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/MilkyWay008/Hermes-OTGnpx agentmods add skills/milkyway008/hermes-otg/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/milkyway008/hermes-otg/excel-author)<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/excel-author"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/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/milkyway008/hermes-otg/excel-author"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/excel-author.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 | $0.00016 | $0.02367 |
| Opus 5 | $0.00008 | $0.01184 |
| Sonnet 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 7d 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
100% identical to excel-author — 0 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 — 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.
- 7d ago First seen · 245 lines · 16 tokens per session scan A f0228fe29411
excel-author is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (15 stars, last pushed 27d ago), licensed MIT. It adds 16 tokens to every session and 2,367 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to excel-author, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
document-exports
Generate and TEST downloadable document exports (PDF/CSV) from a Node/Express backend — pdfkit streaming, CSV BOM, cents formatting, and the supertest/pdfkit pitfalls that silently break tests.
comps-analysis
Build comparable company analysis in Excel — operating metrics, valuation multiples, statistical benchmarking vs peer sets. Pairs with excel-author. Use for public-company valuation, IPO pricing, sector benchmarking, or outlier detection.
lbo-model
Build leveraged buyout models in Excel — sources & uses, debt schedule, cash sweep, exit multiple, IRR/MOIC sensitivity. Pairs with excel-author. Use for PE screening, sponsor-case valuation, or illustrative LBO in a pitch.
excel-author
Build auditable Excel workbooks headless with openpyxl — blue/black/green cell conventions, formulas over hardcodes, named ranges, balance checks, sensitivity tables. Use for financial models, audit outputs, reconciliations.
dcf-model
Build institutional-quality DCF valuation models in Excel — revenue projections, FCF build, WACC, terminal value, Bear/Base/Bull scenarios, 5x5 sensitivity tables. Pairs with excel-author. Use for intrinsic-value equity analysis.
3-statement-model
Build fully-integrated 3-statement models (IS, BS, CF) in Excel with working capital schedules, D&A roll-forwards, debt schedule, and the plugs that make cash and retained earnings tie. Pairs with excel-author.