What makes mx-financial-assistant notable?

6.4K downloads. Trust evidence available

How should mx-financial-assistant be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

mx-financial-assistant

基于东方财富权威金融数据库，打造覆盖"数据-资讯-知识-分析-决策"全链条的智能问答服务。通过自然语言交互，为用户提供从基础金融知识科普到专业投资研究支持的全方位解决方案，实现"一问即达"的高效金融信息获取体验。涵盖七大核心能力：全市场金融数据实时查询（A港美股、基金、债券）、全网财经资讯智能检索（公告、研报... Skill: mx-financial-assistant Owner: financial-ai-analyst Summary: 基于东方财富权威金融数据库，打造覆盖"数据-资讯-知识-分析-决策"全链条的智能问答服务。通过自然语言交互，为用户提供从基础金融知识科普到专业投资研究支持的全方位解决方案，实现"一问即达"的高效金融信息获取体验。涵盖七大核心能力：全市场金融数据实时查询（A港美股、基金、债券）、全网财经资讯智能检索（公告、研报... Tags: latest:1.0.2 Version history: v1.0.2 | 2026-04-17T11:19:18.139Z | user Publish 1.0.2 v1.0.1 | 2026-04-03T09:52:16.461Z | user - 新增对 EM_API_KEY 环境变量的支持，用于接口鉴权。 - 在文档中增加了环境变量配置说明。 - 增加了 Py

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

6.4k

Updated

Oct 9, 2026

Version

1.0.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 6.4K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 6.4K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.2release · observed Apr 17, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s172qfps754dhtdynwv0gwshes83mtd4:mx-financial-assistant

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-financial-ai-analyst-mx-financial-assistant/snapshot"

Documentation

CLAWHUB

19,550 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: mx-financial-assistant
description: >
  基于东方财富权威金融数据库，打造覆盖"数据-资讯-知识-分析-决策"全链条的智能问答服务。通过自然语言交互，
  为用户提供从基础金融知识科普到专业投资研究支持的全方位解决方案，实现"一问即达"的高效金融信息获取体验。
  涵盖七大核心能力：全市场金融数据实时查询（A港美股、基金、债券）、全网财经资讯智能检索（公告、研报、新闻、政策）、
  全球宏观经济数据追踪（国民经济、货币财政、产业就业）、多维度智能选股选基（技术面、基本面、消息面）、
  专业金融百科知识问答、深度金融市场分析（趋势、资金、情绪）、以及实时热点事件解读（政策、公告、行业变革）。
  适配多元化应用场景，满足机构投资者在投资研究、资产配置、风险管理、策略回测等方面的专业需求，
  同时支持个人用户的理财决策、知识学习、行情监控与投资组合管理。
  当用户提出自然语言问题并希望获得智能回答时使用此技能，包括但不限于：
  "帮我查一下"、"请问"、"分析一下"、"选股"、"推荐股票"、"XX怎么样"、"XX是什么"、
  "解释一下"、"总结一下"、"最新政策"、"宏观数据"、"市场行情"、"基金筛选"等问答类请求。
  支持标准模式和深度思考模式（deepThink）。
  此技能不适用于需要生成完整报告的场景（如行业跟踪报告、首次覆盖报告），这些场景应使用对应的报告生成技能。
metadata:
  {
    "openclaw": {
      "requires": {
        "env":["EM_API_KEY"]
      },
      "install": [
        {
          "id": "pip-deps",
          "kind": "python",
          "package": "httpx",
          "label": "Install Python dependencies"
        }
      ]
    }
  }
---

# 金融问答 Skill

## 概述

基于东方财富权威金融数据库，打造覆盖"数据-资讯-知识-分析-决策"全链条的智能问答服务。通过自然语言交互，为用户提供从基础金融知识科普到专业投资研究支持的全方位解决方案，实现"一问即达"的高效金融信息获取体验。以对话式交互降低金融数据获取门槛，让专业投研能力触手可及。

支持标准模式和深度思考模式，返回总结回答内容（含选股类数据结果）及溯源参考（资讯、公告、数据等）。本接口为一次性返回回答结果的接口形式。

---

## 环境变量

| 变量名 | 说明 | 默认 |
|--------|------|------|
| `EM_API_KEY` | 接口鉴权密钥（必填） | 无 |

### 1. 配置 `EM_API_KEY`

```bash
# macOS / Linux
export EM_API_KEY="your_api_key_here"
```

```powershell
# Windows PowerShell
$env:EM_API_KEY="your_api_key_here"
```

## 核心能力

### 1. 金融市场分析

支持自然语言获取多维度市场分析，涵盖市场趋势研判、技术面解析、基本面评估、资金面监测及市场情绪量化分析等，覆盖A港美等全球主要市场指数与板块表现。

**适用场景**：每日复盘、趋势跟踪、策略制定、风险预警及市场情绪研判等，辅助投资决策。

### 2. 金融热点事件解读

支持自然语言追踪并深度解读资本市场重大事件、突发政策、行业变革及公司公告等热点内容，提供事件逻辑梳理、影响路径分析、产业链传导效应及投资机会与风险评估。

**适用场景**：事件驱动策略、政策影响评估、市场情绪跟踪及投资机遇挖掘等。

### 3. 金融数据查询

支持自然语言查询金融数据，覆盖A港美、基金、债券等多种资产，含实时行情、公司信息、估值、财务报表等。

**适用场景**：投资研究、交易复盘、市场监控、行业分析、信用研究、财报审计、资产配置等，适配机构与个人多元需求。

### 4. 金融资讯搜索

支持自然语言搜索全网最新公告、研报、财经新闻、交易所动态及官方政策等，覆盖全球市场标的。

**适用场景**：热点捕捉、舆情监控、研报速览、公告精读及投资决策等。

### 5. 宏观经济数据查询

支持自然语言查询全球宏观经济数据，涵盖国民经济核算、价格指数、货币金融、财政收支、对外贸易、就业民生、产业运行等多个领域。

**适用场景**：宏观经济研究、市场分析、政策解读等多元专业场景。

### 6. 选股 / 选板块 / 选基金

支持通过自然语言输入筛选A港美股、基金、债券等多种资产，支持多元指标筛选，含技术面、消息面、基本面及市场情绪等。

**适用场景**：全球资产速筛、跨市场监控、投资组合构建、策略回测等。

### 7. 金融百科知识问答

支持自然语言问答形式查询金融专业术语、投资理论、交易规则、市场机制及产品详情等基础知识，覆盖股票、基金、债券、衍生品、宏观经济学等多个知识领域。

**适用场景**：投资入门学习、专业概念速查、业务流程理解、金融知识科普及投资者教育等，降低专业金融知识获取门槛。

---

## 处理流程

以用户自然语言输入问句作为入参，调用 `scripts/generate_answer.py` 金融问答脚本，返回结果为总结内容纯 Markdown：

```bash
python3 {baseDir}/scripts/generate_answer.py --query "用户原始问题"
```

如需开启深度思考模式：

```bash
python3 {baseDir}/scripts/generate_answer.py --query "用户原始问题" --deep-think
```

**模式选择规则**：
- 默认使用标准模式
- 当用户明确要求"深度分析"、"深度思考"、"详细分析"、"仔细想想"等表述时，开启深度思考模式

---

## 脚本输出结构

脚本返回 JSON，核心字段：

| 字段 | 类型 | 说明 |
|---|---|---|
| `ok` | Boolean | 是否成功 |
| `answer` | String | 模型回答（Markdown 格式，即接口 `data.displayData`） |
| `references` | Array | 溯源参考列表（即接口 `data.refIndexList`） |

### references 元素说明

| 字段 | 类型 | 说明 |
|---|---|---|
| `refId` | Number | 引用编号（如 1） |
| `type` | String | 数据类型：`查数` `公告` `研报` `资讯` `选股/基` |
| `referen

_meta.json

{
  "ownerId": "kn7b4ptpdag877t9kmq8axyja182taab",
  "slug": "mx-financial-assistant",
  "version": "1.0.2",
  "publishedAt": 1776424758139
}

skill-card.md

## Description:

mx-financial-assistant provides Chinese-language financial Q&A, market data lookup, news and research retrieval, macroeconomic data tracking, asset screening, financial knowledge answers, and market analysis through Eastmoney's financial data service.

This skill is ready for commercial/non-commercial use.

## Publisher:

[financial-ai-analyst](https://clawhub.ai/user/financial-ai-analyst)

### License/Terms of Use:

MIT-0

## Use Case:

Investors, analysts, and financial research users use this skill to ask natural-language questions in Chinese about market data, company information, news, announcements, research reports, macroeconomic indicators, asset screening, and financial concepts. The skill returns a summarized answer with source references when available.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Broad activation wording may route ordinary or non-financial Chinese-language questions to Eastmoney's external API.

Mitigation: Review the skill before installation and narrow activation criteria for deployments where unrelated questions or sensitive prompts may be entered.

Risk: User questions and any included account numbers, private portfolio details, or confidential trading plans may be sent to the external API provider.

Mitigation: Avoid submitting sensitive financial or personal information unless the deployment owner has approved sharing it with the provider.

Risk: The skill depends on a local EM_API_KEY and an unpinned httpx dependency.

Mitigation: Store the API key securely, rotate it if exposed, and pin dependencies before production deployment.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/financial-ai-analyst/skills/mx-financial-assistant)
- [Publisher profile](https://clawhub.ai/user/financial-ai-analyst)
- [Eastmoney financial Q&A API endpoint](https://ai-saas.eastmoney.com/proxy/app-robo-advisor-api/assistant/ask)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [JSON containing a Markdown answer and reference metadata]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires EM_API_KEY; supports standard mode and deep-think mode; answers are intended to be presented in Chinese.]

## Skill Version(s):

1.0.2 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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