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 chuancyzhang/deepseek-cowork --skill mx-xuangugit clone --depth 1 https://github.com/chuancyzhang/deepseek-coworkWrote 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/chuancyzhang/deepseek-cowork/mx-xuangu)<a href="https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/mx-xuangu"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/mx-xuangu/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/chuancyzhang/deepseek-cowork/mx-xuangu"><img src="https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/mx-xuangu.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 | $0.00088 | $0.02492 |
| Opus 5.5 | $0.00035 | $0.00997 |
| Sonnet 5.5 | $0.00018 | $0.00498 |
| Haiku 4.5 | $0.00009 | $0.00249 |
Grade A, and why
mx-xuangu 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 1mo 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mx-xuangu 妙想智能选股 skill
Cowork 适配说明:
MX_APIKEY和MX_API_URL由能力中心统一配置;默认产物目录为当前会话工作区的mx_data/output。下文中的export、OpenClaw 固定路径和直接安装说明仅作为上游参考。
本 Skill 支持基于股票选股条件,如行情指标、财务指标等,筛选满足条件的股票;可查询指定行业 / 板块内的股票、上市公司,以及板块指数的成分股;同时支持股票、上市公司、板块 / 指数推荐等相关任务,采用此skill可避免大模型在选股时使用了过时信息。
功能说明
根据用户问句中的选股条件,智能解析并转换为API可识别的选股规则,调用东方财富官方选股接口进行筛选,并返回符合条件的股票列表及相关统计信息。
配置
- API Key: 通过环境变量
MX_APIKEY设置 - 默认输出目录:
/root/.openclaw/workspace/mx_data/output/(自动创建) - 输出文件名前缀:
mx_xuangu_ - 输出文件:
mx_xuangu_{query}.csv- 筛选结果 CSV 文件mx_xuangu_{query}_description.txt- 筛选结果描述文件mx_xuangu_{query}_raw.json- API 原始 JSON 数据
使用方式(直接 Python 脚本调用)
- 在妙想Skills页面获取apikey
- 将apikey存到环境变量,命名为MX_APIKEY:
export MX_APIKEY=your_apikey_here - 直接运行 Python 脚本选股,支持自然语言条件:
# ==================== 常用调用示例 ====================
# 1. 按行情条件筛选
python ./mx_xuangu.py "今日涨幅大于2%的A股"
python ./mx_xuangu.py "成交量大于10亿的股票"
python ./mx_xuangu.py "市盈率小于20并且市净率小于2"
python ./mx_xuangu.py "股价在10元到20元之间"
# 2. 按财务条件筛选
python ./mx_xuangu.py "净利润增长率大于30%的股票"
python ./mx_xuangu.py "净资产收益率大于15%的公司"
python ./mx_xuangu.py "股息率大于3%的银行股"
# 3. 指定行业/板块
python ./mx_xuangu.py "新能源板块市盈率小于30的股票"
python ./mx_xuangu.py "白酒板块涨幅大于1%的股票"
python ./mx_xuangu.py "半导体行业毛利率大于40%的公司"
# 4. 指数成分股筛选
python ./mx_xuangu.py "沪深300成分股中分红率最高的10只股票"
python ./mx_xuangu.py "创业板成分股中市盈率小于30"
# 5. 组合条件(用逗号分隔条件)
python ./mx_xuangu.py "价格小于20元,市盈率小于20,涨幅大于1%的股票"
python ./mx_xuangu.py "ROE大于15%,净利润连续三年增长"
# 也可以使用 --query 参数
python ./mx_xuangu.py --query "市盈率小于20的银行股"
⚠️ 安全注意事项
- 外部请求: 本 Skill 会将用户的查询关键词(Keyword)发送至东方财富官方 API 接口 (
mkapi2.dfcfs.com) 进行解析与检索。- 数据用途: 提交的数据仅用于匹配选股条件,不包含个人隐私信息。
- 凭据保护: API Key 仅通过环境变量
MX_APIKEY在服务端或受信任的运行环境中使用,不会在前端明文暴露。
输出说明
脚本执行后会:
- 在终端输出统计信息(行数、文件位置)
- 自动创建输出目录
/root/.openclaw/workspace/mx_data/output/ - 保存筛选结果到 CSV 文件,所有列名已转为中文
- 保存描述文件记录查询条件和结果统计
- 保存原始 JSON 响应供二次开发
What ships with it
2 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.
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.
- 1mo ago First seen · 162 lines · 88 tokens per session scan A fbe680334d70
mx-xuangu is a skill published in the GitHub repository chuancyzhang/deepseek-cowork (18 stars, last pushed today), licensed Apache-2.0. It adds 88 tokens to every session and 2,492 once invoked, about $0.0004 per session on Opus 5.5. 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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