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 OpenLAIR/OpenSkill --skill evo-excel-financial-formulasgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-excel-financial-formulas)<a href="https://agentmods.dev/skills/openlair/openskill/evo-excel-financial-formulas"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-excel-financial-formulas/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/openlair/openskill/evo-excel-financial-formulas"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-excel-financial-formulas.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 | $0.00053 | $0.00549 |
| Opus 5 | $0.00026 | $0.00275 |
| Sonnet 5 | $0.00011 | $0.00110 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
evo-excel-financial-formulas 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 today.
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.
What it actually says
evo-excel-financial-formulas
Write financial formulas into Excel workbooks and recalculate with LibreOffice.
Key Functions
write_gold_price_sheet(wb, gold_series)- Populate gold prices into Gold price sheet col Bwrite_log_return_formulas(wb, start_row, end_row)- Write =LN(Bn/Bn-1)*100 in col Cwrite_volatility_formulas(wb, start_row, end_row)- Write STDEV.S formulas in cols D (3-month) and E (12-month)write_answer_sheet_step1(wb)- Fill z-score, volatilities in Answer sheetwrite_answer_sheet_step2(wb, value_countries, volume_countries, gold_avg_price)- Fill country reserves and exposurewrite_answer_sheet_step3(wb, step2_countries, total_reserves_data)- Fill RaR calculationsrecalculate_with_libreoffice(filepath)- Force formula recalculationcreate_output_workbook(template_path, output_path, ...)- Main orchestrator function
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-excel-financial-formulas/scripts')
from utils import create_output_workbook
create_output_workbook(
template_path='/root/data/test-rar.xlsx',
output_path='/root/output/rar_result.xlsx',
gold_series=gold_series,
value_countries=value_countries,
volume_countries=volume_countries,
total_reserves_data=total_reserves_data,
gold_avg_price=avg_price
)
Formula Details
- Log returns:
=LN(B{r}/B{r-1})*100(multiply by 100 for percentage) - 3-month vol:
=STDEV.S(C{r-2}:C{r})starting at row 5 - 12-month vol:
=STDEV.S(C{r-11}:C{r})starting at row 14 - Annualized 3-month vol:
=C4*SQRT(12)where C4 is latest 3-month vol - Gold exposure:
=reserves * annualized_vol/100 * z_score - RaR:
=exposure / total_reserves * 100
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.
- today First seen · 47 lines · 53 tokens per session scan A 8a67d70eebd9
evo-excel-financial-formulas is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 53 tokens to every session and 549 once invoked, about $0.0003 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 2026-09-11.
Other skills, from other repositories
dcf-model
Build discounted cash flow valuation workbooks in Excel.
comps-analysis
Build comparable-company valuation workbooks in Excel.
lbo-model
Build leveraged buyout workbooks with IRR/MOIC in Excel.
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.
3-statement-model
Build integrated IS/BS/CF financial workbooks in Excel.
merger-model
Build M&A accretion/dilution workbooks in Excel.