Data Analysis & Reporting with OpenClaw

mkdir -p ~/clawd/data
# Copy your CSV/Excel files here
cp ~/Downloads/sales_data.csv ~/clawd/data/

You: "Analyze the CSV file at ~/clawd/data/sales_data.csv and give me a summary"

OpenClaw: [Reads CSV, analyzes data, provides summary]
"Analyzed sales_data.csv:
- Total records: 1,234
- Date range: Jan 2024 - Dec 2024
- Total revenue: $456,789
- Top product: Widget A ($123,456)
- Average order value: $370.23"

You: "Find trends in the sales data. What are the best performing months?"

You: "Calculate the correlation between product price and sales volume"

You: "Identify any anomalies or outliers in the data"

You: "Group sales by category and calculate totals"

You: "Analyze the Excel file at ~/clawd/data/financial_report.xlsx. 
      Summarize the key financial metrics from all sheets."

OpenClaw: [Reads Excel, processes all sheets, provides comprehensive summary]

You: "Compare data across all sheets in the Excel file. 
      Identify any inconsistencies or discrepancies."

You: "Create a summary report combining data from Sheet1 and Sheet2"

You: "Generate a detailed report analyzing the sales data. 
      Include trends, top performers, and recommendations."

OpenClaw: [Analyzes data, generates comprehensive report, saves to file]

You: "Create a Markdown report with the analysis results. 
      Save it to ~/clawd/data/analysis_report.md"

OpenClaw: [Creates formatted Markdown report with tables, charts descriptions]

You: "Create a skill that automatically analyzes all CSV files 
      in ~/clawd/data/ every Monday and generates a weekly report"

OpenClaw: [Creates skill with automation logic]

# Add to crontab for weekly analysis
0 9 * * 1 /path/to/openclaw analyze-weekly-data

# Or use OpenClaw's automation features
You: "Set up a weekly task to analyze new data files and email me the report"

You: "Generate Python code to create a bar chart of sales by month 
      from the CSV data. Save it as visualize_sales.py"

OpenClaw: [Creates Python script with matplotlib/plotly code]

You: "What type of chart would best visualize the sales trends?"

You: "Recommend visualizations for comparing product performance"

You: "Suggest the best way to show customer distribution by region"

You: "Clean the CSV file: remove duplicates, fix date formats, 
      and handle missing values. Save the cleaned version."

You: "Standardize column names and data types across multiple CSV files"

You: "Merge sales_data.csv and customer_data.csv on the customer_id column. 
      Save the merged result."

You: "Combine data from multiple Excel sheets into a single CSV file"

You: "Transform the data: calculate profit margins, add percentage columns, 
      and create summary rows"

You: "Pivot the data to show sales by product and month"

You: "Analyze this month's sales data:
      1. Calculate total revenue
      2. Identify top 10 products
      3. Compare to last month
      4. Generate insights and recommendations
      5. Save report to sales_analysis.md"

You: "Process the quarterly financial report:
      - Extract key metrics from all sheets
      - Calculate growth rates
      - Identify areas of concern
      - Create executive summary
      - Format as presentation-ready report"

You: "Analyze customer data:
      - Segment customers by purchase behavior
      - Calculate customer lifetime value
      - Identify churn risk factors
      - Recommend retention strategies"