mx_data

Query stock prices, fund flows, and valuations via natural language.

Updated Jan 25, 2026

Search Tags:#finance #query #data retrieval #market data #historical data #stock prices

Ready to run

npx skills add https://github.com/xfs96192/claude-config --skill mx-data-xfs96192

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mx_data
Source: https://github.com/xfs96192/claude-config/tree/main/skills/mx_data
Command: npx skills add https://github.com/xfs96192/claude-config --skill mx-data-xfs96192

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

本技能通过自然语言查询，解决获取实时与历史金融数据的繁琐与时效性问题，避免依赖记忆或过时信息。

Core Features & Use Cases

实时行情与历史行情查询：获取股票、行业指数及相关财务指标的数值。
资金流向与估值查询：分析主力资金流向、PE/PB、市值等关键指标。
基本信息与经营数据：获取上市公司股本、成立日期、主要经营数据等信息。
使用场景示例：当你需要对某支股票进行最新价格、过去趋势和估值的综合对比时，直接询问本技能即可。

Quick Start

请用自然语言向技能提出查询请求，如：查询最新价格/历史行情/市值等信息。

Frequently Asked Questions about mx_data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I query historical stock prices using natural language?▼

The skill returns real-time stock prices and market data from natural language queries. You simply ask for the latest price or current market quote of a specific stock to retrieve the structured financial data.

Can I get corporate fundamentals and PE/PB valuations through natural language data retrieval?▼

Yes, natural language data retrieval supports querying corporate fundamentals and valuations. You can directly ask for a company's PE/PB ratios, market capitalization, and primary operational data to get structured JSON responses.

Do I need an API key to retrieve market data and fund flows?▼

You do not need to provide an API key to retrieve fund flows. The system uses the MX_APIKEY environment variable for authentication but automatically falls back to a built-in key, allowing immediate access to fund flow data.

What are the limitations when querying financial data across specified time ranges?▼

The main limitation when querying financial data across time ranges is response size. You are guided to avoid excessive date ranges to prevent oversized data responses and ensure successful data retrieval.