---

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."

---

---

✨ 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:

   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)

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:

# 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:

# 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

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)

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

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

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:

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:

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

# 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

---

OpenClaw instance (version >= 0.8.0)

Python 3.8+

Feishu integration enabled (optional, for Feishu data sources)

Install Steps

1. Download the skill package:

   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)

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

---