data-analysis-for-feishu

📊 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.

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

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

---

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

---

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