mx-xuangu

mx-xuangu is a skill for Claude Code, Codex from chuancyzhang/deepseek-cowork. It costs 88 tokens per session (2,492 once invoked), scanned A, original, Apache-2.0.

A stock-screening tool that finds stocks, companies, and index or sector constituents using market and financial conditions. A stock screen is a filter that narrows a large list of stocks to those matching chosen criteria.

In plain words
What is it for?
Use it to filter stocks by market or financial indicators, inspect industries and sectors, find companies or index constituents, and request stock, company, sector, or index recommendations.
Why use it?
It helps avoid basing stock selection on outdated information. It can also identify companies within a specified industry or sector and the stocks included in an index.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to filter stocks by market or financial indicators, inspect industries and sectors, find companies or index constituents, and request stock, company, sector, or index recommendations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chuancyzhang/deepseek-cowork/mx-xuangu
Install

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.

Any agent
npx skills add chuancyzhang/deepseek-cowork --skill mx-xuangu
Clone the repo
git clone --depth 1 https://github.com/chuancyzhang/deepseek-cowork

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for mx-xuangu

README.md
[![agentmods](https://agentmods.dev/badge/skills/chuancyzhang/deepseek-cowork/mx-xuangu/github.svg)](https://agentmods.dev/skills/chuancyzhang/deepseek-cowork/mx-xuangu)
Your own site
<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.

agentmods 80×15 button for mx-xuangu

Your own site · 80×15
<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>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,492 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash fbe680334d70, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (mx_xuangu.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

ai_skills/eastmoney-miaoxiang/mx-xuangu/SKILL.md · 162 lines

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)

  1. 在妙想Skills页面获取apikey
  2. 将apikey存到环境变量,命名为MX_APIKEY:
    export MX_APIKEY=your_apikey_here
    
  3. 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:

  1. 在终端输出统计信息(行数、文件位置)
  2. 自动创建输出目录 /root/.openclaw/workspace/mx_data/output/
  3. 保存筛选结果到 CSV 文件,所有列名已转为中文
  4. 保存描述文件记录查询条件和结果统计
  5. 保存原始 JSON 响应供二次开发

Read the full file on GitHub · 162 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 162 lines · 88 tokens per session scan A fbe680334d70

Subscribe to this mod's changes

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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