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 serejaris/kimi-skills --skill fund-risk-comparegit clone --depth 1 https://github.com/serejaris/kimi-skillsWrote 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/serejaris/kimi-skills/fund-risk-compare)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/fund-risk-compare"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/fund-risk-compare/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/serejaris/kimi-skills/fund-risk-compare"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/fund-risk-compare.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 | $0.00071 | $0.00876 |
| Opus 5 | $0.00036 | $0.00438 |
| Sonnet 5 | $0.00014 | $0.00175 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
fund-risk-compare 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fund Risk Compare — Multi-Dimensional ETF Comparison Tool
Performs multi-dimensional risk-return analysis on multiple ETFs based on user-provided NAV (Net Asset Value) data. Automatically calculates annualized returns, max drawdown, Sharpe ratio, and generates a correlation matrix.
Quick Start
Basic Comparison
python scripts/etf_screener.py --input nav_data.csv
Custom Risk-Free Rate + CSV Export
python scripts/etf_screener.py --input nav_data.csv --risk-free 0.03 --output report.csv
JSON Output (for programmatic processing)
python scripts/etf_screener.py --input nav_data.csv --json
Input Data Format
CSV file with dates in the first column and NAV values for each ETF in subsequent columns:
date,SP500_ETF,NASDAQ_ETF,BOND_ETF
2023-01-03,1.0000,1.0000,1.0000
2023-01-04,1.0050,0.9980,1.0020
2023-01-05,1.0120,1.0010,1.0080
...
- The date column name and format are flexible (used only to label the time range)
- ETF column names are used as labels in the comparison report
- Missing values can be left blank or marked as
NaN— they are automatically skipped
Calculation Details
Annualized Return
Computed from the first and last NAV values, then annualized by the number of trading days:
Ann. Return = (NAV_end / NAV_start) ^ (trading_days / n_days) - 1
Max Drawdown
The largest peak-to-trough decline in the NAV series:
MDD = max( (peak - trough) / peak )
Sharpe Ratio
A risk-adjusted return metric:
Sharpe = (Annualized Return - Risk-Free Rate) / Annualized Volatility
Annualized volatility is derived from the standard deviation of daily returns multiplied by √(trading_days).
Correlation Matrix
Pearson correlation coefficients computed from daily returns, measuring the co-movement between ETFs. A coefficient near 1 indicates strong positive correlation, near 0 indicates no correlation, and near -1 indicates negative correlation.
Parameters
| Parameter | Required | Default | Description |
|---|---|---|---|
--input / -i |
Yes | - | Path to the NAV CSV file |
--risk-free / -rf |
No | 0.02 | Annual risk-free rate (e.g., 0.03 for 3%) |
--trading-days |
No | 252 | Trading days per year (typically 252 for US/China markets) |
--output / -o |
No | - | Output file path (.csv or .json) |
--json |
No | false | Output results as JSON to stdout |
What ships with it
2 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.
- 9d ago First seen · 96 lines · 71 tokens per session scan A ab5baa5fbf40
fund-risk-compare is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 876 once invoked, about $0.0004 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 2026-09-03.
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