DollhouseMCP

--- name: "Data Analysis" description: "Statistical analysis, visualization, and insights extraction from datasets" type: "skill" version: "1.0.0" author: "DollhouseMCP" created: "2025-07-23" category: "analytics" tags: ["data", "statistics", "visualization", "insights", "analytics"] proficiency_levels: beginner: "Basic statistics and simple charts" intermediate: "Correlation analysis and trend detection" advanced: "Predictive modeling and complex visualizations" parameters: analysis_type: type: "array" description: "Types of analysis to perform" default: ["descriptive", "diagnostic"] enum: ["descriptive", "diagnostic", "predictive", "prescriptive"] visualization_format: type: "string" description: "Preferred visualization format" default: "auto" enum: ["auto", "charts", "tables", "narrative", "dashboard"] confidence_level: type: "number" description: "Statistical confidence level" default: 0.95 min: 0.90 max: 0.99 handle_missing_data: type: "string" description: "How to handle missing values" default: "interpolate" enum: ["ignore", "interpolate", "drop", "flag"] _dollhouseMCPTest: true _testMetadata: suite: "bundled-test-data" purpose: "General test data for DollhouseMCP system validation" created: "2025-08-20" version: "1.0.0" migrated: "2025-08-20T23:47:24.346Z" originalPath: "data/skills/data-analysis.md" --- # Data Analysis Skill This skill provides comprehensive data analysis capabilities for extracting insights, identifying patterns, and making data-driven recommendations. ## Core Capabilities ### 1. Descriptive Analysis - **Central Tendency**: Mean, median, mode - **Dispersion**: Standard deviation, variance, range - **Distribution**: Skewness, kurtosis, percentiles - **Frequency**: Histograms, frequency tables ### 2. Diagnostic Analysis - **Correlation Analysis**: Pearson, Spearman, Kendall - **Regression**: Linear, logistic, polynomial - **Time Series**: Trends, seasonality, decomposition - **Anomaly Detection**: Outliers, unusual patterns ### 3. Predictive Analysis - **Forecasting**: Time series prediction - **Classification**: Category prediction - **Clustering**: Group identification - **Probability**: Risk assessment ### 4. Visualization - **Charts**: Line, bar, scatter, pie, heatmap - **Distributions**: Histograms, box plots, violin plots - **Relationships**: Scatter plots, correlation matrices - **Comparisons**: Grouped bars, stacked charts ## Analysis Process ### Step 1: Data Profiling ``` Dataset Overview: - Rows: 10,432 - Columns: 15 - Missing values: 2.3% - Data types: 5 numeric, 8 categorical, 2 datetime ``` ### Step 2: Quality Assessment - Completeness check - Consistency validation - Outlier identification - Data type verification ### Step 3: Analysis Execution - Apply statistical methods - Generate visualizations - Extract key findings - Identify patterns ### Step 4: Insight Generation - Summarize findings - Highlight anomalies - Provide recommendations - Suggest next steps ## Output Formats ### 1. Executive Summary ``` Key Findings: • Sales increased 23% year-over-year • Customer retention improved by 15% • Regional performance varies significantly • Seasonal patterns strongly influence demand ``` ### 2. Detailed Report ``` Statistical Analysis Results: Correlation Matrix: Sales Marketing Satisfaction Sales 1.00 0.82 0.65 Marketing 0.82 1.00 0.54 Satisfaction 0.65 0.54 1.00 Regression Analysis: Sales = 1,234 + 2.5×Marketing + 156×Satisfaction R² = 0.78, p < 0.001 ``` ### 3. Visual Dashboard ``` [Chart: Monthly Sales Trend] 📊 ──────────────────── │ ╱╲ ╱╲ │ ╱╲ ╲ ╱ ╲ │ ╱╲ ╲╱ ╲ └───────────────── J F M A M J J A S ``` ## Special Features ### 1. Natural Language Insights Converts statistical findings into plain English: - "Sales peak in December (43% above average)" - "Customer age strongly correlates with purchase frequency (r=0.72)" - "Northern region underperforms by 18% compared to others" ### 2. Automated Recommendations Based on analysis results: - "Consider increasing marketing spend in Q3" - "Focus on customer retention in 25-34 age group" - "Investigate northern region performance issues" ### 3. Interactive Analysis - Drill-down capabilities - What-if scenarios - Sensitivity analysis - Custom segmentation ## Integration Notes Works well with: - Business Consultant persona for strategic insights - Technical Analyst for deep-dive investigations - Report templates for standardized output - Dashboard agents for real-time monitoring