stock-daily-analysis▌
chjm-ai/stock-daily-analysis-skill · updated Apr 20, 2026
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LLM-powered daily stock analysis across A-shares, Hong Kong, and US markets with technical indicators and AI-driven signals.
- ›Analyzes multiple markets (A-shares, Hong Kong, US stocks) with technical indicators including moving averages, MACD, RSI, and bias rate
- ›Generates trend status, buy signal scores, and AI-driven operation advice with target prices and stop-loss levels
- ›Supports batch analysis of multiple stocks and optional integration with market-data skill for enhanced data sta
Daily Stock Analysis for OpenClaw
基于 LLM 的 A/H/美股智能分析 Skill,提供技术面分析和 AI 决策建议。
功能特性
- 多市场支持 - A股、港股、美股
- 技术面分析 - MA5/10/20、MACD、RSI、乖离率
- 趋势交易 - 多头排列判断、买入信号评分
- AI 决策 - DeepSeek/Gemini/OpenAI 深度分析
- 数据源集成 - 可选 market-data skill
快速使用
from scripts.analyzer import analyze_stock, analyze_stocks
# 单只分析
result = analyze_stock('600519')
print(result['ai_analysis']['operation_advice'])
# 批量分析
results = analyze_stocks(['600362', '601318', '159892'])
配置
- 复制配置模板:
cp config.example.json config.json
- 填入 DeepSeek API Key:
{
"ai": {
"provider": "openai",
"api_key": "sk-your-deepseek-key",
"base_url": "https://api.deepseek.com/v1",
"model": "deepseek-chat"
}
}
- (可选) 启用 market-data skill 数据源:
{
"data": {
"use_market_data_skill": true,
"market_data_skill_path": "../market-data"
}
}
返回数据
{
'code': '600519',
'name': '贵州茅台',
'technical_indicators': {
'trend_status': '强势多头',
'ma5': 1500.0, 'ma10': 1480.0, 'ma20': 1450.0,
'bias_ma5': 2.5,
'macd_status': '金叉',
'rsi_status': '强势买入',
'buy_signal': '买入',
'signal_score': 75
},
'ai_analysis': {
'sentiment_score': 75,
'operation_advice': '买入',
'confidence_level': '高',
'target_price': '1550',
'stop_loss': '1420'
}
}
项目信息
- 开源协议: MIT
- 项目地址: https://github.com/yourusername/stock-daily-analysis
- 原项目: https://github.com/ZhuLinsen/daily_stock_analysis
⚠️ 免责声明: 本项目仅供学习研究,不构成投资建议。股市有风险,投资需谨慎。
How to use stock-daily-analysis 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 stock-daily-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches stock-daily-analysis from GitHub repository chjm-ai/stock-daily-analysis-skill 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 stock-daily-analysis. Access the skill through slash commands (e.g., /stock-daily-analysis) 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
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★73 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Keeps context tight: stock-daily-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Nikhil Ndlovu· Dec 28, 2024
stock-daily-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Amelia Smith· Dec 28, 2024
I recommend stock-daily-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Pratham Ware· Dec 24, 2024
Useful defaults in stock-daily-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Isabella Sharma· Dec 24, 2024
stock-daily-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sofia Brown· Dec 16, 2024
Useful defaults in stock-daily-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Thompson· Dec 8, 2024
stock-daily-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Amelia Thompson· Dec 4, 2024
I recommend stock-daily-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Dev Diallo· Dec 4, 2024
Keeps context tight: stock-daily-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amina Perez· Nov 27, 2024
stock-daily-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
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