Skill: zzzanezhou0829/data-analysis-for-feishu (version 1.0.0)

--- name: data-analysis-for-feishu description: "📊 Powerful ECharts-based data visualization skill optimized for Feishu (Lark) ecosystem. Supports 12+ chart types, 6+ data sources (Excel/CSV/Bitable/Sheet/Markdown), auto chart recommendation, auto analysis reports, generates high-definition PNG charts perfectly displayed in Feishu. No configuration required, works out of the box." --- <div align="center"> <h1>📊 Data Analysis for Feishu</h1> <p> <strong>Open-source data visualization skill for OpenClaw, built for Feishu ecosystem</strong> </p> <p> <a href="https://github.com/openclaw/skills"><img src="https://img.shields.io/badge/OpenClaw-Skill-blue.svg" alt="OpenClaw Skill"></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License MIT"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/Python-3.8+-yellow.svg" alt="Python 3.8+"></a> <img src="https://img.shields.io/badge/Feishu-5.15+-brightgreen.svg" alt="Feishu 5.15+"> </p> <p> <a href="#features">✨ Features</a> • <a href="#installation">🚀 Installation</a> • <a href="#quick-start">⚡ Quick Start</a> • <a href="#chart-types">📊 Chart Types</a> • <a href="#data-sources">📥 Data Sources</a> • <a href="#examples">📖 Examples</a> • <a href="#faq">❓ FAQ</a> • <a href="#contributing">🤝 Contributing</a> </p> </div> --- ## ✨ Features ### 📊 Rich Chart Support 12+ professional chart types cover 99% of data visualization scenarios: - **Basic**: Line, Area, Bar, Stacked Bar, Pie, Donut, Gauge, Radar - **Advanced**: Scatter (correlation analysis), Funnel (conversion analysis), Waterfall (financial analysis), Dual Axis (multi-metric comparison) - **Multi-series**: All charts support multiple data series comparison - **Customizable**: Support stacked mode, area fill, custom colors, etc. ### 🧠 AI-Powered Intelligence - **Auto Chart Recommendation**: Upload data, AI automatically analyzes characteristics and selects the optimal chart type - **Auto Data Cleaning**: Automatically handle null values, outliers, date/percent format conversion - **Auto Analysis Report**: Generate natural language analysis conclusions while generating charts (trends, extremes, proportions, etc.) - **Auto Title Generation**: No need to manually enter titles, automatically generate appropriate titles based on data ### 📥 Multiple Data Sources No manual data conversion required, support 6+ common data sources: - Local files: Excel (.xlsx/.xls), CSV/TSV - Feishu ecosystem: Bitable (multi-dimensional table), Sheet (spreadsheet) - Text formats: Markdown tables, raw JSON/2D arrays, pasted table text ### 🖼️ Perfect Feishu Compatibility - **Ultra HD Output**: 2x Retina DPI rendering, 1200x750 default resolution, sharp text and lines - **Precise Cropping**: Automatically capture only the chart area, no extra whitespace - **Feishu Optimized**: Perfect display in Feishu conversations, documents, and wiki pages - **Dual Mode Support**: - ✅ Screenshot mode: 100% compatible with all Feishu versions, no permissions required - ✅ Interactive card mode: Support hover to view values, toggle series (requires Feishu ECharts component permission) ### ⚡ Excellent Experience - **Zero Configuration**: Works out of the box, dependencies automatically installed on first run - **Fast Generation**: First run ~10s (download browser), subsequent generation only takes 1-3 seconds - **User-friendly**: Clear error prompts, perfect log output, easy to troubleshoot - **Exportable**: Support export analysis conclusions as separate text files, easy to copy and use --- ## 🚀 Installation ### Prerequisites - OpenClaw instance (version >= 0.8.0) - Python 3.8+ - Feishu integration enabled (optional, for Feishu data sources) ### Install Steps 1. **Download the skill package**: ```bash wget https://github.com/openclaw/skills/releases/download/data-analysis-for-feishu-v1.0.0/data-analysis-for-feishu.skill ``` 2. **Install in OpenClaw**: Go to OpenClaw Admin → Skills → Install → Upload the `.skill` file 3. **Done!** Dependencies are automatically installed on first use. ### Manual Installation (for developers) ```bash cd /path/to/openclaw/skills git clone https://github.com/openclaw/data-analysis-for-feishu.git cd data-analysis-for-feishu pip install -r requirements.txt ``` --- ## ⚡ Quick Start ### 1-Minute Test Run Generate your first chart in 1 minute: ```bash # Go to skill directory cd skills/data-analysis-for-feishu # Generate a demo funnel chart python scripts/main.py \ --type funnel \ --title "User Conversion Funnel" \ --labels "Visit" "Register" "Add to Cart" "Purchase" "Repurchase" \ --values 10000 4500 2200 1200 500 \ --output demo_funnel.png ``` You will get a high-definition funnel chart and automatic analysis report. ### Auto Mode (Recommended) Let AI do all the work, just provide data: ```bash # Auto analyze Excel data, recommend chart type, generate chart + analysis python scripts/main.py \ --excel your_data.xlsx \ --output result.png \ --analysis-output analysis.txt ``` --- ## 📊 Chart Types | Chart Type | Best For | Example | |------------|----------|---------| | Line Chart | Time series trend analysis | Daily sales trends for the past month | | Area Chart | Multi-series trend comparison | 2023 vs 2024 monthly sales comparison | | Bar Chart | Category comparison/ranking | Sales ranking by region | | Stacked Bar Chart | Multi-dimensional proportion | Product category composition in each region | | Pie Chart | Proportion/distribution | Revenue composition of each business line | | Donut Chart | Ring-style proportion | Market share of each competitor | | Gauge Chart | Progress/KPI completion | Annual sales target completion rate | | Radar Chart | Multi-dimensional comparison | Product capability assessment | | Scatter Chart | Correlation analysis | Correlation between advertising spend and sales | | Funnel Chart | Conversion analysis | User conversion from visit to purchase | | Waterfall Chart | Financial change analysis | Monthly profit and loss changes | | Dual Axis Chart | Multi-metric comparison | Monthly sales and growth rate | --- ## 📥 Data Sources | Data Source | Usage | |-------------|-------| | Excel (.xlsx/.xls) | `--excel data.xlsx --sheet Sheet1` | | CSV/TSV | `--csv data.csv` | | Feishu Bitable | `--bitable-records '[{"fields": {...}}]'` | | Feishu Sheet | `--sheet-data '[["Header1", "Header2"], ["val1", "val2"]]'` | | Markdown Table | `--markdown-table "| Col1 | Col2 |\n|---|---|\n| a | 1 |"` | | Raw Data | `--x-axis "Jan" "Feb" --y-axis 100 200` | --- ## 📖 Usage Examples ### Example 1: Multi-series Area Chart ```bash python scripts/main.py \ --type area \ --title "2023 vs 2024 Sales Trend" \ --excel sales_comparison.xlsx \ --x-axis-field "Month" \ --y-axis-field "2023 Sales,2024 Sales" \ --series-names "2023,2024" \ --output sales_trend.png ``` ### Example 2: Dual Axis Chart (Sales + Growth Rate) ```bash python scripts/main.py \ --type dual_axis \ --title "Monthly Performance" \ --x-axis "Jan" "Feb" "Mar" "Apr" "May" "Jun" \ --y1-axis 120 150 135 180 210 240 \ --y1-name "Sales (k)" \ --y2-axis 0 25 -10 33.3 16.7 14.3 \ --y2-name "Growth Rate (%)" \ --output performance.png ``` ### Example 3: Waterfall Chart for Financial Analysis ```bash python scripts/main.py \ --type waterfall \ --title "Monthly Profit Breakdown" \ --x-axis "Initial Revenue" "Cost of Goods" "Operating Expenses" "Tax" "Net Profit" \ --y-axis 1000 -300 -200 -150 350 \ --y-name "Amount (k)" \ --output profit_waterfall.png ``` ### Example 4: Generate from Markdown Table ```bash python scripts/main.py \ --type bar \ --title "Quarterly Revenue" \ --markdown-table "| Quarter | Revenue | Profit | |----|----|----| | Q1 | 1200 | 240 | | Q2 | 1500 | 375 | | Q3 | 1350 | 297 | | Q4 | 1800 | 540 |" \ --x-axis-field "Quarter" \ --y-axis-field "Revenue,Profit" \ --output quarterly.png ``` --- ## 🔧 Configuration ### Custom Color Scheme Edit `DEFAULT_COLORS` in `scripts/generate_echarts_screenshot.py` to use your brand colors: ```python DEFAULT_COLORS = ["#YOUR_COLOR1", "#YOUR_COLOR2", ...] ``` ### Custom Default Resolution Change default `width`/`height` in `scripts/main.py` to adjust output size. ### Enable Interactive Card Mode When you have Feishu ECharts component permission, use: ```bash python scripts/generate_echarts_card.py --type line --title "Demo" --x-axis "A" "B" --y-axis 1 2 --output card.json ``` Then send the JSON as Feishu card. --- ## ❓ FAQ ### Q: Why is the picture blank when I first run it? A: First run automatically downloads Chromium browser (about 180MB), please wait patiently. Subsequent runs will be very fast. ### Q: Can I use this without Feishu? A: Yes! You can generate charts as local PNG files for any usage scenario, Feishu integration is optional. ### Q: How to apply for Feishu ECharts component permission? A: Go to Feishu Open Platform → Your App → Permissions → Search for "Message Card - Use ECharts Chart Component" → Apply for permission. It's free and usually approved within 1 working day. ### Q: Does it support Chinese data? A: Perfect support! All components use UTF-8 encoding, Chinese labels, titles, and analysis reports are displayed normally. ### Q: Can I add custom chart types? A: Yes! Just add the chart configuration in `scripts/generate_echarts_screenshot.py`, following the existing pattern. --- ## 🤝 Contributing Contributions are welcome! You can contribute in the following ways: - 🐛 Report bugs and issues - ✨ Propose new feature ideas - 📝 Improve documentation - 🔧 Add new chart types or data sources - 🌐 Add multi-language support ### Development Setup ```bash # Fork and clone the repo git clone https://github.com/your-username/data-analysis-for-feishu.git cd data-analysis-for-feishu # Install dependencies pip install -r requirements.txt # Run tests python scripts/main.py --type funnel --title "Test" --labels "A" "B" "C" --values 100 50 20 --output test.png ``` ### Submitting PR 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request --- ## 📄 License Distributed under the MIT License. See `LICENSE` file for more information. --- ## 🙏 Acknowledgments - [ECharts](https://echarts.apache.org/) - Powerful open-source chart library - [Pyppeteer](https://pyppeteer.github.io/pyppeteer/) - Headless browser for Python - [OpenClaw](https://openclaw.ai/) - Extensible AI agent platform - [Feishu Open Platform](https://open.feishu.cn/) - Feishu API and documentation --- <div align="center"> <strong>If this skill helps you, please give it a ⭐ on GitHub!</strong> <br> <a href="https://github.com/openclaw/data-analysis-for-feishu/stargazers"><img src="https://img.shields.io/github/stars/openclaw/data-analysis-for-feishu.svg?style=social" alt="GitHub stars"></a> </div>


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)