Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add GGbond-bo/MemOmics-Agent --skill merger-modelgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ggbond-bo/memomics-agent/merger-model)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/merger-model"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/merger-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/merger-model"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/merger-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.01421 |
| Opus 5 | $0.00027 | $0.00711 |
| Sonnet 5 | $0.00011 | $0.00284 |
| Haiku 4.5 | $0.00005 | $0.00142 |
Grade A, and why
merger-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to merger-model — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
Workflow
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
Step 2: Purchase Price Analysis
| Item | Value |
|---|---|
| Offer price per share | |
| Premium to current | |
| Equity value | |
| Plus: net debt assumed | |
| Enterprise value | |
| EV / EBITDA implied | |
| P/E implied |
Step 3: Sources & Uses
| Sources | $ | Uses | $ |
|---|---|---|---|
| New debt | Equity purchase price | ||
| Cash on hand | Refinance target debt | ||
| New equity issued | Transaction fees | ||
| Financing fees | |||
| Total | Total |
Step 4: Pro Forma EPS (Accretion / Dilution)
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) % |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 145 lines · 54 tokens per session scan A 64f323ee1fca
merger-model is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 7d ago), licensed MIT. It adds 54 tokens to every session and 1,421 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to merger-model, differing in 2 lines, and is treated as a copy.
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