{"className":148},[],[150],{"type":47,"value":77},{"type":47,"value":152}," Section 4.5.",{"type":42,"tag":43,"props":154,"children":155},{},[156],{"type":47,"value":157},"Then identify 6-10 comparable companies using the same logic as the comps skill:",{"type":42,"tag":159,"props":160,"children":161},"ul",{},[162,173,183,193],{"type":42,"tag":163,"props":164,"children":165},"li",{},[166,171],{"type":42,"tag":67,"props":167,"children":168},{},[169],{"type":47,"value":170},"Direct competitors",{"type":47,"value":172}," in the same market",{"type":42,"tag":163,"props":174,"children":175},{},[176,181],{"type":42,"tag":67,"props":177,"children":178},{},[179],{"type":47,"value":180},"Business model peers",{"type":47,"value":182}," (similar revenue model)",{"type":42,"tag":163,"props":184,"children":185},{},[186,191],{"type":42,"tag":67,"props":187,"children":188},{},[189],{"type":47,"value":190},"Size peers",{"type":47,"value":192}," (similar market cap range)",{"type":42,"tag":163,"props":194,"children":195},{},[196,201],{"type":42,"tag":67,"props":197,"children":198},{},[199],{"type":47,"value":200},"Growth profile peers",{"type":47,"value":202}," (similar growth rate)",{"type":42,"tag":43,"props":204,"children":205},{},[206],{"type":47,"value":207},"Look up all peer company_ids via Daloopa. If a peer isn't available in Daloopa, include it with market data only and note the limitation.",{"type":42,"tag":43,"props":209,"children":210},{},[211],{"type":47,"value":212},"List the full peer group with brief justification for each.",{"type":42,"tag":96,"props":214,"children":216},{"id":215},"_2-deep-data-gathering",[217],{"type":47,"value":218},"2. Deep Data Gathering",{"type":42,"tag":43,"props":220,"children":221},{},[222],{"type":47,"value":223},"For each company (target + all peers), pull from Daloopa:",{"type":42,"tag":43,"props":225,"children":226},{},[227],{"type":42,"tag":67,"props":228,"children":229},{},[230,232,237],{"type":47,"value":231},"Calculate 8 quarters backward from ",{"type":42,"tag":55,"props":233,"children":235},{"className":234},[],[236],{"type":47,"value":128},{"type":47,"value":238},". Pull financials:",{"type":42,"tag":159,"props":240,"children":241},{},[242,247,252,257],{"type":42,"tag":163,"props":243,"children":244},{},[245],{"type":47,"value":246},"Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS",{"type":42,"tag":163,"props":248,"children":249},{},[250],{"type":47,"value":251},"Operating Cash Flow, Capital Expenditures, D&A",{"type":42,"tag":163,"props":253,"children":254},{},[255],{"type":47,"value":256},"Free Cash Flow (compute as OCF - CapEx)",{"type":42,"tag":163,"props":258,"children":259},{},[260],{"type":47,"value":261},"R&D Expense, SG&A (where available)",{"type":42,"tag":43,"props":263,"children":264},{},[265,270],{"type":42,"tag":67,"props":266,"children":267},{},[268],{"type":47,"value":269},"Segment revenue breakdown",{"type":47,"value":271}," (all available segments, 8 quarters)",{"type":42,"tag":43,"props":273,"children":274},{},[275,280],{"type":42,"tag":67,"props":276,"children":277},{},[278],{"type":47,"value":279},"Company-specific operational KPIs",{"type":47,"value":281}," — use the 9-sector taxonomy to know what to search for:",{"type":42,"tag":159,"props":283,"children":284},{},[285,295,305,315,325,335,345,355,365],{"type":42,"tag":163,"props":286,"children":287},{},[288,293],{"type":42,"tag":67,"props":289,"children":290},{},[291],{"type":47,"value":292},"SaaS\u002FCloud",{"type":47,"value":294},": ARR, net revenue retention, RPO\u002FcRPO, customers >$100K, cloud gross margin",{"type":42,"tag":163,"props":296,"children":297},{},[298,303],{"type":42,"tag":67,"props":299,"children":300},{},[301],{"type":47,"value":302},"Consumer Tech",{"type":47,"value":304},": DAU\u002FMAU, ARPU, engagement metrics, installed base, paid subscribers",{"type":42,"tag":163,"props":306,"children":307},{},[308,313],{"type":42,"tag":67,"props":309,"children":310},{},[311],{"type":47,"value":312},"E-commerce\u002FMarketplace",{"type":47,"value":314},": GMV, take rate, active buyers\u002Fsellers, order frequency",{"type":42,"tag":163,"props":316,"children":317},{},[318,323],{"type":42,"tag":67,"props":319,"children":320},{},[321],{"type":47,"value":322},"Retail",{"type":47,"value":324},": same-store sales, store count, average ticket, transactions",{"type":42,"tag":163,"props":326,"children":327},{},[328,333],{"type":42,"tag":67,"props":329,"children":330},{},[331],{"type":47,"value":332},"Telecom\u002FMedia",{"type":47,"value":334},": subscribers, churn, ARPU, content spend",{"type":42,"tag":163,"props":336,"children":337},{},[338,343],{"type":42,"tag":67,"props":339,"children":340},{},[341],{"type":47,"value":342},"Hardware",{"type":47,"value":344},": units shipped, ASP, attach rate, installed base",{"type":42,"tag":163,"props":346,"children":347},{},[348,353],{"type":42,"tag":67,"props":349,"children":350},{},[351],{"type":47,"value":352},"Financial Services",{"type":47,"value":354},": AUM, NIM, loan growth, credit quality metrics, fee income ratio",{"type":42,"tag":163,"props":356,"children":357},{},[358,363],{"type":42,"tag":67,"props":359,"children":360},{},[361],{"type":47,"value":362},"Pharma\u002FBiotech",{"type":47,"value":364},": pipeline stage, patient starts, scripts, market share",{"type":42,"tag":163,"props":366,"children":367},{},[368,373],{"type":42,"tag":67,"props":369,"children":370},{},[371],{"type":47,"value":372},"Industrials\u002FEnergy",{"type":47,"value":374},": backlog, book-to-bill, utilization, production volumes, reserves",{"type":42,"tag":43,"props":376,"children":377},{},[378,383,385,391,393,398],{"type":42,"tag":67,"props":379,"children":380},{},[381],{"type":47,"value":382},"Stock prices & valuation multiples:",{"type":47,"value":384},"\nUse ",{"type":42,"tag":55,"props":386,"children":388},{"className":387},[],[389],{"type":47,"value":390},"get_stock_prices",{"type":47,"value":392}," (see ",{"type":42,"tag":55,"props":394,"children":396},{"className":395},[],[397],{"type":47,"value":77},{"type":47,"value":399}," Section 1.7) to pull prices for ALL companies in a single batch call. Get:",{"type":42,"tag":159,"props":401,"children":402},{},[403,416],{"type":42,"tag":163,"props":404,"children":405},{},[406,408,414],{"type":47,"value":407},"Current price: ",{"type":42,"tag":55,"props":409,"children":411},{"className":410},[],[412],{"type":47,"value":413},"dates",{"type":47,"value":415}," = 3 most recent calendar days for all company_ids",{"type":42,"tag":163,"props":417,"children":418},{},[419,421,426],{"type":47,"value":420},"Quarter-end prices: ",{"type":42,"tag":55,"props":422,"children":424},{"className":423},[],[425],{"type":47,"value":413},{"type":47,"value":427}," = quarter-end dates matching the financial periods (for historical multiples)",{"type":42,"tag":43,"props":429,"children":430},{},[431],{"type":47,"value":432},"Then compute valuation metrics by combining stock prices with Daloopa fundamentals:",{"type":42,"tag":159,"props":434,"children":435},{},[436,446,456,466,476,486,496,506,516],{"type":42,"tag":163,"props":437,"children":438},{},[439,444],{"type":42,"tag":67,"props":440,"children":441},{},[442],{"type":47,"value":443},"Market Cap",{"type":47,"value":445}," = Close price × Diluted shares outstanding",{"type":42,"tag":163,"props":447,"children":448},{},[449,454],{"type":42,"tag":67,"props":450,"children":451},{},[452],{"type":47,"value":453},"Enterprise Value",{"type":47,"value":455}," = Market Cap + Total Debt - Cash",{"type":42,"tag":163,"props":457,"children":458},{},[459,464],{"type":42,"tag":67,"props":460,"children":461},{},[462],{"type":47,"value":463},"P\u002FE (trailing)",{"type":47,"value":465}," = Market Cap \u002F Net Income (trailing 4Q)",{"type":42,"tag":163,"props":467,"children":468},{},[469,474],{"type":42,"tag":67,"props":470,"children":471},{},[472],{"type":47,"value":473},"EV\u002FEBITDA",{"type":47,"value":475}," = EV \u002F EBITDA (trailing 4Q)",{"type":42,"tag":163,"props":477,"children":478},{},[479,484],{"type":42,"tag":67,"props":480,"children":481},{},[482],{"type":47,"value":483},"P\u002FS",{"type":47,"value":485}," = Market Cap \u002F Revenue (trailing 4Q)",{"type":42,"tag":163,"props":487,"children":488},{},[489,494],{"type":42,"tag":67,"props":490,"children":491},{},[492],{"type":47,"value":493},"P\u002FB",{"type":47,"value":495}," = Market Cap \u002F Total Equity",{"type":42,"tag":163,"props":497,"children":498},{},[499,504],{"type":42,"tag":67,"props":500,"children":501},{},[502],{"type":47,"value":503},"EV\u002FFCF",{"type":47,"value":505}," = EV \u002F Free Cash Flow (trailing 4Q)",{"type":42,"tag":163,"props":507,"children":508},{},[509,514],{"type":42,"tag":67,"props":510,"children":511},{},[512],{"type":47,"value":513},"FCF Yield",{"type":47,"value":515}," = FCF (trailing 4Q) \u002F Market Cap",{"type":42,"tag":163,"props":517,"children":518},{},[519,524],{"type":42,"tag":67,"props":520,"children":521},{},[522],{"type":47,"value":523},"Dividend Yield",{"type":47,"value":525}," = Dividends Paid (trailing 4Q) \u002F Market Cap",{"type":42,"tag":43,"props":527,"children":528},{},[529,531,536],{"type":47,"value":530},"For beta, use web search (see ",{"type":42,"tag":55,"props":532,"children":534},{"className":533},[],[535],{"type":47,"value":77},{"type":47,"value":537}," Section 2). For forward multiples, use consensus estimates if available (Section 3).",{"type":42,"tag":96,"props":539,"children":541},{"id":540},"_3-kpi-discovery-mapping",[542],{"type":47,"value":543},"3. KPI Discovery & Mapping",{"type":42,"tag":43,"props":545,"children":546},{},[547],{"type":47,"value":548},"After pulling data, build the KPI mapping:",{"type":42,"tag":159,"props":550,"children":551},{},[552,557,615],{"type":42,"tag":163,"props":553,"children":554},{},[555],{"type":47,"value":556},"Which KPIs are available for which companies? 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Output Summary",{"type":42,"tag":43,"props":1190,"children":1191},{},[1192],{"type":47,"value":1193},"After generating the Excel workbook, provide a concise summary highlighting:",{"type":42,"tag":43,"props":1195,"children":1196},{},[1197,1202],{"type":42,"tag":67,"props":1198,"children":1199},{},[1200],{"type":47,"value":1201},"Target positioning vs peers",{"type":47,"value":1203},":",{"type":42,"tag":159,"props":1205,"children":1206},{},[1207,1212],{"type":42,"tag":163,"props":1208,"children":1209},{},[1210],{"type":47,"value":1211},"Where does it rank on growth, margins, and valuation?",{"type":42,"tag":163,"props":1213,"children":1214},{},[1215],{"type":47,"value":1216},"Quartile positioning across key metrics",{"type":42,"tag":43,"props":1218,"children":1219},{},[1220,1225],{"type":42,"tag":67,"props":1221,"children":1222},{},[1223],{"type":47,"value":1224},"Most differentiated KPIs",{"type":47,"value":1203},{"type":42,"tag":159,"props":1227,"children":1228},{},[1229,1234],{"type":42,"tag":163,"props":1230,"children":1231},{},[1232],{"type":47,"value":1233},"Which operational metrics set the target apart (positive or negative)?",{"type":42,"tag":163,"props":1235,"children":1236},{},[1237],{"type":47,"value":1238},"Notable outliers in the KPI matrix",{"type":42,"tag":43,"props":1240,"children":1241},{},[1242,1247],{"type":42,"tag":67,"props":1243,"children":1244},{},[1245],{"type":47,"value":1246},"Implied valuation range",{"type":47,"value":1203},{"type":42,"tag":159,"props":1249,"children":1250},{},[1251,1256,1261],{"type":42,"tag":163,"props":1252,"children":1253},{},[1254],{"type":47,"value":1255},"What does the peer group suggest the stock is worth?",{"type":42,"tag":163,"props":1257,"children":1258},{},[1259],{"type":47,"value":1260},"Premium\u002Fdiscount vs current price",{"type":42,"tag":163,"props":1262,"children":1263},{},[1264],{"type":47,"value":1265},"Which methodology drives the highest\u002Flowest implied value?",{"type":42,"tag":43,"props":1267,"children":1268},{},[1269,1274],{"type":42,"tag":67,"props":1270,"children":1271},{},[1272],{"type":47,"value":1273},"Key risk",{"type":47,"value":1203},{"type":42,"tag":159,"props":1276,"children":1277},{},[1278],{"type":42,"tag":163,"props":1279,"children":1280},{},[1281],{"type":47,"value":1282},"What's the biggest vulnerability the comp sheet reveals (e.g., premium valuation with decelerating KPIs, margins below peers, concentration risk)?",{"type":42,"tag":43,"props":1284,"children":1285},{},[1286,1288],{"type":47,"value":1287},"All financial figures in the summary must use Daloopa citation format: ",{"type":42,"tag":1289,"props":1290,"children":1294},"a",{"href":1291,"rel":1292},"https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F%7Bfundamental_id%7D",[1293],"nofollow",[1295],{"type":47,"value":1296},"$X.XX million",{"items":1298,"total":1503},[1299,1320,1343,1360,1376,1395,1414,1430,1446,1460,1472,1487],{"slug":1300,"name":1300,"fn":1301,"description":1302,"org":1303,"tags":1304,"stars":1317,"repoUrl":1318,"updatedAt":1319},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1305,1308,1311,1314],{"name":1306,"slug":1307,"type":15},"Documents","documents",{"name":1309,"slug":1310,"type":15},"Healthcare","healthcare",{"name":1312,"slug":1313,"type":15},"Insurance","insurance",{"name":1315,"slug":1316,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":1321,"name":1321,"fn":1322,"description":1323,"org":1324,"tags":1325,"stars":1340,"repoUrl":1341,"updatedAt":1342},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1326,1329,1331,1334,1337],{"name":1327,"slug":1328,"type":15},".NET","dotnet",{"name":1330,"slug":1321,"type":15},"ASP.NET Core",{"name":1332,"slug":1333,"type":15},"Blazor","blazor",{"name":1335,"slug":1336,"type":15},"C#","csharp",{"name":1338,"slug":1339,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":1344,"name":1344,"fn":1345,"description":1346,"org":1347,"tags":1348,"stars":1340,"repoUrl":1341,"updatedAt":1359},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1349,1352,1355,1358],{"name":1350,"slug":1351,"type":15},"Apps SDK","apps-sdk",{"name":1353,"slug":1354,"type":15},"ChatGPT","chatgpt",{"name":1356,"slug":1357,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":1361,"name":1361,"fn":1362,"description":1363,"org":1364,"tags":1365,"stars":1340,"repoUrl":1341,"updatedAt":1375},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1366,1369,1372],{"name":1367,"slug":1368,"type":15},"API Development","api-development",{"name":1370,"slug":1371,"type":15},"CLI","cli",{"name":1373,"slug":1374,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":1377,"name":1377,"fn":1378,"description":1379,"org":1380,"tags":1381,"stars":1340,"repoUrl":1341,"updatedAt":1394},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1382,1385,1388,1391],{"name":1383,"slug":1384,"type":15},"Cloudflare","cloudflare",{"name":1386,"slug":1387,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":1389,"slug":1390,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":1392,"slug":1393,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":1396,"name":1396,"fn":1397,"description":1398,"org":1399,"tags":1400,"stars":1340,"repoUrl":1341,"updatedAt":1413},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1401,1404,1407,1410],{"name":1402,"slug":1403,"type":15},"Productivity","productivity",{"name":1405,"slug":1406,"type":15},"Project Management","project-management",{"name":1408,"slug":1409,"type":15},"Strategy","strategy",{"name":1411,"slug":1412,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":1415,"name":1415,"fn":1416,"description":1417,"org":1418,"tags":1419,"stars":1340,"repoUrl":1341,"updatedAt":1429},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1420,1423,1425,1428],{"name":1421,"slug":1422,"type":15},"Design","design",{"name":1424,"slug":1415,"type":15},"Figma",{"name":1426,"slug":1427,"type":15},"Frontend","frontend",{"name":1356,"slug":1357,"type":15},"2026-04-12T05:06:47.939943",{"slug":1431,"name":1431,"fn":1432,"description":1433,"org":1434,"tags":1435,"stars":1340,"repoUrl":1341,"updatedAt":1445},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1436,1437,1440,1441,1442],{"name":1421,"slug":1422,"type":15},{"name":1438,"slug":1439,"type":15},"Design System","design-system",{"name":1424,"slug":1415,"type":15},{"name":1426,"slug":1427,"type":15},{"name":1443,"slug":1444,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":1447,"name":1447,"fn":1448,"description":1449,"org":1450,"tags":1451,"stars":1340,"repoUrl":1341,"updatedAt":1459},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1452,1453,1454,1457,1458],{"name":1421,"slug":1422,"type":15},{"name":1438,"slug":1439,"type":15},{"name":1455,"slug":1456,"type":15},"Documentation","documentation",{"name":1424,"slug":1415,"type":15},{"name":1426,"slug":1427,"type":15},"2026-05-16T06:07:47.821474",{"slug":1461,"name":1461,"fn":1462,"description":1463,"org":1464,"tags":1465,"stars":1340,"repoUrl":1341,"updatedAt":1471},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1466,1467,1468,1469,1470],{"name":1421,"slug":1422,"type":15},{"name":1424,"slug":1415,"type":15},{"name":1426,"slug":1427,"type":15},{"name":1443,"slug":1444,"type":15},{"name":1338,"slug":1339,"type":15},"2026-05-16T06:07:40.583615",{"slug":1473,"name":1473,"fn":1474,"description":1475,"org":1476,"tags":1477,"stars":1340,"repoUrl":1341,"updatedAt":1486},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1478,1481,1482,1485],{"name":1479,"slug":1480,"type":15},"Animation","animation",{"name":1373,"slug":1374,"type":15},{"name":1483,"slug":1484,"type":15},"Creative","creative",{"name":1421,"slug":1422,"type":15},"2026-05-02T05:31:48.48485",{"slug":1488,"name":1488,"fn":1489,"description":1490,"org":1491,"tags":1492,"stars":1340,"repoUrl":1341,"updatedAt":1502},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1493,1494,1495,1498,1501],{"name":1483,"slug":1484,"type":15},{"name":1421,"slug":1422,"type":15},{"name":1496,"slug":1497,"type":15},"Image Generation","image-generation",{"name":1499,"slug":1500,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":1505,"total":1618},[1506,1522,1538,1550,1568,1586,1606],{"slug":1507,"name":1507,"fn":1508,"description":1509,"org":1510,"tags":1511,"stars":25,"repoUrl":26,"updatedAt":27},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1512,1515,1518,1521],{"name":1513,"slug":1514,"type":15},"Accessibility","accessibility",{"name":1516,"slug":1517,"type":15},"Charts","charts",{"name":1519,"slug":1520,"type":15},"Data Visualization","data-visualization",{"name":1421,"slug":1422,"type":15},{"slug":1523,"name":1523,"fn":1524,"description":1525,"org":1526,"tags":1527,"stars":25,"repoUrl":26,"updatedAt":1537},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1528,1531,1534],{"name":1529,"slug":1530,"type":15},"Agents","agents",{"name":1532,"slug":1533,"type":15},"Browser Automation","browser-automation",{"name":1535,"slug":1536,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":1539,"name":1539,"fn":1540,"description":1541,"org":1542,"tags":1543,"stars":25,"repoUrl":26,"updatedAt":1549},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1544,1545,1548],{"name":1532,"slug":1533,"type":15},{"name":1546,"slug":1547,"type":15},"Local Development","local-development",{"name":1535,"slug":1536,"type":15},"2026-04-06T18:41:17.526867",{"slug":1551,"name":1551,"fn":1552,"description":1553,"org":1554,"tags":1555,"stars":25,"repoUrl":26,"updatedAt":1567},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1556,1557,1558,1561,1564],{"name":1529,"slug":1530,"type":15},{"name":1389,"slug":1390,"type":15},{"name":1559,"slug":1560,"type":15},"SDK","sdk",{"name":1562,"slug":1563,"type":15},"Serverless","serverless",{"name":1565,"slug":1566,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":1569,"name":1569,"fn":1570,"description":1571,"org":1572,"tags":1573,"stars":25,"repoUrl":26,"updatedAt":1585},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1574,1575,1578,1581,1582],{"name":1426,"slug":1427,"type":15},{"name":1576,"slug":1577,"type":15},"React","react",{"name":1579,"slug":1580,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":1443,"slug":1444,"type":15},{"name":1583,"slug":1584,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":1587,"name":1587,"fn":1588,"description":1589,"org":1590,"tags":1591,"stars":25,"repoUrl":26,"updatedAt":1605},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1592,1595,1598,1601,1604],{"name":1593,"slug":1594,"type":15},"AI Infrastructure","ai-infrastructure",{"name":1596,"slug":1597,"type":15},"Cost Optimization","cost-optimization",{"name":1599,"slug":1600,"type":15},"LLM","llm",{"name":1602,"slug":1603,"type":15},"Performance","performance",{"name":1583,"slug":1584,"type":15},"2026-04-06T18:40:44.377464",{"slug":1607,"name":1607,"fn":1608,"description":1609,"org":1610,"tags":1611,"stars":25,"repoUrl":26,"updatedAt":1617},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1612,1613,1616],{"name":1596,"slug":1597,"type":15},{"name":1614,"slug":1615,"type":15},"Database","database",{"name":1599,"slug":1600,"type":15},"2026-04-06T18:41:08.513425",600]