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 guyulong/cn-agent-skills --skill stock-analysisgit clone --depth 1 https://github.com/guyulong/cn-agent-skillsWrote 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/guyulong/cn-agent-skills/stock-analysis)<a href="https://agentmods.dev/skills/guyulong/cn-agent-skills/stock-analysis"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/stock-analysis/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/guyulong/cn-agent-skills/stock-analysis"><img src="https://agentmods.dev/badge/skills/guyulong/cn-agent-skills/stock-analysis.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 | $0.00023 | $0.00690 |
| Opus 5 | $0.00012 | $0.00345 |
| Sonnet 5 | $0.00005 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
stock-analysis 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 9d 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.
What it actually says
A股/港股技术分析
使用场景
生成股票技术分析脚本,支持常见技术指标计算和可视化。
支持的指标
- K线图(蜡烛图)
- MA(移动平均线):MA5, MA10, MA20, MA60
- MACD(指数平滑异同移动平均线)
- RSI(相对强弱指标)
- KDJ(随机指标)
- BOLL(布林带)
- 成交量分析
数据源
A股
# 方式1:akshare(推荐,免费)
import akshare as ak
df = ak.stock_zh_a_hist(symbol="000001", period="daily", start_date="20240101")
# 方式2:tushare(需要token)
import tushare as ts
pro = ts.pro_api('your_token')
df = pro.daily(ts_code='000001.SZ', start_date='20240101')
港股
import akshare as ak
df = ak.stock_hk_hist(symbol="00700", period="daily", start_date="20240101")
分析模板
import akshare as ak
import pandas as pd
import matplotlib.pyplot as plt
# 设置中文显示
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS']
plt.rcParams['axes.unicode_minus'] = False
# 获取数据
symbol = "000001" # 平安银行
df = ak.stock_zh_a_hist(symbol=symbol, period="daily", start_date="20240101")
# 计算MA
df['MA5'] = df['收盘'].rolling(5).mean()
df['MA10'] = df['收盘'].rolling(10).mean()
df['MA20'] = df['收盘'].rolling(20).mean()
# 计算MACD
exp1 = df['收盘'].ewm(span=12, adjust=False).mean()
exp2 = df['收盘'].ewm(span=26, adjust=False).mean()
df['MACD'] = exp1 - exp2
df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
df['Histogram'] = df['MACD'] - df['Signal']
# 绘图
fig, axes = plt.subplots(3, 1, figsize=(14, 10))
# ... 绘制K线、均线、MACD
plt.title(f'{symbol} 技术分析')
plt.show()
输出建议
- 中文标签和注释
- 标注买卖信号
- 支持导出为图片
- 可选输出分析报告
注意事项
- 仅供学习研究,不构成投资建议
- akshare 接口可能变动,需定期维护
- 港股数据可能有延迟
- 注意数据频率限制
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.
- 9d ago First seen · 88 lines · 23 tokens per session scan A ab2847b48935
stock-analysis is a skill published in the GitHub repository guyulong/cn-agent-skills (3 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 690 once invoked, about $0.0001 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 2026-08-31.
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