Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill merger-modelgit clone --depth 1 https://github.com/NousResearch/hermes-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/nousresearch/hermes-agent/merger-model)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/merger-model"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-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/nousresearch/hermes-agent/merger-model"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/merger-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00018 | $0.01385 |
| Opus 5.5 | $0.00007 | $0.00554 |
| Sonnet 5.5 | $0.00004 | $0.00277 |
| Haiku 4.5 | $0.00002 | $0.00138 |
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 1mo 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.
Copies of this mod
6 near-identical copies found in the catalogue:
- merger-model — 100% identical, 0 lines differ
- merger-model — 100% identical, 0 lines differ
- merger-model — 100% identical, 0 lines differ
- merger-model — 97% identical, 0 lines differ
- merger-model — 97% identical, 2 lines differ
- merger-model — 97% identical, 2 lines differ
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.
- 1mo ago First seen · 145 lines · 18 tokens per session scan A 1e2893e53bd5
merger-model is a skill published in the GitHub repository NousResearch/hermes-agent (256,246 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,385 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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