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 qingkongzhiqian/groundapi --skill groundapi-stock-screenergit clone --depth 1 https://github.com/qingkongzhiqian/groundapiWrote 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/qingkongzhiqian/groundapi/groundapi-stock-screener)<a href="https://agentmods.dev/skills/qingkongzhiqian/groundapi/groundapi-stock-screener"><img src="https://agentmods.dev/badge/skills/qingkongzhiqian/groundapi/groundapi-stock-screener/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/qingkongzhiqian/groundapi/groundapi-stock-screener"><img src="https://agentmods.dev/badge/skills/qingkongzhiqian/groundapi/groundapi-stock-screener.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 | $0.00052 | $0.00983 |
| Opus 5 | $0.00026 | $0.00491 |
| Sonnet 5 | $0.00010 | $0.00197 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
groundapi-stock-screener 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 11d 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 股选股助手
当用户要求选股、筛选、或类似以下表达时自动触发:
- "帮我找几只低估值的股票"、"高分红的有哪些"
- "PE 低于 10 的银行股"
- "市值 100 亿以下的科技股"
- "AI 概念里哪些股票不错"
前置条件
{
"mcpServers": {
"groundapi": {
"url": "https://mcp.groundapi.net/mcp",
"headers": {
"X-API-Key": "sk_gapi_xxxxx"
}
}
}
}
执行流程
Step 1 — 理解用户需求,映射为筛选参数
| 用户说 | 映射为 |
|---|---|
| "低估值" | pe_max=15 |
| "高分红" | min_dividend_yield=3, sort_by="dividend_yield" |
| "银行股" | industry="银行" |
| "AI概念" | concept="AI" |
| "小盘股" | max_market_cap=10000000000 |
| "大盘蓝筹" | min_market_cap=50000000000, pe_max=20 |
也可以用预置组合快速筛选:
filter_preset="low_pe_high_div"→ PE<15 且 股息率>3%filter_preset="small_cap_growth"→ 市值<100亿filter_preset="large_cap_stable"→ 市值>500亿 且 PE<20
Step 2 — 执行筛选
finance_screen(industry="银行", pe_max=10, sort_by="pe", order="asc", limit=20)
Step 3 — 宏观环境参考
并行获取宏观数据,为选股提供大环境背景:
life_calendar()→ 确认是否交易日finance_gold_price()→ 金价走势(避险情绪参考)
Step 4 — 对优选结果深度挖掘
从筛选结果中取 Top 3-5 只,调用 summary 获取多维度数据:
finance_stock(symbol="601398,601939,600036", aspects="overview")
或逐个深度分析:
finance_stock(symbol="601398", aspects="summary")
Step 5 — 输出选股报告
## 选股结果 — {筛选条件描述}
### 宏观环境
- 日期:{YYYY-MM-DD}({交易日/非交易日})
- 黄金:¥XXX/克 | 美元兑人民币:X.XXXX
### 筛选条件
- 行业:银行
- PE 上限:10
- 排序:PE 从低到高
### 结果(共XX只)
| 排名 | 代码 | 名称 | PE | PB | 股息率 | 市值(亿) | 今日涨跌 |
|------|------|------|-----|-----|--------|---------|---------|
| 1 | 601398 | 工商银行 | 5.2 | 0.5 | 6.1% | 18000 | +0.3% |
| 2 | ... | ... | ... | ... | ... | ... | ... |
### Top 3 快速概览
**1. 工商银行(601398)**
- 主力资金:近5日净流入XX亿
- 技术面:均线多头排列,MACD金叉
- 股东:户数连续3期减少(筹码集中)
**2. ...**
以上数据基于公开信息,不构成投资建议。
注意事项
finance_screen依赖数据库快照数据,非实时交易数据- 筛选条件可自由组合,所有参数均可选
- PE 为负的股票默认被过滤(亏损企业)
- 输出语言跟随用户
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.
- 11d ago First seen · 110 lines · 52 tokens per session scan A 05abf176876c
groundapi-stock-screener is a skill published in the GitHub repository qingkongzhiqian/groundapi (0 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 983 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 2026-08-31.
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