Data Analyzer

Overview

Professional data analysis skill for OpenClaw. Analyze CSV, Excel, and JSON data with statistical functions, visualizations, and automated report generation.

Features

• CSV/Excel/JSON data processing
• Basic statistical analysis
• HTML report generation
• Group-by analysis
• E-commerce data support

Quick Start

Analyze Data

python scripts/analyze_data.py \
  --input sales.csv \
  --output report.html \
  --group-by date

Generate JSON Summary

python scripts/analyze_data.py \
  --input orders.json \
  --output summary.json

Scripts

analyze_data.py

Analyze CSV/Excel/JSON data and generate reports.

Arguments:

• --input - Input data file
• --output - Output report file
• --group-by - Group data by field
• --metrics - Metrics to calculate (comma-separated)
• --format - Output format (html, json)

E-commerce Analytics

Taobao/Douyin Sales Analysis

# Daily sales report
python scripts/analyze_sales.py \
  --input taobao_orders.csv \
  --output daily_report.html \
  --group-by product \
  --metrics revenue,quantity,profit

# Monthly trend analysis
python scripts/generate_charts.py \
  --input monthly_sales.json \
  --charts line \
  --x-axis month \
  --y-axis revenue

Inventory Analysis

python scripts/inventory_analysis.py \
  --input stock_levels.csv \
  --output inventory_report.xlsx \
  --alert-low-stock 10

Customer Analytics

python scripts/customer_analysis.py \
  --input customers.csv \
  --output customer_segments.html \
  --segment-by purchase_frequency

Output Formats

HTML Report

Interactive report with charts and tables.

Excel Workbook

Multiple sheets with raw data, analysis, and charts.

CSV Export

Clean data for further processing.

Templates

Daily Sales Report

• Total revenue
• Order count
• Top products
• Hourly breakdown

Weekly Summary

• Week-over-week comparison
• Trend analysis
• Top categories
• Customer insights

Monthly Executive Report

• KPI dashboard
• Revenue breakdown
• Growth metrics
• Recommendations

Best Practices

1. Clean data first - Remove duplicates, handle missing values
2. Validate inputs - Check data types and ranges
3. Use appropriate charts - Match chart type to data
4. Label clearly - Add titles, axis labels, legends
5. Export in multiple formats - HTML for viewing, CSV for further analysis

Troubleshooting

• Import errors: Install required packages (pandas, matplotlib)
• Memory issues: Process large files in chunks
• Chart rendering: Check output directory permissions
• Date parsing: Ensure consistent date formats