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-analyzergit 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-analyzer)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/fund-risk-analyzer"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/fund-risk-analyzer/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-analyzer"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/fund-risk-analyzer.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 | $0.00113 | $0.00995 |
| Opus 5 | $0.00056 | $0.00498 |
| Sonnet 5 | $0.00023 | $0.00199 |
| Haiku 4.5 | $0.00011 | $0.00100 |
Grade A, and why
fund-risk-analyzer 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.
What it actually says
ETF Screener / ETF 多维对比工具
基于用户提供的净值（NAV）数据，对多只 ETF 进行多维度风险收益对比分析，自动计算年化收益率、最大回撤、夏普比率，并生成相关性矩阵。
Quick Start
基础对比
python scripts/etf_screener.py --input nav_data.csv
自定义无风险利率 + CSV 导出
python scripts/etf_screener.py --input nav_data.csv --risk-free 0.03 --output report.csv
JSON 输出（便于程序化处理）
python scripts/etf_screener.py --input nav_data.csv --json
输入数据格式
CSV 文件，第一列为日期，后续列为各 ETF 的净值：
date,沪深300ETF,中证500ETF,纳指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
...
- 日期列名不限，格式不限（仅用于标注区间）
- ETF 列名即为对比报告中的名称
- 缺失值用空白或
NaN表示，会自动跳过
计算说明
年化收益率 (Annualized Return)
基于首尾净值计算总收益，再按交易日数年化：
Ann. Return = (NAV_end / NAV_start) ^ (trading_days / n_days) - 1
最大回撤 (Max Drawdown)
净值序列中从峰值到谷底的最大跌幅：
MDD = max( (peak - trough) / peak )
夏普比率 (Sharpe Ratio)
风险调整后收益指标：
Sharpe = (Annualized Return - Risk-Free Rate) / Annualized Volatility
年化波动率由日收益率标准差乘以 √(trading_days) 得出。
相关性矩阵 (Correlation Matrix)
基于日收益率计算 Pearson 相关系数，衡量 ETF 间的联动程度。相关系数接近 1 表示高度正相关，接近 0 表示不相关，接近 -1 表示负相关。
参数说明
| 参数 | 必填 | 默认值 | 说明 |
|---|---|---|---|
--input / -i |
是 | - | 净值 CSV 文件路径 |
--risk-free / -rf |
否 | 0.02 | 年化无风险利率（如 0.03 表示 3%） |
--trading-days |
否 | 252 | 每年交易日数（A 股 252，美股 252） |
--output / -o |
否 | - | 输出文件路径（.csv 或 .json） |
--json |
否 | false | 以 JSON 格式输出到 stdout |
使用场景
- 对比多只 ETF 的风险收益特征，辅助资产配置决策
- 分析 ETF 间的相关性，构建低相关的投资组合
- 评估基金经理表现（夏普比率越高越好）
- 回测不同资产在特定时间段的表现
注意事项
- 本工具使用纯 Python 标准库，无需安装额外依赖
- 净值数据需要足够的时间跨度（建议至少 60 个交易日）才能获得有意义的统计指标
- 夏普比率受无风险利率假设影响，请根据实际市场环境调整
--risk-free参数 - 相关性矩阵需要至少 2 只 ETF 才能生成
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 · 113 tokens per session scan A ba96943aa8fc
fund-risk-analyzer is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 113 tokens to every session and 995 once invoked, about $0.0006 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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