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 agentmods add skills/nousresearch/hermes-agent/3-statement-modelnpx skills add NousResearch/hermes-agent --skill 3-statement-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/3-statement-model)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/3-statement-model"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/3-statement-model.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.04801 |
| Opus 5 | $0.00009 | $0.02400 |
| Sonnet 5 | $0.00004 | $0.00960 |
| Haiku 4.5 | $0.00002 | $0.00480 |
Grade A, and why
3-statement-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 5d 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
8 near-identical copies found in the catalogue:
- 3-statement-model — 100% identical, 0 lines differ
- 3-statement-model — 100% identical, 0 lines differ
- 3-statement-model — 100% identical, 0 lines differ
- 3-statement-model — 95% identical, 2 lines differ
- 3-statement-model — 95% identical, 2 lines differ
- 3-statement-model — 95% identical, 2 lines differ
- 3-statement-model — 86% identical, 39 lines differ
- 3-statement-model — 86% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 434 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.
3-Statement Financial Model Template Completion
Complete and populate integrated financial model templates with proper linkages between Income Statement, Balance Sheet, and Cash Flow Statement.
⚠️ CRITICAL PRINCIPLES — Read Before Populating Any Template
Formulas over hardcodes (non-negotiable):
- Every projection cell, roll-forward, linkage, and subtotal MUST be an Excel formula — never a pre-computed value
- When using Python/openpyxl: write formula strings (
ws["D15"] = "=D14*(1+Assumptions!$B$5)"), NOT computed results (ws["D15"] = 12500) - The ONLY cells that should contain hardcoded numbers are: (1) historical actuals, (2) assumption drivers in the Assumptions tab
- If you find yourself computing a value in Python and writing the result to a cell — STOP. Write the formula instead.
- Why: the model must flex when scenarios toggle or assumptions change. Hardcodes break every downstream integrity check silently.
Verify step-by-step with the user:
- After mapping the template → show the user which tabs/sections you've identified and confirm before touching any cells
- After populating historicals → show the user the historical block and confirm values/periods match source data
- After building IS projections → run the subtotal checks, show the user the projected IS, confirm before moving to BS
- After building BS → show the user the balance check (Assets = L+E) for every period, confirm before moving to CF
- After building CF → show the user the cash tie-out (CF ending cash = BS cash), confirm before finalizing
- Do NOT populate the entire model end-to-end and present it complete — break at each statement, show the work, catch errors early
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 434 lines · 18 tokens per session scan A 2022e0ec2ef0
3-statement-model is a skill published in the GitHub repository NousResearch/hermes-agent (240,739 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 4,801 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
check-model
Financial model audit: structural checks, formula validation, integrity testing.
meter-invoice
Generate CocoMeter chargeback invoices. Usage: $meter invoice.
receipts-to-expenses
Read a batch of receipt images directly via vision, classify each into expense categories, optionally reconcile against a bank statement CSV, and produce a multi-sheet Excel workbook + a PDF summary. Use when given receipt photos and asked for an expense report.
pn-financial-model-audit
Audit a financial model (spreadsheet, structured table, or agent-produced model) for hardcoded values, broken formula logic, balance-sheet balance, circular references, and missing cross-checks. Use after any DCF, LBO, comps, or 3-statement model is built before it is used in a deliverable.
investor-materials
Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.
lbo-model
This skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.