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.
git clone --depth 1 https://github.com/godisego/hot-moneyWrote 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/commands/godisego/hot-money/dcf)<a href="https://agentmods.dev/commands/godisego/hot-money/dcf"><img src="https://agentmods.dev/badge/commands/godisego/hot-money/dcf.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.00440 |
| Opus 5 | $0.00018 | $0.00220 |
| Sonnet 5 | $0.00007 | $0.00088 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
dcf 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 dcf — 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.
What it actually says
/dcf <股票代码或名称>
对目标股票做完整的 DCF 估值，按照 JPMorgan / Goldman / Morgan Stanley 投行标准。
工作流
- 解析股票代码（
002273.SZ/00700.HK/AAPL等） - 拉取基础数据：
python skills/deep-analysis/scripts/fetch_basic.py <ticker> - 提取特征：
from lib.stock_features import extract_features - 调用 DCF：
from lib.fin_models import compute_dcf dcf = compute_dcf(features, assumptions={ "stage1_growth": 0.10, "stage2_growth": 0.05, "terminal_g": 0.025, "target_debt_ratio": 0.30, }) - 展示结果：
- WACC 分解（k_e / k_d / weights）
- 10 年 FCF 预测（显式期 5 + 过渡期 5）
- 终值 + PV 折现
- EV → 净债 → 股权 → 每股内在价值
- 安全边际 % 和结论（深度低估/合理/高估）
- 5×5 敏感性表（WACC ±200bp × 终值 g ±100bp），中心格等于基础案例
A 股默认参数（可覆盖）
- 无风险利率 2.5%（10Y 国债）
- 股权风险溢价 6%
- 标准税率 25%（高新 15%）
- Beta 1.0
- 终值永续 2.5%
方法论参考
skills/deep-analysis/references/fin-methods/README.md
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 · 44 lines · 36 tokens per session scan A eef8ba8ab1ab
dcf is a command published in the GitHub repository godisego/hot-money (2 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 440 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to dcf, differing in 0 lines, and is treated as a copy.
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