allowed-tools: Task, Read, Write, Bash, Grep, Glob

argument-hint: [dataset] [analysis_type]

description: Perform comprehensive data analysis on specified dataset

Data Analysis Command

Execute data analysis on dataset $1 with analysis type $2 using the data-explorer subagent.

Context

• Dataset location: @data_storage/$1
• Analysis type: $2 (exploratory, statistical, predictive, complete)
• Current working directory: !pwd
• Available visualization libraries: matplotlib, seaborn, plotly
• Python data science stack: pandas, numpy, scipy

Your Task

Use the data-explorer subagent to perform comprehensive data analysis:

1. Data Assessment

• Load and inspect the dataset structure
• Check data types, missing values, and duplicates
• Generate initial summary statistics
• Identify data quality issues

2. Statistical Analysis

• Perform descriptive statistics analysis
• Calculate correlations between variables
• Identify outliers and anomalies
• Conduct appropriate statistical tests

3. Pattern Discovery

• Identify trends and patterns in the data
• Discover relationships between variables
• Detect seasonal patterns or cycles
• Find clusters or segments in the data

4. Generate Insights

• Extract key findings from the analysis
• Identify actionable insights
• Suggest areas for deeper investigation
• Recommend visualization approaches

Analysis Types

Exploratory Analysis

• Basic data understanding
• Summary statistics
• Data quality assessment
• Initial pattern identification

Statistical Analysis

• Advanced statistical testing
• Correlation and regression analysis
• Hypothesis testing
• Confidence intervals

Predictive Analysis

• Feature importance analysis
• Predictive modeling preparation
• Variable relationships
• Model recommendation

Complete Analysis

• All of the above plus
• Comprehensive report generation
• Visualization recommendations
• Next steps planning

Expected Output

Analysis Report

Create a comprehensive analysis report with:

• Executive Summary: Key findings in plain language
• Data Overview: Dataset characteristics and quality
• Statistical Findings: Detailed statistical analysis
• Key Insights: Actionable discoveries
• Recommendations: Next steps for deeper analysis
• Limitations: Data and method constraints

File Outputs

• analysis_reports/analysis_summary_$1.md - Detailed analysis report
• analysis_reports/statistical_summary_$1.csv - Statistical summary table
• analysis_reports/data_quality_$1.json - Data quality assessment

Quality Assurance

• Validate all statistical calculations
• Cross-check important findings
• Document all assumptions and limitations
• Ensure reproducible analysis

Example Usage

/analyze user_behavior.csv exploratory
/analyze sales_data.csv statistical
/analyze customer_data.csv predictive
/analyze financial_data.csv complete

Notes

• Dataset should be located in the data_storage/ directory
• Analysis results will be saved to analysis_reports/ directory
• Use Task tool to delegate to data-explorer subagent
• Consider following up with /visualize command for charts