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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NousResearch/hermes-agentnpx agentmods add skills/nousresearch/hermes-agent/excel-authorWrote 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/excel-author)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/excel-author"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/excel-author/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/excel-author"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/excel-author.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00016 | $0.02367 |
| Opus 5.5 | $0.00006 | $0.00947 |
| Sonnet 5.5 | $0.00003 | $0.00473 |
| Haiku 4.5 | $0.00002 | $0.00237 |
Grade A, and why
excel-author 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:
- excel-author — 100% identical, 0 lines differ
- excel-author — 100% identical, 0 lines differ
- excel-author — 100% identical, 0 lines differ
- excel-author — 95% identical, 4 lines differ
- excel-author — 95% identical, 4 lines differ
- excel-author — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
excel-author
Produce an .xlsx file on disk using openpyxl. Follow the banker-grade conventions below so the model is auditable, flexible, and reviewable by someone other than the person who built it.
Adapted from Anthropic's xlsx-author and audit-xls skills in the anthropics/financial-services repo. The MCP / Office-JS / Cowork-specific branches of the originals are dropped — this skill assumes headless Python.
Output contract
- Write to
./out/<name>.xlsx. Create./out/if it does not exist. - Return the relative path in your final message so downstream tools can pick it up.
- One logical model per file. Do not append to an existing workbook unless explicitly asked.
Setup
pip install "openpyxl>=3.0"
Core conventions (non-negotiable)
Blue / black / green cell color
- Blue (
Font(color="0000FF")) — hardcoded input a human entered. Revenue drivers, WACC inputs, terminal growth, market data. - Black (default) — formula. Every derived cell is a live Excel formula.
- Green (
Font(color="006100")) — link to another sheet or external file.
A reviewer can then scan the sheet and immediately see what's an assumption vs. what's computed.
Formulas over hardcodes
Every calculation cell MUST be a formula string, never a number computed in Python and pasted as a value.
# WRONG — silent bug waiting to happen
ws["D20"] = revenue_prior_year * (1 + growth)
# CORRECT — flexes when the user changes the assumption
ws["D20"] = "=D19*(1+$B$8)"
The only hardcoded numbers permitted:
- Raw historical inputs (actual revenues, reported EBITDA, etc.)
- Assumption drivers the user is meant to flex (growth rates, WACC inputs, terminal g)
- Current market data (share price, debt balance) — with a cell comment documenting source + date
If you catch yourself computing a value in Python and writing the result, stop.
Named ranges for cross-sheet references
Use named ranges for any figure referenced from another sheet, a deck, or a memo.
What ships with it
1 file 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.
- 1mo ago First seen · 245 lines · 16 tokens per session scan A f0228fe29411
excel-author is a skill published in the GitHub repository NousResearch/hermes-agent (255,903 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 2,367 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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