Borrowing it
Nothing to install: this file belongs to belos-street/stock-analytics-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/belos-street/stock-analytics-skill/main/.agents/skills/market-valuation/SKILL.mdgit clone --depth 1 https://github.com/belos-street/stock-analytics-skillWrote 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/belos-street/stock-analytics-skill/market-valuation)<a href="https://agentmods.dev/skills/belos-street/stock-analytics-skill/market-valuation"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/market-valuation/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/belos-street/stock-analytics-skill/market-valuation"><img src="https://agentmods.dev/badge/skills/belos-street/stock-analytics-skill/market-valuation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.02418 |
| Opus 5 | $0.00031 | $0.01209 |
| Sonnet 5 | $0.00012 | $0.00484 |
| Haiku 4.5 | $0.00006 | $0.00242 |
Grade A, and why
market-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 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.
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A股市场估值高低判断
核心定位
核心目标
通过对比万得全A、沪深300、上证指数的历史估值分位数，帮助用户判断A股市场的整体高低位置，为加仓减仓时机提供参考依据。
市场估值逻辑
核心原理：
- 万得全A（Wind All A）：代表A股全市场，涵盖所有上市公司，是最全面的市场指标
- 沪深300：代表A股大盘蓝筹，反映核心资产估值
- 上证指数：仅代表上海交易所上市公司，反映上海市场表现
估值关系：
- 万得全A > 沪深300：说明小盘股、成长股估值更高，市场偏向成长风格
- 沪深300 >> 上证指数：说明沪深300成分股表现优于上证指数，大盘蓝筹强于上海整体
- 当万得全A估值处于历史低位时，通常是较好的布局时机
数据获取
优先使用CLI获取A股指数数据：
# 查询A股主要指数
cd {{PROJECT_PATH}}
# 沪深300指数（指数代码：sh000300）
# 上证指数（指数代码：sh000001）
bun run main.ts -s sh000001,sh000300
万得全A数据获取： 访问 Wind官网 可以获取万得全A的实时估值数据：
- 网址：
https://www.wind.com.cn/portal/zh/WindIndex/detail.html?id=2&v=1 - 该页面提供万得全A的PE、PB历史分位数等关键数据
备选方案：如无法访问Wind官网，使用WebSearch搜索各指数的PE、PB历史分位数
估值分析指标
主要估值指标
1. 市盈率（PE）
定义：市盈率是股票价格与每股收益的比率，反映投资者为获得1元利润愿意支付的价格。
历史分位数判断：
- PE历史分位数 < 20%：历史低位，市场低估，建议加仓
- PE历史分位数 20-50%：历史较低，市场偏低，可继续持有
- PE历史分位数 50-80%：历史较高，市场偏高，考虑减仓
- PE历史分位数 > 80%：历史高位，市场高估，建议减仓或离场
2. 市净率（PB）
定义：市净率是股票价格与每股净资产的比率，反映投资者为获得1元净资产愿意支付的价格。
历史分位数判断：
- PB历史分位数 < 20%：历史低位，市场低估
- PB历史分位数 20-50%：历史较低，市场偏低
- PB历史分位数 50-80%：历史较高，市场偏高
- PB历史分位数 > 80%：历史高位，市场高估
3. 股息率
定义：股息率是股息与股票价格的比率，股息率越高说明市场估值越低。
历史分位数判断：
- 股息率历史分位数 > 80%：历史高位，市场低估（高股息吸引资金）
- 股息率历史分位数 50-80%：历史较高，市场偏低
- 股息率历史分位数 20-50%：历史较低，市场偏高
- 股息率历史分位数 < 20%：历史低位，市场高估
市场高低判断标准
综合判断逻辑
第一步：获取三个指数的估值数据
| 指数 | 代码 | 代表意义 |
|---|---|---|
| 万得全A | 881001 | 全市场估值 |
| 沪深300 | sh000300 | 大盘蓝筹估值 |
| 上证指数 | sh000001 | 上海市场估值 |
第二步：计算各指数的历史分位数
| 指数 | PE所处历史分位数 | PB所处历史分位数 | 股息率所处历史分位数 |
|---|---|---|---|
| 万得全A | 待查询 | 待查询 | 待查询 |
| 沪深300 | 待查询 | 待查询 | 待查询 |
| 上证指数 | 待查询 | 待查询 | 待查询 |
第三步：综合判断市场位置
| 市场位置 | 判断标准 | 操作建议 |
|---|---|---|
| 极度低估 | 万得全A PE分位数 < 15% 且 沪深300 PE分位数 < 15% | 全力加仓 |
| 低估 | 万得全A PE分位数 < 30% 且 沪深300 PE分位数 < 30% | 逐步加仓 |
| 合理偏低 | 万得全A PE分位数 30-50% | 持有为主，可定投 |
| 合理偏高 | 万得全A PE分位数 50-70% | 持有为主，暂停定投 |
| 高估 | 万得全A PE分位数 > 70% 且 沪深300 PE分位数 > 70% | 逐步减仓 |
| 极度高估 | 万得全A PE分位数 > 85% 且 沪深300 PE分位数 > 85% | 全力减仓 |
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 · 233 lines · 62 tokens per session scan A e2adb6bb1668
market-valuation is a skill published in the GitHub repository belos-street/stock-analytics-skill (49 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 2,418 once invoked, about $0.0003 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 2026-08-30.
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