Skill: wuyandong8/smart-data-insights (version 1.0.2)

--- 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 ```bash # 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"** ```bash # 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"** ```bash {baseDir}/tools/analyze.py customer_data.xlsx --clean --visualize ``` ### Example 3: Generate Full Report **"生成完整的数据报告"** ```bash {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 ```bash # 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 ```bash # 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 ```bash # 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"** ```bash 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`


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