{"skill":{"slug":"data-analysis-for-feishu","displayName":"data-analysis-for-feishu","summary":"📊 Powerful ECharts-based data visualization skill optimized for Feishu (Lark) ecosystem. Supports 12+ chart types, 6+ data sources (Excel/CSV/Bitable/Sheet/...","description":"---\nname: data-analysis-for-feishu\ndescription: \"📊 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.\"\n---\n\n<div align=\"center\">\n  <h1>📊 Data Analysis for Feishu</h1>\n  <p>\n    <strong>Open-source data visualization skill for OpenClaw, built for Feishu ecosystem</strong>\n  </p>\n  <p>\n    <a href=\"https://github.com/openclaw/skills\"><img src=\"https://img.shields.io/badge/OpenClaw-Skill-blue.svg\" alt=\"OpenClaw Skill\"></a>\n    <a href=\"https://opensource.org/licenses/MIT\"><img src=\"https://img.shields.io/badge/License-MIT-green.svg\" alt=\"License MIT\"></a>\n    <a href=\"https://www.python.org/downloads/\"><img src=\"https://img.shields.io/badge/Python-3.8+-yellow.svg\" alt=\"Python 3.8+\"></a>\n    <img src=\"https://img.shields.io/badge/Feishu-5.15+-brightgreen.svg\" alt=\"Feishu 5.15+\">\n  </p>\n  <p>\n    <a href=\"#features\">✨ Features</a> •\n    <a href=\"#installation\">🚀 Installation</a> •\n    <a href=\"#quick-start\">⚡ Quick Start</a> •\n    <a href=\"#chart-types\">📊 Chart Types</a> •\n    <a href=\"#data-sources\">📥 Data Sources</a> •\n    <a href=\"#examples\">📖 Examples</a> •\n    <a href=\"#faq\">❓ FAQ</a> •\n    <a href=\"#contributing\">🤝 Contributing</a>\n  </p>\n</div>\n\n---\n\n## ✨ Features\n### 📊 Rich Chart Support\n12+ professional chart types cover 99% of data visualization scenarios:\n- **Basic**: Line, Area, Bar, Stacked Bar, Pie, Donut, Gauge, Radar\n- **Advanced**: Scatter (correlation analysis), Funnel (conversion analysis), Waterfall (financial analysis), Dual Axis (multi-metric comparison)\n- **Multi-series**: All charts support multiple data series comparison\n- **Customizable**: Support stacked mode, area fill, custom colors, etc.\n\n### 🧠 AI-Powered Intelligence\n- **Auto Chart Recommendation**: Upload data, AI automatically analyzes characteristics and selects the optimal chart type\n- **Auto Data Cleaning**: Automatically handle null values, outliers, date/percent format conversion\n- **Auto Analysis Report**: Generate natural language analysis conclusions while generating charts (trends, extremes, proportions, etc.)\n- **Auto Title Generation**: No need to manually enter titles, automatically generate appropriate titles based on data\n\n### 📥 Multiple Data Sources\nNo manual data conversion required, support 6+ common data sources:\n- Local files: Excel (.xlsx/.xls), CSV/TSV\n- Feishu ecosystem: Bitable (multi-dimensional table), Sheet (spreadsheet)\n- Text formats: Markdown tables, raw JSON/2D arrays, pasted table text\n\n### 🖼️ Perfect Feishu Compatibility\n- **Ultra HD Output**: 2x Retina DPI rendering, 1200x750 default resolution, sharp text and lines\n- **Precise Cropping**: Automatically capture only the chart area, no extra whitespace\n- **Feishu Optimized**: Perfect display in Feishu conversations, documents, and wiki pages\n- **Dual Mode Support**: \n  - ✅ Screenshot mode: 100% compatible with all Feishu versions, no permissions required\n  - ✅ Interactive card mode: Support hover to view values, toggle series (requires Feishu ECharts component permission)\n\n### ⚡ Excellent Experience\n- **Zero Configuration**: Works out of the box, dependencies automatically installed on first run\n- **Fast Generation**: First run ~10s (download browser), subsequent generation only takes 1-3 seconds\n- **User-friendly**: Clear error prompts, perfect log output, easy to troubleshoot\n- **Exportable**: Support export analysis conclusions as separate text files, easy to copy and use\n\n---\n\n## 🚀 Installation\n### Prerequisites\n- OpenClaw instance (version >= 0.8.0)\n- Python 3.8+\n- Feishu integration enabled (optional, for Feishu data sources)\n\n### Install Steps\n1. **Download the skill package**:\n   ```bash\n   wget https://github.com/openclaw/skills/releases/download/data-analysis-for-feishu-v1.0.0/data-analysis-for-feishu.skill\n   ```\n   \n2. **Install in OpenClaw**:\n   Go to OpenClaw Admin → Skills → Install → Upload the `.skill` file\n\n3. **Done!** Dependencies are automatically installed on first use.\n\n### Manual Installation (for developers)\n```bash\ncd /path/to/openclaw/skills\ngit clone https://github.com/openclaw/data-analysis-for-feishu.git\ncd data-analysis-for-feishu\npip install -r requirements.txt\n```\n\n---\n\n## ⚡ Quick Start\n### 1-Minute Test Run\nGenerate your first chart in 1 minute:\n```bash\n# Go to skill directory\ncd skills/data-analysis-for-feishu\n\n# Generate a demo funnel chart\npython scripts/main.py \\\n  --type funnel \\\n  --title \"User Conversion Funnel\" \\\n  --labels \"Visit\" \"Register\" \"Add to Cart\" \"Purchase\" \"Repurchase\" \\\n  --values 10000 4500 2200 1200 500 \\\n  --output demo_funnel.png\n```\nYou will get a high-definition funnel chart and automatic analysis report.\n\n### Auto Mode (Recommended)\nLet AI do all the work, just provide data:\n```bash\n# Auto analyze Excel data, recommend chart type, generate chart + analysis\npython scripts/main.py \\\n  --excel your_data.xlsx \\\n  --output result.png \\\n  --analysis-output analysis.txt\n```\n\n---\n\n## 📊 Chart Types\n| Chart Type | Best For | Example |\n|------------|----------|---------|\n| Line Chart | Time series trend analysis | Daily sales trends for the past month |\n| Area Chart | Multi-series trend comparison | 2023 vs 2024 monthly sales comparison |\n| Bar Chart | Category comparison/ranking | Sales ranking by region |\n| Stacked Bar Chart | Multi-dimensional proportion | Product category composition in each region |\n| Pie Chart | Proportion/distribution | Revenue composition of each business line |\n| Donut Chart | Ring-style proportion | Market share of each competitor |\n| Gauge Chart | Progress/KPI completion | Annual sales target completion rate |\n| Radar Chart | Multi-dimensional comparison | Product capability assessment |\n| Scatter Chart | Correlation analysis | Correlation between advertising spend and sales |\n| Funnel Chart | Conversion analysis | User conversion from visit to purchase |\n| Waterfall Chart | Financial change analysis | Monthly profit and loss changes |\n| Dual Axis Chart | Multi-metric comparison | Monthly sales and growth rate |\n\n---\n\n## 📥 Data Sources\n| Data Source | Usage |\n|-------------|-------|\n| Excel (.xlsx/.xls) | `--excel data.xlsx --sheet Sheet1` |\n| CSV/TSV | `--csv data.csv` |\n| Feishu Bitable | `--bitable-records '[{\"fields\": {...}}]'` |\n| Feishu Sheet | `--sheet-data '[[\"Header1\", \"Header2\"], [\"val1\", \"val2\"]]'` |\n| Markdown Table | `--markdown-table \"| Col1 | Col2 |\\n|---|---|\\n| a | 1 |\"` |\n| Raw Data | `--x-axis \"Jan\" \"Feb\" --y-axis 100 200` |\n\n---\n\n## 📖 Usage Examples\n### Example 1: Multi-series Area Chart\n```bash\npython scripts/main.py \\\n  --type area \\\n  --title \"2023 vs 2024 Sales Trend\" \\\n  --excel sales_comparison.xlsx \\\n  --x-axis-field \"Month\" \\\n  --y-axis-field \"2023 Sales,2024 Sales\" \\\n  --series-names \"2023,2024\" \\\n  --output sales_trend.png\n```\n\n### Example 2: Dual Axis Chart (Sales + Growth Rate)\n```bash\npython scripts/main.py \\\n  --type dual_axis \\\n  --title \"Monthly Performance\" \\\n  --x-axis \"Jan\" \"Feb\" \"Mar\" \"Apr\" \"May\" \"Jun\" \\\n  --y1-axis 120 150 135 180 210 240 \\\n  --y1-name \"Sales (k)\" \\\n  --y2-axis 0 25 -10 33.3 16.7 14.3 \\\n  --y2-name \"Growth Rate (%)\" \\\n  --output performance.png\n```\n\n### Example 3: Waterfall Chart for Financial Analysis\n```bash\npython scripts/main.py \\\n  --type waterfall \\\n  --title \"Monthly Profit Breakdown\" \\\n  --x-axis \"Initial Revenue\" \"Cost of Goods\" \"Operating Expenses\" \"Tax\" \"Net Profit\" \\\n  --y-axis 1000 -300 -200 -150 350 \\\n  --y-name \"Amount (k)\" \\\n  --output profit_waterfall.png\n```\n\n### Example 4: Generate from Markdown Table\n```bash\npython scripts/main.py \\\n  --type bar \\\n  --title \"Quarterly Revenue\" \\\n  --markdown-table \"| Quarter | Revenue | Profit |\n|----|----|----|\n| Q1 | 1200 | 240 |\n| Q2 | 1500 | 375 |\n| Q3 | 1350 | 297 |\n| Q4 | 1800 | 540 |\" \\\n  --x-axis-field \"Quarter\" \\\n  --y-axis-field \"Revenue,Profit\" \\\n  --output quarterly.png\n```\n\n---\n\n## 🔧 Configuration\n### Custom Color Scheme\nEdit `DEFAULT_COLORS` in `scripts/generate_echarts_screenshot.py` to use your brand colors:\n```python\nDEFAULT_COLORS = [\"#YOUR_COLOR1\", \"#YOUR_COLOR2\", ...]\n```\n\n### Custom Default Resolution\nChange default `width`/`height` in `scripts/main.py` to adjust output size.\n\n### Enable Interactive Card Mode\nWhen you have Feishu ECharts component permission, use:\n```bash\npython scripts/generate_echarts_card.py --type line --title \"Demo\" --x-axis \"A\" \"B\" --y-axis 1 2 --output card.json\n```\nThen send the JSON as Feishu card.\n\n---\n\n## ❓ FAQ\n### Q: Why is the picture blank when I first run it?\nA: First run automatically downloads Chromium browser (about 180MB), please wait patiently. Subsequent runs will be very fast.\n\n### Q: Can I use this without Feishu?\nA: Yes! You can generate charts as local PNG files for any usage scenario, Feishu integration is optional.\n\n### Q: How to apply for Feishu ECharts component permission?\nA: 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.\n\n### Q: Does it support Chinese data?\nA: Perfect support! All components use UTF-8 encoding, Chinese labels, titles, and analysis reports are displayed normally.\n\n### Q: Can I add custom chart types?\nA: Yes! Just add the chart configuration in `scripts/generate_echarts_screenshot.py`, following the existing pattern.\n\n---\n\n## 🤝 Contributing\nContributions are welcome! You can contribute in the following ways:\n- 🐛 Report bugs and issues\n- ✨ Propose new feature ideas\n- 📝 Improve documentation\n- 🔧 Add new chart types or data sources\n- 🌐 Add multi-language support\n\n### Development Setup\n```bash\n# Fork and clone the repo\ngit clone https://github.com/your-username/data-analysis-for-feishu.git\ncd data-analysis-for-feishu\n\n# Install dependencies\npip install -r requirements.txt\n\n# Run tests\npython scripts/main.py --type funnel --title \"Test\" --labels \"A\" \"B\" \"C\" --values 100 50 20 --output test.png\n```\n\n### Submitting PR\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n---\n\n## 📄 License\nDistributed under the MIT License. See `LICENSE` file for more information.\n\n---\n\n## 🙏 Acknowledgments\n- [ECharts](https://echarts.apache.org/) - Powerful open-source chart library\n- [Pyppeteer](https://pyppeteer.github.io/pyppeteer/) - Headless browser for Python\n- [OpenClaw](https://openclaw.ai/) - Extensible AI agent platform\n- [Feishu Open Platform](https://open.feishu.cn/) - Feishu API and documentation\n\n---\n\n<div align=\"center\">\n  <strong>If this skill helps you, please give it a ⭐ on GitHub!</strong>\n  <br>\n  <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>\n</div>\n","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":793,"installsAllTime":0,"installsCurrent":0,"stars":66,"versions":1},"createdAt":1775715272182,"updatedAt":1778492492156},"latestVersion":{"version":"1.0.0","createdAt":1775715272182,"changelog":"data-analysis-for-feishu 1.0.0\n\n- Initial release of a powerful ECharts-based data visualization skill for the Feishu (Lark) ecosystem.\n- Supports 12+ chart types including line, area, bar, pie, gauge, radar, scatter, funnel, waterfall, dual axis, and more.\n- Integrates with 6+ data sources: Excel, CSV, Feishu Bitable, Feishu Sheet, Markdown tables, and raw/pasted data.\n- Features auto chart recommendation, automatic data cleaning, analysis report generation, and auto title creation.\n- Produces high-definition PNG charts optimized for perfect display in Feishu, with zero configuration required.\n- Offers both screenshot and interactive card modes for full Feishu version compatibility.","license":"MIT-0"},"metadata":null,"owner":{"handle":"zzzanezhou0829","userId":"s176b8e927ekwcm13wsyy7txwd83qvz5","displayName":"zane iris zhou","image":"https://avatars.githubusercontent.com/u/265173160?v=4"},"moderation":{"isSuspicious":false,"isMalwareBlocked":false,"verdict":"clean","reasonCodes":["review.llm_review"],"summary":"Review: review.llm_review","engineVersion":"v2.4.24","updatedAt":1779977188666}}