📊 Data Analysis for Feishu#

Open-source data visualization skill for OpenClaw, built for Feishu ecosystem

✨ Features • 🚀 Installation • ⚡ Quick Start • 📊 Chart Types • 📥 Data Sources • 📖 Examples • ❓ FAQ • 🤝 Contributing

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✨ 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

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🚀 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

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⚡ 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

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📊 Chart Types#

Chart TypeBest ForExampleLine ChartTime series trend analysisDaily sales trends for the past monthArea ChartMulti-series trend comparison2023 vs 2024 monthly sales comparisonBar ChartCategory comparison/rankingSales ranking by regionStacked Bar ChartMulti-dimensional proportionProduct category composition in each regionPie ChartProportion/distributionRevenue composition of each business lineDonut ChartRing-style proportionMarket share of each competitorGauge ChartProgress/KPI completionAnnual sales target completion rateRadar ChartMulti-dimensional comparisonProduct capability assessmentScatter ChartCorrelation analysisCorrelation between advertising spend and salesFunnel ChartConversion analysisUser conversion from visit to purchaseWaterfall ChartFinancial change analysisMonthly profit and loss changesDual Axis ChartMulti-metric comparisonMonthly sales and growth rate

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📥 Data Sources#

Data SourceUsageExcel (.xlsx/.xls)--excel data.xlsx --sheet Sheet1CSV/TSV--csv data.csvFeishu Bitable--bitable-records '[{"fields": {...}}]'Feishu Sheet--sheet-data '[["Header1", "Header2"], ["val1", "val2"]]'Markdown Table`--markdown-table "Raw Data--x-axis "Jan" "Feb" --y-axis 100 200

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📖 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

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🔧 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.

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❓ 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.

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🤝 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

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📄 License#

Distributed under the MIT License. See LICENSE file for more information.

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🙏 Acknowledgments#

• ECharts - Powerful open-source chart library
• Pyppeteer - Headless browser for Python
• OpenClaw - Extensible AI agent platform
• Feishu Open Platform - Feishu API and documentation

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If this skill helps you, please give it a ⭐ on GitHub!