Build M&A accretion/dilution workbooks in Excel.

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Skill metadata

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Reference: full SKILL.md

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Environment

This skill assumes headless openpyxl — you are producing an .xlsx file on disk. Follow the excel-author skill’s conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.

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Merger Model

Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms.

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Workflow

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Step 1: Gather Inputs

Acquirer:

• Company name, current share price, shares outstanding
• LTM and NTM EPS (GAAP and adjusted)
• P/E multiple
• Pre-tax cost of debt, tax rate
• Cash on balance sheet, existing debt

Target:

• Company name, current share price, shares outstanding (if public)
• LTM and NTM EPS or net income
• Enterprise value or equity value

Deal Terms:

• Offer price per share (or premium to current)
• Consideration mix: % cash vs. % stock
• New debt raised to fund cash portion
• Expected synergies (revenue and cost) and phase-in timeline
• Transaction fees and financing costs
• Expected close date

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Step 2: Purchase Price Analysis

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Step 3: Sources & Uses

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Step 4: Pro Forma EPS (Accretion / Dilution)

Calculate year-by-year (Year 1-3):

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Step 5: Sensitivity Analysis

Accretion/Dilution vs. Synergies and Offer Premium: Accretion/Dilution vs. Cash/Stock Mix:

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Step 6: Breakeven Synergies

Calculate the minimum synergies needed for the deal to be EPS-neutral in Year 1.

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Step 7: Output

• Excel workbook with:
  • Assumptions tab
  • Sources & uses
  • Pro forma income statement
  • Accretion/dilution summary
  • Sensitivity tables
  • Breakeven analysis
• One-page merger consequences summary for pitch book

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Important Notes

• Always show both GAAP and adjusted (cash) EPS where relevant
• Stock deals: use acquirer’s current price for exchange ratio, note dilution from new shares
• Include purchase price allocation — goodwill and intangible amortization matter for GAAP EPS
• Synergy phase-in is critical — Year 1 is often only 25-50% of run-rate synergies
• Don’t forget foregone interest income on cash used and new interest expense on debt raised
• Tax rate on synergies and interest adjustments should match the acquirer’s marginal rate

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Data sources — MCP first, web fallback

Many passages below say “use the S&P Kensho MCP / Daloopa MCP / FactSet MCP”. Those are commercial financial-data MCPs from the original Cowork plugin context. In Mibyan:

• If you have any structured financial-data MCP configured (Mibyan supports MCP — see native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.
• Otherwise, fall back to:
  • web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filings
  • Company IR pages for press releases, earnings decks
  • browser_navigate for interactive data portals
  • User-provided data (explicitly ask when the context doesn’t have it)
• Never fabricate. If a multiple, precedent, or filing number can’t be sourced, flag the cell as [UNSOURCED] and surface it to the user.

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Attribution

This skill is adapted from Anthropic’s Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the excel-author skill’s conventions. Original: https://github.com/anthropics/financial-services