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/...

Security Scan

The skill's code, instructions, and requirements are coherent with its stated purpose (ECharts-based chart generation for Feishu); no obvious attempts to access unrelated secrets or exfiltrate data were found.

Purpose & Capability

Name/description (Feishu-oriented ECharts visualizations) align with included files: data parsing, chart JSON generation, headless rendering (pyppeteer) and auto-analysis. Required capabilities (pandas, openpyxl, pyppeteer, optional Feishu JSON inputs) are consistent with the described feature set (Excel/CSV parsing, screenshot rendering, Feishu card JSON).

Instruction Scope

SKILL.md and scripts instruct only on parsing local data or user-provided Feishu JSON, generating chart options, and rendering screenshots or card JSON. The runtime does fetch ECharts from a CDN for rendering and the headless browser downloads Chromium on first run — both are justified by the rendering requirements but are external network operations. The test script uses subprocess.run to exercise local commands; its commands are static in the repository. No instructions ask the agent to read unrelated system files, environment secrets, or to transmit data to unknown endpoints.

Install Mechanism

There is no formal install spec; the repo contains a requirements.txt listing pandas, openpyxl, requests, and pyppeteer. This is proportionate to the task, but pyppeteer will download a Chromium binary on first use (large download, network access). ECharts is loaded from jsdelivr CDN during rendering (external dependency). These network downloads are expected for local rendering but raise the usual risks of third-party resources (supply-chain/network availability).

Credentials

The skill does not request environment variables, API tokens, or config paths. Feishu integration is supported but implemented as accepting Feishu JSON inputs (bitable/sheet data) rather than requiring stored Feishu credentials — so there is no unexpected credential request in the package.

Persistence & Privilege

Skill flags are standard (not always: true). The package does not attempt to modify other skills or system-wide configs. It runs as a normal skill with no elevated persistent privileges.

Assessment

This skill appears to do what it claims (parse data, recommend charts, render ECharts to PNG or generate Feishu card JSON). Before installing, consider: 1) Network downloads: the first run will download Chromium (pyppeteer) and the page renderer loads ECharts from jsdelivr — this requires outbound network access and a ~100–200MB download. 2) Sandbox flags: the headless browser is launched with --no-sandbox (common for some containerized environments) — run the skill in a trusted or isolated environment if you are concerned. 3) Dependencies: install requirements in a virtualenv to avoid polluting system Python. 4) Running tests: test.py uses subprocess.run with shell=True to invoke the bundled scripts — avoid modifying those commands with untrusted input. 5) Feishu integration: interactive card mode only emits JSON; the skill does not itself send messages to Feishu or store tokens — you'll need to handle sending and permissions separately. If you need stronger assurance, review the remaining truncated files (sending code, network calls) or run the code in an isolated VM/container first.

<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>

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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:

   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

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⚡ 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

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📊 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

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📥 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

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📖 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

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🔧 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.

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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

# 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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<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>