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/godisego/hot-moneynpx agentmods add commands/godisego/hot-money/segmental-modelWrote 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/segmental-model)<a href="https://agentmods.dev/commands/godisego/hot-money/segmental-model"><img src="https://agentmods.dev/badge/commands/godisego/hot-money/segmental-model.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 | $0.00044 | $0.01357 |
| Opus 5 | $0.00022 | $0.00678 |
| Sonnet 5 | $0.00009 | $0.00271 |
| Haiku 4.5 | $0.00004 | $0.00136 |
Grade A, and why
segmental-model 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
92% identical to segmental-model — 2 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/segmental-model <股票代码或名称>
对目标股票做 bottom-up 分业务收入模型，跟自上而下的 DCF 互补校验。
为什么需要这个方法
现有 17 种机构方法里，DCF/3-statement/IC Memo 都是 top-down——从整体营收 × 单一增速假设推未来。遇到业务结构在转型的公司就失真：
- 贵州茅台 直销比例 17% → 目标 50%+
- 宁德时代 动力电池 vs 储能 vs 海外 三条曲线增速完全不同
- Credo 收购 SiPh 后，Optics 线增速和 AEC 线差两倍
单一营收增速掩盖了这些分化。本方法把公司拆 3-5 条业务线，每条独立给 driver，三情景 3 年 projection，总量必须对账回当前营收。
工作流（agent 必须走完 5 步）
Step 1 · 生成骨架（脚本）
python skills/deep-analysis/scripts/compute_segmental.py discover <ticker>
读 5_chain.breakdown_top + 1_financials.revenue_history + 15_events，产出 .cache/<ticker>/segmental_skeleton.json：
{
"ticker": "600519.SH",
"name": "贵州茅台",
"currency": "CNY",
"total_revenue_latest_yi": 1721.4,
"segments": [
{"name": "茅台酒", "latest_revenue_yi": 1460.3, "latest_share_pct": 84.83, ...},
{"name": "系列酒", "latest_revenue_yi": 246.0, "latest_share_pct": 14.29, ...}
],
"inflection_candidates": [
"直销渠道占比持续提升",
"1935 新品上市拓展年轻客群"
]
}
Step 2 · 读骨架 + 识别核心 thesis（agent）
Agent 必做：
- 打开
.cache/<ticker>/segmental_skeleton.json - 结合
6_research.reports（卖方研报对业务分段的看法）+14_moat+13_policy - 识别 核心 inflection（1-2 条）——例如：
- 茅台 = "直销占比 17% → 50%+"
- 宁德 = "海外储能 0 → 30%+"
- Credo = "copper → SiPh optics"
- 围绕 inflection 组织 segment 叙事
Step 3 · 填 driver（agent）
对每个 segment 写：
- drivers: 价 × 量 × 市占 × 渗透 —— 至少列 2 个
- ✓ 好例子：
["ASP +5%/年", "shipment +20%/年"] - ✗ 坏例子：
["行业景气"]
- ✓ 好例子：
- thesis_tag:
growth_engine/cash_cow/declining/cyclical/turnaround - bull_growth_3y_cagr / base_growth_3y_cagr / bear_growth_3y_cagr 必须满足 bull ≥ base ≥ bear
Step 4 · 写回 + 校验（脚本）
写 .cache/<ticker>/segmental_model.json，然后：
python skills/deep-analysis/scripts/compute_segmental.py validate <ticker>
校验规则：
- 🔴 sum(segment.latest_revenue) 必须对账回 total_revenue ±10%
- 🔴 每个 segment bull ≥ base ≥ bear 单调
- 🟡 Base 情景 3 年总增速 > 100% 需要明确收购/新业务 note
- 🟡 每个 segment 必须有 ≥ 1 个 driver
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 · 112 lines · 44 tokens per session scan A 38e09c8cf674
segmental-model is a command published in the GitHub repository godisego/hot-money (2 stars, last pushed 4mo ago), licensed MIT. It adds 44 tokens to every session and 1,357 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to segmental-model, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
finops-status
Orientation — say where an opportunity or assignment sits in the five-step FinOps lifecycle and what unlocks next. Useful when a record has no active stage: an opportunity while its assignments do the work, an assignment whose plan has not been approved yet, or a rejected or archived assignment. Read-only; mutates…
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.