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Get Started Free →Build M&A accretion/dilution workbooks in Excel.
.claude/skills/nousresearch-merger-model/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 40% | 0% |
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.
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.
Acquirer:
Target:
Deal Terms:
| Item | Value | |------|-------| | Offer price per share | | | Premium to current | | | Equity value | | | Plus: net debt assumed | | | Enterprise value | | | EV / EBITDA implied | | | P/E implied | |
| Sources | $ | Uses | $ | |---------|---|------|---| | New debt | | Equity purchase price | | | Cash on hand | | Refinance target debt | | | New equity issued | | Transaction fees | | | | | Financing fees | | | Total | | Total | |
Calculate year-by-year (Year 1-3):
| | Standalone | Pro Forma | Accretion/(Dilution) | |---|-----------|-----------|---------------------| | Acquirer net income | | | | | Target net income | | | | | Synergies (after tax) | | | | | Foregone interest on cash (after tax) | | | | | New debt interest (after tax) | | | | | Intangible amortization (after tax) | | | | | Pro forma net income | | | | | Pro forma shares | | | | | Pro forma EPS | | | | | Accretion / (Dilution) % | | | |
Accretion/Dilution vs. Synergies and Offer Premium:
| | $0M syn | $25M syn | $50M syn | $75M syn | $100M syn | |---|---------|----------|----------|----------|-----------| | 15% premium | | | | | | | 20% premium | | | | | | | 25% premium | | | | | | | 30% premium | | | | | |
Accretion/Dilution vs. Cash/Stock Mix:
| | 100% cash | 75/25 | 50/50 | 25/75 | 100% stock | |---|-----------|-------|-------|-------|------------| | Year 1 | | | | | | | Year 2 | | | | | |
Calculate the minimum synergies needed for the deal to be EPS-neutral in Year 1.
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 Hermes:
native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filingsbrowser_navigate for interactive data portals[UNSOURCED] and surface it to the user.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
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | pass→pass | 19,353 | 33,571 | +73% | 1 | 1 | 0% | 3,392 | 7,534 | +122% | 0 | 0 | — |
case-01 | fail→fail | 28,074 | 27,926 | -1% | 1 | 1 | 0% | 6,243 | 7,605 | +22% | 0 | 0 | — |
case-02 | fail→fail | 28,958 | 53,999 | +86% | 1 | 1 | 0% | 6,244 | 13,504 | +116% | 0 | 0 | — |
case-03 | fail→fail | 32,215 | 29,686 | -8% | 1 | 1 | 0% | 6,233 | 7,596 | +22% | 0 | 0 | — |
case-04 | pass→pass | 10,468 | 12,070 | +15% | 1 | 1 | 0% | 1,887 | 3,512 | +86% | 0 | 0 | — |
case-05 | pass→pass | 6,621 | 5,842 | -12% | 1 | 1 | 0% | 1,237 | 2,397 | +94% | 0 | 0 | — |
case-06 | pass→pass | 3,049 | 7,103 | +133% | 1 | 1 | 0% | 542 | 2,625 | +384% | 0 | 0 | — |
case-07 | pass→pass | 11,466 | 25,286 | +121% | 1 | 1 | 0% | 2,144 | 4,238 | +98% | 0 | 0 | — |
case-08 | pass→pass | 5,408 | 8,311 | +54% | 1 | 1 | 0% | 1,201 | 3,105 | +159% | 0 | 0 | — |
case-09 | fail→pass | 16,479 | 10,370 | -37% | 1 | 1 | 0% | 2,581 | 2,987 | +16% | 0 | 0 | — |
case-10 | pass→pass | 5,636 | 5,194 | -8% | 1 | 1 | 0% | 962 | 2,283 | +137% | 0 | 0 | — |
case-11 | pass→pass | 5,135 | 5,766 | +12% | 1 | 1 | 0% | 1,027 | 2,466 | +140% | 0 | 0 | — |
case-12 | fail→fail | 7,205 | 6,376 | -12% | 1 | 1 | 0% | 1,138 | 2,420 | +113% | 0 | 0 | — |
case-13 | fail→pass | 18,642 | 19,708 | +6% | 1 | 1 | 0% | 3,369 | 5,160 | +53% | 0 | 0 | — |
case-14 | pass→pass | 4,149 | 4,299 | +4% | 1 | 1 | 0% | 798 | 2,143 | +169% | 0 | 0 | — |
case-15 | fail→pass | 9,102 | 1,800 | -80% | 1 | 1 | 0% | 1,563 | 1,626 | +4% | 0 | 0 | — |
case-16 | pass→pass | 3,529 | 18,876 | +435% | 1 | 1 | 0% | 704 | 5,631 | +700% | 0 | 0 | — |
case-17 | pass→pass | 10,871 | 9,851 | -9% | 1 | 1 | 0% | 1,666 | 3,086 | +85% | 0 | 0 | — |
case-18 | fail→pass | 13,026 | 5,721 | -56% | 1 | 1 | 0% | 2,215 | 2,396 | +8% | 0 | 0 | — |
case-19 | fail→pass | 11,757 | 9,192 | -22% | 1 | 1 | 0% | 2,086 | 2,915 | +40% | 0 | 0 | — |
case-20 | pass→fail | 21,737 | 27,152 | +25% | 1 | 1 | 0% | 5,161 | 7,550 | +46% | 0 | 0 | — |
case-21 | pass→fail | 24,659 | 29,625 | +20% | 1 | 1 | 0% | 5,655 | 7,544 | +33% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.