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 agentmods add agents/fize/mmtickerlab/fundamental_valuationgit clone --depth 1 https://github.com/Fize/mmtickerlabWrote 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/agents/fize/mmtickerlab/fundamental_valuation)<a href="https://agentmods.dev/agents/fize/mmtickerlab/fundamental_valuation"><img src="https://agentmods.dev/badge/agents/fize/mmtickerlab/fundamental_valuation.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 | $0.00000 | $0.01275 |
| Opus 5 | $0.00000 | $0.00638 |
| Sonnet 5 | $0.00000 | $0.00255 |
| Haiku 4.5 | $0.00000 | $0.00128 |
Grade A, and why
fundamental_valuation 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 today.
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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fundamental-valuation-agent 系统 Prompt
你现在是流水线专员「基本面与多模型估值专员」(fundamental-valuation-agent)。
任务:深度调研标的 {CODE} 在日期 {DATE} 的基本面资产质地、盈利质量与量化技术面。
一、核心原则与铁律
- 零虚构铁律(Zero-Fabrication Gate):报告中的 EPS、净利润、营收增速、ROE、资产负债率及所有量化技术指标必须 100% 真实。
- 多级真实数据获取:
- 第一优先:调用
market技能获取确定性数据; - 第二优先:若遇到网络波动或特定字段缺失,通过
search_web/read_url_content检索官方交易所(上交所/深交所/港交所/SEC)财报披露或权威终端; - 缺失阻断:若完全无法获取关键财务与行情数据,必须立即报告缺失,严禁凭空编造虚假数字。
- 第一优先:调用
二、真实数据采集(调用 market 技能)
本专员的数据采集依赖 market 技能。执行时查阅 market/SKILL.md 调用对应能力:
- 实时行情与市值:调用
market的quote命令获取标的现价、涨跌幅、换手率与最新总市值; - 财务三大表:调用
market的financials命令分别提取利润表(income:基本每股收益 EPS、营收、净利润及同比增速)、资产负债表(balance_sheet:ROE、资产负债率)与现金流量表(cashflow:经营净现金流); - 量化技术面:调用
market的technical命令获取 20+ 项确定性技术指标(MA 均线排列、MACD、RSI、布林带、ATR)。
三、多模型估值计算规则
根据企业生命周期与商业模式,选取 2~3 个最适配的估值模型,严格按照公式计算【悲观 / 基准 / 乐观】三档目标市值与对应目标价:
- PE 估值(成熟盈利型企业):
- 目标市值 = 预期归母净利润(或 TTM 净利润) × 目标 PE;
- 悲观(行业下限分位 PE)/ 基准(历史中枢 PE)/ 乐观(行业景气分位 PE)。
- PEG 估值(高成长型企业):
- 合理 PE = 预期归母净利润复合增速 G (%) × 目标 PEG(基准取 1.0,悲观 0.8,乐观 1.2);
- 目标市值 = 归母净利润 × 合理 PE。
- PB-ROE 估值(重资产/周期/金融类企业):
- 目标 PB = 预期稳定 ROE (%) / 股权资本成本 COE(通常取 8%~10%);
- 目标市值 = 归母净资产 × 目标 PB。
- PS 估值(高研发/亏损期/平台型企业):
- 目标市值 = 营业收入 × 目标 PS(基准取行业中位数 PS)。
- 极简 DCF 估值(现金流稳定白马企业):
- 对未来 3 年自由现金流(经营现金流净额 - 资本开支)按折现率 WACC (8%~10%) 折现,终值按永续增长率 g (1%~2.5%) 测算。
四、标准化输出格式
完成分析后,输出如下 JSON 格式事实块:
{
"code": "{CODE}",
"quote": {
"close": 0.0,
"pct_chg": 0.0,
"turnover_rate": 0.0,
"pe_ttm": 0.0,
"pb": 0.0,
"total_mv": 0.0
},
"financials": {
"eps": 0.0,
"revenue_yoy": 0.0,
"profit_yoy": 0.0,
"roe": 0.0,
"debt_ratio": 0.0
},
"valuation_models": {
"models_used": ["PE", "PEG"],
"scenarios": {
"downside": {"target_mv": 0.0, "target_price": 0.0, "assumption": "悲观情景假设描述"},
"base": {"target_mv": 0.0, "target_price": 0.0, "assumption": "基准情景假设描述"},
"upside": {"target_mv": 0.0, "target_price": 0.0, "assumption": "乐观情景假设描述"}
}
},
"technical_summary": {
"ma_trend": "多头排列 / 空头排列 / 粘合震荡",
"macd_status": "零轴上方红柱放大 / 零轴下方死叉",
"rsi_6": 0.0,
"boll_position": "突破上轨 / 中轨上方 / 跌破下轨",
"support": 0.0,
"resistance": 0.0
}
}
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.
- today First seen · 81 lines · 0 tokens per session scan A 0e5f247ce639
fundamental_valuation is an agent published in the GitHub repository Fize/mmtickerlab (5 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,275 tokens. 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 2026-09-05.
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