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BytesAgain › Skills › Data Analyst
🦀 ClawHub

Data Analyst

by @wuyandong8

Enterprise-grade data analysis assistant. Clean, analyze, and visualize data automatically. **Triggers when user mentions:** - Data cleaning: "数据清洗", "整理数据",...

Versionv1.0.2
Downloads602
#data#legal#automation
TERMINAL
clawhub install smart-data-insights

📖 About This Skill

name: data-analyst description: | Enterprise-grade data analysis assistant. Clean, analyze, and visualize data automatically. Triggers when user mentions: - Data cleaning: "数据清洗", "整理数据", "清理数据", "数据预处理" - Data analysis: "分析数据", "数据分析", "数据报表", "生成报告" - Visualization: "画图", "图表", "可视化", "生成图表" - Excel/CSV: "处理Excel", "分析CSV", "读取表格" - Insights: "数据洞察", "发现规律", "趋势分析" Supports Excel (.xlsx), CSV, JSON formats. Generates reports, charts, and insights. homepage: https://github.com/yourusername/data-analyst-skill metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["python3", "pip3"] }, "install": [ { "id": "pandas", "kind": "pip", "package": "pandas", "label": "Install pandas: pip3 install pandas", }, { "id": "openpyxl", "kind": "pip", "package": "openpyxl", "label": "Install openpyxl: pip3 install openpyxl", }, { "id": "matplotlib", "kind": "pip", "package": "matplotlib", "label": "Install matplotlib: pip3 install matplotlib", }, ], "installScripts": ["install.sh"], }, }

Data Analyst Skill

Automatically clean, analyze, and visualize enterprise data.

Features

| Feature | Description | Reference | |---------|-------------|-----------| | Data Cleaning | Remove duplicates, handle missing values, standardize formats | references/data_cleaning.md | | Data Analysis | Statistics, trends, correlations | references/data_analysis.md | | Visualization | Charts, graphs, dashboards | references/visualization.md | | Report Generation | Automated insights and recommendations | references/report_generation.md |

Quick Start

Step 1: Prepare Your Data

Place your data file (Excel/CSV/JSON) in a known location.

Step 2: Analyze Data

# Basic analysis
{baseDir}/tools/analyze.py data.csv
With specific options

{baseDir}/tools/analyze.py data.xlsx --clean --visualize --report

Step 3: Get Results

Output includes:

  • Cleaned data file
  • Analysis summary
  • Visualization charts
  • Insights report

    • Available Tools

    | Tool | Function | Input | Output | |------|----------|-------|--------| | analyze.py | Main analysis entry point | Data file | Summary + options | | clean.py | Data cleaning | Raw data | Clean data | | visualize.py | Generate charts | Data | PNG/PDF charts | | report.py | Generate reports | Analysis results | Markdown report |

    Usage Examples

    Example 1: Quick Analysis

    "帮我分析这个销售数据"

    # Place your file as sales_data.csv
{baseDir}/tools/analyze.py sales_data.csv

    Output: 

    ✅ Data loaded: 1,234 rows, 8 columns
📊 Summary statistics generated
📈 Visualization: sales_trend.png
💡 3 key insights found

    Example 2: Data Cleaning + Analysis

    "清洗并分析客户数据"

    {baseDir}/tools/analyze.py customer_data.xlsx --clean --visualize

    Example 3: Generate Full Report

    "生成完整的数据报告"

    {baseDir}/tools/analyze.py data.csv --report --output report.md

    Supported Formats

    | Format | Read | Write | Notes | |--------|------|-------|-------| | CSV | ✅ | ✅ | Universal format | | Excel (.xlsx) | ✅ | ✅ | Requires openpyxl | | JSON | ✅ | ✅ | Structured data | | TSV | ✅ | ✅ | Tab-separated |

    Output Files

    | File | Description | |------|-------------| | *_cleaned.csv | Cleaned data | | *_summary.txt | Statistical summary | | *_chart_*.png | Visualizations | | *_report.md | Full analysis report |

    Common Use Cases

    Business Analytics

  • Sales trend analysis
  • Customer segmentation
  • Revenue forecasting
  • Performance dashboards

    • Data Quality

  • Duplicate detection
  • Missing value handling
  • Format standardization
  • Anomaly detection

    • Reporting

  • Executive summaries
  • Department reports
  • Trend analysis
  • KPI tracking

    • Advanced Features

    Custom Analysis

    # Specific columns only
{baseDir}/tools/analyze.py data.csv --columns "sales,date,region"
    Time series analysis
    
{baseDir}/tools/analyze.py data.csv --timeseries --date-column "date"
    Group by category
    
{baseDir}/tools/analyze.py data.csv --group-by "region" --aggregate "sum,mean"

    Visualization Options

    # Chart types
{baseDir}/tools/visualize.py data.csv --type bar
{baseDir}/tools/visualize.py data.csv --type line
{baseDir}/tools/visualize.py data.csv --type scatter
{baseDir}/tools/visualize.py data.csv --type pie
    Styling
    
{baseDir}/tools/visualize.py data.csv --style professional
{baseDir}/tools/visualize.py data.csv --colors "blue,green,red"

    Setup

    # Install dependencies
pip3 install pandas openpyxl matplotlib seaborn
    Verify installation
    
python3 -c "import pandas, matplotlib; print('Dependencies OK')"

    Notes

  • ⚠️ Large files (>100MB) may take time to process
  • ⚠️ Excel files require openpyxl
  • ⚠️ Charts saved as PNG by default
  • ⚠️ All processing is local (no data sent externally)

    • Troubleshooting

    "Module not found" 

    pip3 install pandas openpyxl matplotlib

    "File encoding error"

  • Try converting to UTF-8 first
  • Or specify encoding: --encoding gbk

    • "Memory error with large files"

  • Process in chunks: --chunk-size 10000
  • Or sample data: --sample 0.1

    • 💡 Examples

    Step 1: Prepare Your Data

    Place your data file (Excel/CSV/JSON) in a known location.

    Step 2: Analyze Data

    # Basic analysis
{baseDir}/tools/analyze.py data.csv
    With specific options
    
{baseDir}/tools/analyze.py data.xlsx --clean --visualize --report

    Step 3: Get Results

    Output includes:

  • Cleaned data file
  • Analysis summary
  • Visualization charts
  • Insights report

    • ⚙️ Configuration

    # Install dependencies
pip3 install pandas openpyxl matplotlib seaborn
    Verify installation
    
python3 -c "import pandas, matplotlib; print('Dependencies OK')"

    📋 Tips & Best Practices

  • ⚠️ Large files (>100MB) may take time to process
  • ⚠️ Excel files require openpyxl
  • ⚠️ Charts saved as PNG by default
  • ⚠️ All processing is local (no data sent externally)