eastmoney_financial_data▌
meission/eastmoney · updated Apr 8, 2026
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通过文本输入查询金融相关数据(股票、板块、指数等),接口返回 JSON格式内容。
东方财富金融数据skill (eastmoney_financial_data)
通过文本输入查询金融相关数据(股票、板块、指数等),接口返回 JSON格式内容。
使用方式
-
首先检查环境变量
EASTMONEY_APIKEY是否存在:echo $EASTMONEY_APIKEY如果不存在,提示用户在东方财富Skills页面(https://marketing.dfcfs.com/views/finskillshub/indexuNdYscEA?appfenxiang=1)获取apikey并设置到环境变量。
⚠️ 安全注意事项
- 外部请求: 本 Skill 会将您的查询文本发送至东方财富官方 API 域名 (
mkapi2.dfcfs.com) 以获取金融数据。 - 凭据保护: API Key 仅通过环境变量
EASTMONEY_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。
- 外部请求: 本 Skill 会将您的查询文本发送至东方财富官方 API 域名 (
-
使用POST请求调用接口:
curl -X POST --location 'https://mkapi2.dfcfs.com/finskillshub/api/claw/query' \ --header 'Content-Type: application/json' \ --header "apikey: $EASTMONEY_APIKEY" \ --data '{"toolQuery":"用户的查询内容"}'
适用场景
当用户查询以下类型的内容时使用本skill:
- 行情类数据:股票、行业、板块、指数、基金、债券的实时行情、主力资金流向、估值等数据
- 财务类数据:上市公司与非上市公司的基本信息、财务指标、高管信息、主营业务、股东结构、融资情况等数据
- 关系与经营类数据:股票、非上市公司、股东及高管之间的关联关系数据,以及企业经营相关数据
数据限制说明
请谨慎查询大数据范围的数据,如某只股票3年的每日最新价,可能会导致返回内容过多,模型上下文爆炸问题。
返回结构说明
一级核心路径:data
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
data.questionId |
字符串 | 查数请求唯一标识 ID,关联单次查询任务 |
data.dataTableDTOList |
数组 | 【核心】标准化后的证券指标数据列表,每个元素对应1 个证券 + 1 个指标的完整数据 |
data.rawDataTableDTOList |
数组 | 原始未加工的证券指标数据列表,与标准化列表结构完全一致,供原始数据调用 |
data.condition |
对象 | 本次查数的查询条件,记录查询关键词、时间范围等 |
data.entityTagDTOList |
数组 | 本次查询关联的证券主体汇总信息,去重后展示所有涉事证券的基础属性 |
二级核心路径:data.dataTableDTOList[](单指标对象,表格核心)
数组内每个对象为独立的指标数据单元,包含证券信息 + 表格数据 + 指标元信息 + 证券标签四大部分。
2.1 证券基础信息
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
dataTableDTOList[].code |
字符串 | 证券完整代码(含市场标识,如 300059.SZ) |
dataTableDTOList[].entityName |
字符串 | 证券全称(含代码,如东方财富 (300059.SZ)) |
dataTableDTOList[].title |
字符串 | 本指标数据的标题,概括查询结果(如东方财富最新价) |
2.2 表格数据核心(渲染用)
| 字段路径 | 类型 | 核心释义 | 表格逻辑 |
|---|---|---|---|
dataTableDTOList[].table |
对象 | 【核心】标准化表格数据,键 = 指标编码,值 = 指标数值数组;headName为时间 / 维度列值 |
键为指标列,headName为时间列,值为交叉单元格的指标数值 |
dataTableDTOList[].rawTable |
对象 | 原始表格数据,与table结构一致,未做数据标准化处理 |
同table,为原始数值,无格式 / 单位修正 |
dataTableDTOList[].nameMap |
对象 | 【核心】列名映射关系,将指标编码 / 内置字段转为业务中文名(如 f2→最新价) | 解决表格列名 “编码转中文” 的问题,headNameSub为时间列的固定名称 |
dataTableDTOList[].indicatorOrder |
数组 | 指标列的展示排序,元素为指标编码(如 [f2]) | 控制表格中多个指标列的前后顺序,单指标时为单元素数组 |
2.3 指标元信息(属性 / 规则)
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
dataTableDTOList[].dataType |
字符串 | 数据来源类型(如行情数据 / 数据浏览器) |
dataTableDTOList[].dataTypeEnum |
字符串 | 数据类型枚举值(HQ = 行情,DATA_BROWSER = 数据浏览器) |
dataTableDTOList[].field |
对象 | 【核心】当前指标的详细元信息,含指标编码、名称、查询时间、粒度等 |
2.4 证券标签信息(主体属性)
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
dataTableDTOList[].entityTagDTO |
对象 | 本指标关联证券的详细主体属性(如证券类型、市场、简称等) |
三级核心路径
3.1 指标元信息:dataTableDTOList[].field
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
field.returnCode |
字符串 | 指标唯一编码 |
field.returnName |
字符串 | 指标业务中文名(如最新价 / 收盘价) |
field.startDate/endDate |
字符串 | 本次查询的时间范围(开始 / 结束) |
field.dateGranularity |
字符串 | 数据粒度(DAY = 日度,MIN = 分钟等) |
3.2 证券主体属性:dataTableDTOList[].entityTagDTO
| 字段路径 | 类型 | 核心释义 |
|---|---|---|
entityTagDTO.secuCode |
字符串 | 证券纯代码(无市场标识,如 300059) |
entityTagDTO.marketChar |
字符串 | 市场标识(.SZ = 深交所,.SH = 上交所) |
entityTagDTO.entityTypeName |
字符串 | 证券类型(如 A 股 / 港股 / 债券) |
entityTagDTO.fullName |
字符串 | 证券完整中文名(如东方财富) |
示例
import os
import requests
api_key = os.getenv("EASTMONEY_APIKEY")
if not api_key:
raise ValueError("请先设置EASTMONEY_APIKEY环境变量")
url = "https://mkapi2.dfcfs.com/finskillshub/api/claw/query"
headers = {
"Content-Type": "application/json",
"apikey": api_key
}
data = {
"toolQuery": "东方财富最新价"
}
response = requests.post(url, headers=headers, json=data)
response.raise_for_status()
result = response.json()
print(result)
异常处理
- 如果数据结果为空,提示用户到东方财富妙想AI查询
- 如果请求失败,检查API Key是否正确,网络是否正常
How to use eastmoney_financial_data on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add eastmoney_financial_data
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches eastmoney_financial_data from GitHub repository meission/eastmoney and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate eastmoney_financial_data. Access the skill through slash commands (e.g., /eastmoney_financial_data) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★28 reviews- ★★★★★Arya Wang· Dec 28, 2024
Useful defaults in eastmoney_financial_data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Shikha Mishra· Dec 12, 2024
We added eastmoney_financial_data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ren Sharma· Nov 19, 2024
We added eastmoney_financial_data from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Nov 3, 2024
Useful defaults in eastmoney_financial_data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Pratham Ware· Oct 22, 2024
Registry listing for eastmoney_financial_data matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ren Flores· Oct 10, 2024
eastmoney_financial_data reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hana Singh· Sep 21, 2024
Useful defaults in eastmoney_financial_data — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Valentina Smith· Sep 17, 2024
eastmoney_financial_data has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Sep 1, 2024
Solid pick for teams standardizing on skills: eastmoney_financial_data is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Aug 20, 2024
eastmoney_financial_data is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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