[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-precedent-transactions":3,"mdc--w83048-key":36,"related-org-openai-precedent-transactions":1025,"related-repo-openai-precedent-transactions":1232},{"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},"precedent-transactions","analyze precedent M&A transactions","Precedent M&A transactions analysis with deal multiples and acquisition history",{"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},"Research","research","tag",{"name":17,"slug":18,"type":15},"Valuation","valuation",{"name":20,"slug":21,"type":15},"Finance","finance",{"name":23,"slug":24,"type":15},"Investment Banking","investment-banking",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\u002Fprecedent-transactions","---\nname: precedent-transactions\ndescription: Precedent M&A transactions analysis with deal multiples and acquisition\n  history\n---\n\nBuild a precedent transactions analysis for the company named in the user's request. If no ticker or company is provided, ask for one before proceeding.\n\nThis is the third pillar of valuation (alongside trading comps and DCF) — it answers: what have acquirers actually paid for businesses like this one? The output is two tables: comparable M&A transactions with deal multiples, and the subject company's own acquisition history.\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 Lookup\nLook up the company by ticker using `discover_companies`. Capture:\n- `company_id`\n- `latest_calendar_quarter` — anchor for all period calculations below (see `..\u002Fdata-access.md` Section 1.5)\n- `latest_fiscal_quarter`\n- Firm name for report attribution (default: \"Daloopa\") — see `..\u002Fdata-access.md` Section 4.5\n\nIdentify:\n- Full legal company name\n- Primary stock exchange and reporting currency\n- Country of domicile and primary operations\n- Industry and sub-sector\n- Approximate revenue and EBITDA scale (to calibrate comparable deal sizing)\n\n## 2. Subject Company Financials\nCalculate 4 quarters backward from `latest_calendar_quarter`. Pull from Daloopa:\n- Revenue (compute trailing 4Q \u002F LTM total)\n- EBITDA (compute trailing 4Q; if not available, use Operating Income + D&A, label \"(calc.)\")\n- Operating Income\n- Net Income\n- Free Cash Flow (OCF - CapEx, label \"(calc.)\")\n\nThese serve as the reference point for comparing deal multiples — what would an acquirer be paying relative to this company's current financials?\n\n## 3. Identify Comparable Precedent Transactions\nFind 8-15 completed M&A transactions from the last 7-10 years involving target companies comparable to the subject. \"Comparable\" means:\n- Same industry and sub-sector\n- Similar business model (e.g., SaaS, semiconductor IP, consumer internet, industrials)\n- Roughly comparable scale — within ~0.5x-4x of the subject's revenue\n- Completed transactions only (not rumored, not pending)\n\n**Research sources in priority order:**\n1. **SEC EDGAR** (for US targets) — SC TO, DEFM14A, 8-K filings disclose EV and deal terms\n2. **Equivalent regulators for non-US targets:** FCA (UK), EDINET (Japan), HKEx (Hong Kong), SEDAR+ (Canada), ASX (Australia)\n3. **Official investor relations press releases** from acquirer or target\n4. **Reputable financial news:** Reuters, Bloomberg, Wall Street Journal, Financial Times\n\nUse web search to identify deals: `\"{industry} acquisitions {sub-sector} last 10 years\"`, `\"{TICKER} comparable M&A transactions\"`, `\"{sector} deal comps precedent transactions\"`.\n\n**Do NOT use:** finance blogs, Seeking Alpha, Reddit, anonymous wiki contributions, or aggregators without a traceable primary source.\n\nFor each transaction, capture:\n- Announcement date\n- Acquirer name\n- Target name\n- Transaction Enterprise Value\n- Deal consideration (All Cash \u002F All Stock \u002F Cash + Stock)\n- Source (press release URL, SEC filing, or regulatory filing)\n\n## 4. Source Target Financials via Daloopa\nFor each target company in the precedent transactions table, source LTM Revenue and EBITDA from Daloopa:\n\n1. **Look up the target** using `discover_companies` with the target's ticker or name\n2. **Find relevant series** using `discover_company_series` with keywords `[\"revenue\", \"EBITDA\"]` and the appropriate period (the last complete fiscal year before the deal announcement)\n3. **Pull the data** using `get_company_fundamentals` with the discovered series IDs\n4. For EBITDA, look for series containing \"Adjusted EBITDA\", \"EBITDA\", or fall back to \"Operating Income\" + D&A\n5. If a target is not in Daloopa (e.g., pre-IPO targets, private companies), fall back to SEC filings, press releases, or regulatory filings\n\n**Daloopa is the primary source.** Only fall back to other sources when a target is genuinely unavailable in the database.\n\n## 5. Compute Deal Multiples\nFor each transaction where both EV and financials are available:\n- **EV\u002FRevenue** = Transaction EV ÷ LTM Revenue\n- **EV\u002FEBITDA** = Transaction EV ÷ LTM EBITDA\n- Round to one decimal, append \"x\"\n- If a figure cannot be sourced, mark as **N\u002FA** — do not estimate\n\nCompute summary statistics (excluding N\u002FA values):\n- 75th Percentile\n- **Average** (bold)\n- **Median** (bold)\n- 25th Percentile\n\nIf fewer than 3 valid data points exist for a multiple, note that the statistic is not meaningful.\n\n## 6. Subject Company's Acquisition History\nFind deals where the subject company itself was the acquirer. Sources: company IR page, SEC 8-K or equivalent filings, Reuters\u002FBloomberg\u002FWSJ.\n\nFor each acquisition, capture:\n- Date\n- Target name\n- Deal value (if disclosed)\n- Consideration (Cash \u002F Stock \u002F Mix)\n- Strategic rationale (one sentence from press release or filing)\n\n## 7. Implied Valuation for Subject Company\nApply the precedent transaction multiples to the subject's current financials:\n\n| Methodology | Percentile | Multiple | Subject LTM Metric | Implied EV |\n|---|---|---|---|---|\n| EV\u002FRevenue | Median | XX.Xx | $XXX | $XXX |\n| EV\u002FRevenue | 25th-75th | XX.Xx-XX.Xx | $XXX | $XXX-$XXX |\n| EV\u002FEBITDA | Median | XX.Xx | $XXX | $XXX |\n| EV\u002FEBITDA | 25th-75th | XX.Xx-XX.Xx | $XXX | $XXX-$XXX |\n\nConvert implied EV to implied equity value (EV - Net Debt) and implied share price where market data is available (see `..\u002Fdata-access.md` Section 2). Compare to current market price.\n\n**Context matters more than precision:**\n- Precedent transaction multiples are snapshots from specific deal contexts (competitive auctions, strategic premiums, distressed sales). Note which deals had unusual dynamics.\n- Control premiums are embedded in these multiples — a public market investor should not expect to realize the full precedent transaction value unless a takeout actually happens.\n- If the current market cap is well below precedent transaction implied value, that's a signal of takeout optionality, not necessarily undervaluation.\n\n## 8. Deal Environment Commentary\nSearch filings and news for context on the M&A environment:\n- Search: `\"{industry} M&A outlook {current_year}\"` — deal activity trends\n- Search: `\"{TICKER} acquisition target rumors\"` — is the subject itself a takeout candidate?\n\nSummarize in 3-5 bullets:\n- Is deal activity in this sector accelerating or declining?\n- What are typical premiums being paid (control premium trends)?\n- Are strategic buyers or financial sponsors (PE) driving activity?\n- Any regulatory headwinds to deals in this space (antitrust scrutiny)?\n- Is the subject company a plausible acquisition target? Why or why not?\n\n## 9. Save Report\nSave to `reports\u002F{TICKER}_precedent_transactions.html` using the HTML report template from `..\u002Fdesign-system.md`. Write the full analysis as styled HTML with the design system CSS inlined. This is the final deliverable — no intermediate markdown step needed.\n\nThe report should include interactive features:\n- **Clickable acquirer names** in Table 1 that open a modal showing all source links for that transaction (press release, SEC filing, Daloopa data links). Implement with `data-` attributes and safe DOM methods (`createElement`, `textContent`, `appendChild`) — never `innerHTML`.\n- **Consideration badges** styled inline: All Cash (green background), All Stock (purple background), Cash + Stock (amber background).\n\nStructure the report with these sections:\n\n```\n\u003Ch1>{Company Name} ({TICKER}) — Precedent Transactions Analysis\u003C\u002Fh1>\n\u003Cp>Generated: {date}\u003C\u002Fp>\n\n\u003Ch2>Summary\u003C\u002Fh2>\n{2-3 sentences: What do precedent transactions imply for this company's valuation? How does it compare to the current market price?}\n\n\u003Ch2>Subject Company Overview\u003C\u002Fh2>\n{Exchange, currency, industry, LTM Revenue and EBITDA with Daloopa citations}\n{Note: \"Revenue and EBITDA sourced from Daloopa where available\"}\n\n\u003Ch2>Selected Precedent Transactions\u003C\u002Fh2>\n\u003Ctable>\n| Date | Acquirer | Target | EV ($M) | LTM Rev ($M) | LTM EBITDA ($M) | EV\u002FRev | EV\u002FEBITDA | Consideration |\n{data rows with Daloopa-cited financials, footnote superscripts, clickable acquirers}\n| 75th Percentile | | | | | | XX.Xx | XX.Xx | |\n| **Average** | | | | | | **XX.Xx** | **XX.Xx** | |\n| **Median** | | | | | | **XX.Xx** | **XX.Xx** | |\n| 25th Percentile | | | | | | XX.Xx | XX.Xx | |\n\u003C\u002Ftable>\n\n\u003Ch2>Implied Valuation\u003C\u002Fh2>\n\u003Ctable>\n| Methodology | Multiple | Subject Metric | Implied EV | Implied Equity | Implied Price | vs Current |\n{valuation bridge using median and range multiples}\n\u003C\u002Ftable>\n\n\u003Ch2>{Company Name} Acquisition History\u003C\u002Fh2>\n\u003Ctable>\n| Date | Target | Deal Value | Consideration | Strategic Rationale |\n{company's own M&A deals}\n\u003C\u002Ftable>\n\n\u003Ch2>Deal Environment\u003C\u002Fh2>\n\u003Cul>{3-5 bullets on sector M&A trends, control premiums, takeout potential}\u003C\u002Ful>\n\n\u003Ch2>Sources\u003C\u002Fh2>\n{Numbered footnote list — each deal with press release link, SEC filing, Daloopa data links}\n{Data sourced from Daloopa attribution}\n```\n\nAll financial figures from Daloopa must use citation format: `\u003Ca href=\"https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id}\">$X.XX million\u003C\u002Fa>`\n\nTell the user where the HTML report was saved.\n\nHighlight: what precedent transactions imply about the company's takeout value, how it compares to the current market price, and whether the sector M&A environment supports deal activity.\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,49,54,82,87,94,107,160,165,193,199,211,239,244,250,255,278,286,330,358,368,373,406,412,417,489,499,505,510,550,555,587,592,598,603,608,635,641,646,786,798,806,824,830,835,863,868,896,902,922,927,987,992,1004,1015,1020],{"type":42,"tag":43,"props":44,"children":45},"element","p",{},[46],{"type":47,"value":48},"text","Build a precedent transactions analysis 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],{"type":47,"value":53},"This is the third pillar of valuation (alongside trading comps and DCF) — it answers: what have acquirers actually paid for businesses like this one? The output is two tables: comparable M&A transactions with deal multiples, and the subject company's own acquisition history.",{"type":42,"tag":43,"props":55,"children":56},{},[57,80],{"type":42,"tag":58,"props":59,"children":60},"strong",{},[61,63,70,72,78],{"type":47,"value":62},"Before starting, read ",{"type":42,"tag":64,"props":65,"children":67},"code",{"className":66},[],[68],{"type":47,"value":69},"..\u002Fdata-access.md",{"type":47,"value":71}," for data access methods and ",{"type":42,"tag":64,"props":73,"children":75},{"className":74},[],[76],{"type":47,"value":77},"..\u002Fdesign-system.md",{"type":47,"value":79}," for formatting conventions.",{"type":47,"value":81}," Follow the data access detection logic and design system throughout this skill.",{"type":42,"tag":43,"props":83,"children":84},{},[85],{"type":47,"value":86},"Follow these steps:",{"type":42,"tag":88,"props":89,"children":91},"h2",{"id":90},"_1-company-lookup",[92],{"type":47,"value":93},"1. Company Lookup",{"type":42,"tag":43,"props":95,"children":96},{},[97,99,105],{"type":47,"value":98},"Look up the company by ticker using ",{"type":42,"tag":64,"props":100,"children":102},{"className":101},[],[103],{"type":47,"value":104},"discover_companies",{"type":47,"value":106},". Capture:",{"type":42,"tag":108,"props":109,"children":110},"ul",{},[111,121,139,148],{"type":42,"tag":112,"props":113,"children":114},"li",{},[115],{"type":42,"tag":64,"props":116,"children":118},{"className":117},[],[119],{"type":47,"value":120},"company_id",{"type":42,"tag":112,"props":122,"children":123},{},[124,130,132,137],{"type":42,"tag":64,"props":125,"children":127},{"className":126},[],[128],{"type":47,"value":129},"latest_calendar_quarter",{"type":47,"value":131}," — anchor for all period calculations below (see ",{"type":42,"tag":64,"props":133,"children":135},{"className":134},[],[136],{"type":47,"value":69},{"type":47,"value":138}," Section 1.5)",{"type":42,"tag":112,"props":140,"children":141},{},[142],{"type":42,"tag":64,"props":143,"children":145},{"className":144},[],[146],{"type":47,"value":147},"latest_fiscal_quarter",{"type":42,"tag":112,"props":149,"children":150},{},[151,153,158],{"type":47,"value":152},"Firm name for report attribution (default: \"Daloopa\") — see ",{"type":42,"tag":64,"props":154,"children":156},{"className":155},[],[157],{"type":47,"value":69},{"type":47,"value":159}," Section 4.5",{"type":42,"tag":43,"props":161,"children":162},{},[163],{"type":47,"value":164},"Identify:",{"type":42,"tag":108,"props":166,"children":167},{},[168,173,178,183,188],{"type":42,"tag":112,"props":169,"children":170},{},[171],{"type":47,"value":172},"Full legal company name",{"type":42,"tag":112,"props":174,"children":175},{},[176],{"type":47,"value":177},"Primary stock exchange and reporting currency",{"type":42,"tag":112,"props":179,"children":180},{},[181],{"type":47,"value":182},"Country of domicile and primary operations",{"type":42,"tag":112,"props":184,"children":185},{},[186],{"type":47,"value":187},"Industry and sub-sector",{"type":42,"tag":112,"props":189,"children":190},{},[191],{"type":47,"value":192},"Approximate revenue and EBITDA scale (to calibrate comparable deal sizing)",{"type":42,"tag":88,"props":194,"children":196},{"id":195},"_2-subject-company-financials",[197],{"type":47,"value":198},"2. Subject Company Financials",{"type":42,"tag":43,"props":200,"children":201},{},[202,204,209],{"type":47,"value":203},"Calculate 4 quarters backward from ",{"type":42,"tag":64,"props":205,"children":207},{"className":206},[],[208],{"type":47,"value":129},{"type":47,"value":210},". Pull from Daloopa:",{"type":42,"tag":108,"props":212,"children":213},{},[214,219,224,229,234],{"type":42,"tag":112,"props":215,"children":216},{},[217],{"type":47,"value":218},"Revenue (compute trailing 4Q \u002F LTM total)",{"type":42,"tag":112,"props":220,"children":221},{},[222],{"type":47,"value":223},"EBITDA (compute trailing 4Q; if not available, use Operating Income + D&A, label \"(calc.)\")",{"type":42,"tag":112,"props":225,"children":226},{},[227],{"type":47,"value":228},"Operating Income",{"type":42,"tag":112,"props":230,"children":231},{},[232],{"type":47,"value":233},"Net Income",{"type":42,"tag":112,"props":235,"children":236},{},[237],{"type":47,"value":238},"Free Cash Flow (OCF - CapEx, label \"(calc.)\")",{"type":42,"tag":43,"props":240,"children":241},{},[242],{"type":47,"value":243},"These serve as the reference point for comparing deal multiples — what would an acquirer be paying relative to this company's current financials?",{"type":42,"tag":88,"props":245,"children":247},{"id":246},"_3-identify-comparable-precedent-transactions",[248],{"type":47,"value":249},"3. Identify Comparable Precedent Transactions",{"type":42,"tag":43,"props":251,"children":252},{},[253],{"type":47,"value":254},"Find 8-15 completed M&A transactions from the last 7-10 years involving target companies comparable to the subject. \"Comparable\" means:",{"type":42,"tag":108,"props":256,"children":257},{},[258,263,268,273],{"type":42,"tag":112,"props":259,"children":260},{},[261],{"type":47,"value":262},"Same industry and sub-sector",{"type":42,"tag":112,"props":264,"children":265},{},[266],{"type":47,"value":267},"Similar business model (e.g., SaaS, semiconductor IP, consumer internet, industrials)",{"type":42,"tag":112,"props":269,"children":270},{},[271],{"type":47,"value":272},"Roughly comparable scale — within ~0.5x-4x of the subject's revenue",{"type":42,"tag":112,"props":274,"children":275},{},[276],{"type":47,"value":277},"Completed transactions only (not rumored, not pending)",{"type":42,"tag":43,"props":279,"children":280},{},[281],{"type":42,"tag":58,"props":282,"children":283},{},[284],{"type":47,"value":285},"Research sources in priority order:",{"type":42,"tag":287,"props":288,"children":289},"ol",{},[290,300,310,320],{"type":42,"tag":112,"props":291,"children":292},{},[293,298],{"type":42,"tag":58,"props":294,"children":295},{},[296],{"type":47,"value":297},"SEC EDGAR",{"type":47,"value":299}," (for US targets) — SC TO, DEFM14A, 8-K filings disclose EV and deal terms",{"type":42,"tag":112,"props":301,"children":302},{},[303,308],{"type":42,"tag":58,"props":304,"children":305},{},[306],{"type":47,"value":307},"Equivalent regulators for non-US targets:",{"type":47,"value":309}," FCA (UK), EDINET (Japan), HKEx (Hong Kong), SEDAR+ (Canada), ASX (Australia)",{"type":42,"tag":112,"props":311,"children":312},{},[313,318],{"type":42,"tag":58,"props":314,"children":315},{},[316],{"type":47,"value":317},"Official investor relations press releases",{"type":47,"value":319}," from acquirer or target",{"type":42,"tag":112,"props":321,"children":322},{},[323,328],{"type":42,"tag":58,"props":324,"children":325},{},[326],{"type":47,"value":327},"Reputable financial news:",{"type":47,"value":329}," Reuters, Bloomberg, Wall Street Journal, Financial Times",{"type":42,"tag":43,"props":331,"children":332},{},[333,335,341,343,349,350,356],{"type":47,"value":334},"Use web search to identify deals: ",{"type":42,"tag":64,"props":336,"children":338},{"className":337},[],[339],{"type":47,"value":340},"\"{industry} acquisitions {sub-sector} last 10 years\"",{"type":47,"value":342},", ",{"type":42,"tag":64,"props":344,"children":346},{"className":345},[],[347],{"type":47,"value":348},"\"{TICKER} comparable M&A transactions\"",{"type":47,"value":342},{"type":42,"tag":64,"props":351,"children":353},{"className":352},[],[354],{"type":47,"value":355},"\"{sector} deal comps precedent transactions\"",{"type":47,"value":357},".",{"type":42,"tag":43,"props":359,"children":360},{},[361,366],{"type":42,"tag":58,"props":362,"children":363},{},[364],{"type":47,"value":365},"Do NOT use:",{"type":47,"value":367}," finance blogs, Seeking Alpha, Reddit, anonymous wiki contributions, or aggregators without a traceable primary source.",{"type":42,"tag":43,"props":369,"children":370},{},[371],{"type":47,"value":372},"For each transaction, capture:",{"type":42,"tag":108,"props":374,"children":375},{},[376,381,386,391,396,401],{"type":42,"tag":112,"props":377,"children":378},{},[379],{"type":47,"value":380},"Announcement date",{"type":42,"tag":112,"props":382,"children":383},{},[384],{"type":47,"value":385},"Acquirer name",{"type":42,"tag":112,"props":387,"children":388},{},[389],{"type":47,"value":390},"Target name",{"type":42,"tag":112,"props":392,"children":393},{},[394],{"type":47,"value":395},"Transaction Enterprise Value",{"type":42,"tag":112,"props":397,"children":398},{},[399],{"type":47,"value":400},"Deal consideration (All Cash \u002F All Stock \u002F Cash + Stock)",{"type":42,"tag":112,"props":402,"children":403},{},[404],{"type":47,"value":405},"Source (press release URL, SEC filing, or regulatory filing)",{"type":42,"tag":88,"props":407,"children":409},{"id":408},"_4-source-target-financials-via-daloopa",[410],{"type":47,"value":411},"4. Source Target Financials via Daloopa",{"type":42,"tag":43,"props":413,"children":414},{},[415],{"type":47,"value":416},"For each target company in the precedent transactions table, source LTM Revenue and EBITDA from Daloopa:",{"type":42,"tag":287,"props":418,"children":419},{},[420,437,462,479,484],{"type":42,"tag":112,"props":421,"children":422},{},[423,428,430,435],{"type":42,"tag":58,"props":424,"children":425},{},[426],{"type":47,"value":427},"Look up the target",{"type":47,"value":429}," using ",{"type":42,"tag":64,"props":431,"children":433},{"className":432},[],[434],{"type":47,"value":104},{"type":47,"value":436}," with the target's ticker or name",{"type":42,"tag":112,"props":438,"children":439},{},[440,445,446,452,454,460],{"type":42,"tag":58,"props":441,"children":442},{},[443],{"type":47,"value":444},"Find relevant series",{"type":47,"value":429},{"type":42,"tag":64,"props":447,"children":449},{"className":448},[],[450],{"type":47,"value":451},"discover_company_series",{"type":47,"value":453}," with keywords ",{"type":42,"tag":64,"props":455,"children":457},{"className":456},[],[458],{"type":47,"value":459},"[\"revenue\", \"EBITDA\"]",{"type":47,"value":461}," and the appropriate period (the last complete fiscal year before the deal announcement)",{"type":42,"tag":112,"props":463,"children":464},{},[465,470,471,477],{"type":42,"tag":58,"props":466,"children":467},{},[468],{"type":47,"value":469},"Pull the data",{"type":47,"value":429},{"type":42,"tag":64,"props":472,"children":474},{"className":473},[],[475],{"type":47,"value":476},"get_company_fundamentals",{"type":47,"value":478}," with the discovered series IDs",{"type":42,"tag":112,"props":480,"children":481},{},[482],{"type":47,"value":483},"For EBITDA, look for series containing \"Adjusted EBITDA\", \"EBITDA\", or fall back to \"Operating Income\" + D&A",{"type":42,"tag":112,"props":485,"children":486},{},[487],{"type":47,"value":488},"If a target is not in Daloopa (e.g., pre-IPO targets, private companies), fall back to SEC filings, press releases, or regulatory filings",{"type":42,"tag":43,"props":490,"children":491},{},[492,497],{"type":42,"tag":58,"props":493,"children":494},{},[495],{"type":47,"value":496},"Daloopa is the primary source.",{"type":47,"value":498}," Only fall back to other sources when a target is genuinely unavailable in the database.",{"type":42,"tag":88,"props":500,"children":502},{"id":501},"_5-compute-deal-multiples",[503],{"type":47,"value":504},"5. 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How does it compare to the current market price?}\n\n\u003Ch2>Subject Company Overview\u003C\u002Fh2>\n{Exchange, currency, industry, LTM Revenue and EBITDA with Daloopa citations}\n{Note: \"Revenue and EBITDA sourced from Daloopa where available\"}\n\n\u003Ch2>Selected Precedent Transactions\u003C\u002Fh2>\n\u003Ctable>\n| Date | Acquirer | Target | EV ($M) | LTM Rev ($M) | LTM EBITDA ($M) | EV\u002FRev | EV\u002FEBITDA | Consideration |\n{data rows with Daloopa-cited financials, footnote superscripts, clickable acquirers}\n| 75th Percentile | | | | | | XX.Xx | XX.Xx | |\n| **Average** | | | | | | **XX.Xx** | **XX.Xx** | |\n| **Median** | | | | | | **XX.Xx** | **XX.Xx** | |\n| 25th Percentile | | | | | | XX.Xx | XX.Xx | |\n\u003C\u002Ftable>\n\n\u003Ch2>Implied Valuation\u003C\u002Fh2>\n\u003Ctable>\n| Methodology | Multiple | Subject Metric | Implied EV | Implied Equity | Implied Price | vs Current |\n{valuation bridge using median and range multiples}\n\u003C\u002Ftable>\n\n\u003Ch2>{Company Name} Acquisition History\u003C\u002Fh2>\n\u003Ctable>\n| Date | Target | Deal Value | Consideration | Strategic Rationale |\n{company's own M&A deals}\n\u003C\u002Ftable>\n\n\u003Ch2>Deal Environment\u003C\u002Fh2>\n\u003Cul>{3-5 bullets on sector M&A trends, control premiums, takeout potential}\u003C\u002Ful>\n\n\u003Ch2>Sources\u003C\u002Fh2>\n{Numbered footnote list — each deal with press release link, SEC filing, Daloopa data links}\n{Data sourced from Daloopa attribution}\n",[999],{"type":42,"tag":64,"props":1000,"children":1002},{"__ignoreMap":1001},"",[1003],{"type":47,"value":997},{"type":42,"tag":43,"props":1005,"children":1006},{},[1007,1009],{"type":47,"value":1008},"All financial figures from Daloopa must use citation format: ",{"type":42,"tag":64,"props":1010,"children":1012},{"className":1011},[],[1013],{"type":47,"value":1014},"\u003Ca href=\"https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id}\">$X.XX million\u003C\u002Fa>",{"type":42,"tag":43,"props":1016,"children":1017},{},[1018],{"type":47,"value":1019},"Tell the user where the HTML report was saved.",{"type":42,"tag":43,"props":1021,"children":1022},{},[1023],{"type":47,"value":1024},"Highlight: what precedent transactions imply about the company's takeout value, how it compares to the current market price, and whether the sector M&A environment supports deal activity.",{"items":1026,"total":1231},[1027,1048,1071,1088,1104,1123,1142,1158,1174,1188,1200,1215],{"slug":1028,"name":1028,"fn":1029,"description":1030,"org":1031,"tags":1032,"stars":1045,"repoUrl":1046,"updatedAt":1047},"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},[1033,1036,1039,1042],{"name":1034,"slug":1035,"type":15},"Documents","documents",{"name":1037,"slug":1038,"type":15},"Healthcare","healthcare",{"name":1040,"slug":1041,"type":15},"Insurance","insurance",{"name":1043,"slug":1044,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":1049,"name":1049,"fn":1050,"description":1051,"org":1052,"tags":1053,"stars":1068,"repoUrl":1069,"updatedAt":1070},"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},[1054,1057,1059,1062,1065],{"name":1055,"slug":1056,"type":15},".NET","dotnet",{"name":1058,"slug":1049,"type":15},"ASP.NET Core",{"name":1060,"slug":1061,"type":15},"Blazor","blazor",{"name":1063,"slug":1064,"type":15},"C#","csharp",{"name":1066,"slug":1067,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":1072,"name":1072,"fn":1073,"description":1074,"org":1075,"tags":1076,"stars":1068,"repoUrl":1069,"updatedAt":1087},"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},[1077,1080,1083,1086],{"name":1078,"slug":1079,"type":15},"Apps SDK","apps-sdk",{"name":1081,"slug":1082,"type":15},"ChatGPT","chatgpt",{"name":1084,"slug":1085,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":1089,"name":1089,"fn":1090,"description":1091,"org":1092,"tags":1093,"stars":1068,"repoUrl":1069,"updatedAt":1103},"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},[1094,1097,1100],{"name":1095,"slug":1096,"type":15},"API Development","api-development",{"name":1098,"slug":1099,"type":15},"CLI","cli",{"name":1101,"slug":1102,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":1105,"name":1105,"fn":1106,"description":1107,"org":1108,"tags":1109,"stars":1068,"repoUrl":1069,"updatedAt":1122},"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},[1110,1113,1116,1119],{"name":1111,"slug":1112,"type":15},"Cloudflare","cloudflare",{"name":1114,"slug":1115,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":1117,"slug":1118,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":1120,"slug":1121,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":1124,"name":1124,"fn":1125,"description":1126,"org":1127,"tags":1128,"stars":1068,"repoUrl":1069,"updatedAt":1141},"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},[1129,1132,1135,1138],{"name":1130,"slug":1131,"type":15},"Productivity","productivity",{"name":1133,"slug":1134,"type":15},"Project Management","project-management",{"name":1136,"slug":1137,"type":15},"Strategy","strategy",{"name":1139,"slug":1140,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":1143,"name":1143,"fn":1144,"description":1145,"org":1146,"tags":1147,"stars":1068,"repoUrl":1069,"updatedAt":1157},"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},[1148,1151,1153,1156],{"name":1149,"slug":1150,"type":15},"Design","design",{"name":1152,"slug":1143,"type":15},"Figma",{"name":1154,"slug":1155,"type":15},"Frontend","frontend",{"name":1084,"slug":1085,"type":15},"2026-04-12T05:06:47.939943",{"slug":1159,"name":1159,"fn":1160,"description":1161,"org":1162,"tags":1163,"stars":1068,"repoUrl":1069,"updatedAt":1173},"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},[1164,1165,1168,1169,1170],{"name":1149,"slug":1150,"type":15},{"name":1166,"slug":1167,"type":15},"Design System","design-system",{"name":1152,"slug":1143,"type":15},{"name":1154,"slug":1155,"type":15},{"name":1171,"slug":1172,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":1175,"name":1175,"fn":1176,"description":1177,"org":1178,"tags":1179,"stars":1068,"repoUrl":1069,"updatedAt":1187},"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},[1180,1181,1182,1185,1186],{"name":1149,"slug":1150,"type":15},{"name":1166,"slug":1167,"type":15},{"name":1183,"slug":1184,"type":15},"Documentation","documentation",{"name":1152,"slug":1143,"type":15},{"name":1154,"slug":1155,"type":15},"2026-05-16T06:07:47.821474",{"slug":1189,"name":1189,"fn":1190,"description":1191,"org":1192,"tags":1193,"stars":1068,"repoUrl":1069,"updatedAt":1199},"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},[1194,1195,1196,1197,1198],{"name":1149,"slug":1150,"type":15},{"name":1152,"slug":1143,"type":15},{"name":1154,"slug":1155,"type":15},{"name":1171,"slug":1172,"type":15},{"name":1066,"slug":1067,"type":15},"2026-05-16T06:07:40.583615",{"slug":1201,"name":1201,"fn":1202,"description":1203,"org":1204,"tags":1205,"stars":1068,"repoUrl":1069,"updatedAt":1214},"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},[1206,1209,1210,1213],{"name":1207,"slug":1208,"type":15},"Animation","animation",{"name":1101,"slug":1102,"type":15},{"name":1211,"slug":1212,"type":15},"Creative","creative",{"name":1149,"slug":1150,"type":15},"2026-05-02T05:31:48.48485",{"slug":1216,"name":1216,"fn":1217,"description":1218,"org":1219,"tags":1220,"stars":1068,"repoUrl":1069,"updatedAt":1230},"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},[1221,1222,1223,1226,1229],{"name":1211,"slug":1212,"type":15},{"name":1149,"slug":1150,"type":15},{"name":1224,"slug":1225,"type":15},"Image Generation","image-generation",{"name":1227,"slug":1228,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":1233,"total":1346},[1234,1250,1266,1278,1296,1314,1334],{"slug":1235,"name":1235,"fn":1236,"description":1237,"org":1238,"tags":1239,"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},[1240,1243,1246,1249],{"name":1241,"slug":1242,"type":15},"Accessibility","accessibility",{"name":1244,"slug":1245,"type":15},"Charts","charts",{"name":1247,"slug":1248,"type":15},"Data Visualization","data-visualization",{"name":1149,"slug":1150,"type":15},{"slug":1251,"name":1251,"fn":1252,"description":1253,"org":1254,"tags":1255,"stars":25,"repoUrl":26,"updatedAt":1265},"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},[1256,1259,1262],{"name":1257,"slug":1258,"type":15},"Agents","agents",{"name":1260,"slug":1261,"type":15},"Browser Automation","browser-automation",{"name":1263,"slug":1264,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":1267,"name":1267,"fn":1268,"description":1269,"org":1270,"tags":1271,"stars":25,"repoUrl":26,"updatedAt":1277},"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},[1272,1273,1276],{"name":1260,"slug":1261,"type":15},{"name":1274,"slug":1275,"type":15},"Local Development","local-development",{"name":1263,"slug":1264,"type":15},"2026-04-06T18:41:17.526867",{"slug":1279,"name":1279,"fn":1280,"description":1281,"org":1282,"tags":1283,"stars":25,"repoUrl":26,"updatedAt":1295},"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},[1284,1285,1286,1289,1292],{"name":1257,"slug":1258,"type":15},{"name":1117,"slug":1118,"type":15},{"name":1287,"slug":1288,"type":15},"SDK","sdk",{"name":1290,"slug":1291,"type":15},"Serverless","serverless",{"name":1293,"slug":1294,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":1297,"name":1297,"fn":1298,"description":1299,"org":1300,"tags":1301,"stars":25,"repoUrl":26,"updatedAt":1313},"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},[1302,1303,1306,1309,1310],{"name":1154,"slug":1155,"type":15},{"name":1304,"slug":1305,"type":15},"React","react",{"name":1307,"slug":1308,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":1171,"slug":1172,"type":15},{"name":1311,"slug":1312,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":1315,"name":1315,"fn":1316,"description":1317,"org":1318,"tags":1319,"stars":25,"repoUrl":26,"updatedAt":1333},"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},[1320,1323,1326,1329,1332],{"name":1321,"slug":1322,"type":15},"AI Infrastructure","ai-infrastructure",{"name":1324,"slug":1325,"type":15},"Cost Optimization","cost-optimization",{"name":1327,"slug":1328,"type":15},"LLM","llm",{"name":1330,"slug":1331,"type":15},"Performance","performance",{"name":1311,"slug":1312,"type":15},"2026-04-06T18:40:44.377464",{"slug":1335,"name":1335,"fn":1336,"description":1337,"org":1338,"tags":1339,"stars":25,"repoUrl":26,"updatedAt":1345},"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},[1340,1341,1344],{"name":1324,"slug":1325,"type":15},{"name":1342,"slug":1343,"type":15},"Database","database",{"name":1327,"slug":1328,"type":15},"2026-04-06T18:41:08.513425",600]