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 godisego/hot-money --skill investor-panelgit 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/skills/godisego/hot-money/investor-panel)<a href="https://agentmods.dev/skills/godisego/hot-money/investor-panel"><img src="https://agentmods.dev/badge/skills/godisego/hot-money/investor-panel/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/godisego/hot-money/investor-panel"><img src="https://agentmods.dev/badge/skills/godisego/hot-money/investor-panel.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 | $0.00124 | $0.01356 |
| Opus 5 | $0.00062 | $0.00678 |
| Sonnet 5 | $0.00025 | $0.00271 |
| Haiku 4.5 | $0.00012 | $0.00136 |
Grade A, and why
investor-panel 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 12d 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
86% identical to investor-panel — 6 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investor Panel · 50 贤评审团
调用上下文
读取以下输入：
.cache/{ticker}/dimensions.json— 19 维评分.cache/{ticker}/raw_data.json— 原始数据scripts/lib/investor_db.py— 50 人元数据scripts/lib/seat_db.py— 22 位游资射程规则
输出：
.cache/{ticker}/panel.json— 50 个 Signal + 投票统计
严格输出格式（Pydantic Signal，抄自 ai-hedge-fund）
每个投资者必须返回严格 JSON：
{
"investor_id": "buffett",
"name": "巴菲特",
"group": "A",
"avatar": "avatars/buffett.svg",
"signal": "bullish | neutral | bearish",
"confidence": 87,
"score": 82,
"verdict": "强烈买入 | 买入 | 关注 | 观望 | 等待 | 回避 | 不达标 | 不适合",
"reasoning": "1-3 句具体逻辑",
"comment": "用该投资者语言风格的金句 1-2 句",
"pass": ["..."],
"fail": ["..."],
"ideal_price": 16.20,
"period": "3-5 年"
}
Confidence 校准规则：
- 85-100：核心方法论硬指标全部命中或全部不命中
- 60-84：多数命中
- 30-59：部分命中、需要等待信号
- 0-29：方法论不适用此股 / 信息不足
执行步骤
Step 1: 加载元数据
from lib.investor_db import INVESTORS, by_group
from lib.seat_db import SEATS, is_in_range
Step 2: 对每位投资者
- 取出
fields白名单 - 从 dimensions.json 提取相关字段
- 读取该投资者所在 group 的 reference 文件（按需）
- 用该投资者的方法论 + 语言样本生成 Signal（Claude 自己生成）
- 校验 JSON 合法性
Step 3: 游资射程预过滤（F 组特殊）
对 22 位游资，先用 is_in_range(nickname, ticker_features) 判断是否在射程内：
- 在射程 → 正常评分
- 不在射程 →
signal: "neutral",verdict: "不适合",confidence: 90,comment: "{nick}的射程是{style}，这只票不在风格内。"
Step 4: 汇总投票
{
"panel_consensus": (bullish_count / 50) * 100,
"vote_distribution": Counter(verdict for i in investors),
"signal_distribution": Counter(signal for i in investors),
"investors": [...]
}
7 大流派详细方法论
按需读取下列 references：
| 组 | 文件 | 人数 |
|---|---|---|
| A 经典价值 | references/group-a-classic-value.md |
6 |
| B 成长投资 | references/group-b-growth.md |
4 |
| C 宏观对冲 | references/group-c-macro-hedge.md |
5 |
| D 技术趋势 | references/group-d-technical.md |
4 |
| E 中国价投 | references/group-e-china-value.md |
6 |
| F 游资 | references/group-f-china-youzi.md |
22 |
| G 量化系统 | references/group-g-quant.md |
3 |
📚 语料库 (必读)
What ships with it
9 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.
- assets/investor-cards.json 8.7 KB
- references/group-a-classic-value.md 4.4 KB
- references/group-b-growth.md 2.8 KB
- references/group-c-macro-hedge.md 2.7 KB
- references/group-d-technical.md 2.4 KB
- references/group-e-china-value.md 2.9 KB
- references/group-f-china-youzi.md 5.6 KB
- references/group-g-quant.md 1.6 KB
- references/quotes-knowledge-base.md 56 KB
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.
- 12d ago First seen · 122 lines · 124 tokens per session scan A 8638ac31fa03
investor-panel is a skill published in the GitHub repository godisego/hot-money (2 stars, last pushed 4mo ago), licensed MIT. It adds 124 tokens to every session and 1,356 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to investor-panel, differing in 6 lines, and is treated as a copy.
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