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name: analyzing-data description: Performs comprehensive data analysis including statistical analysis, visualization, pattern detection, and report generation. Use when the user asks to analyze data, find patterns, generate insights, create visualizations, or mentions data analysis, statistics, or data science tasks.

Data Analyzer

This skill performs comprehensive data analysis with statistical methods, visualizations, and automated reporting.

When to Use This Skill

Invoke this skill when the user:

• Asks to analyze a dataset
• Wants statistical insights
• Needs data visualization
• Requests pattern detection
• Mentions data analysis, statistics, or data science
• Wants to generate analysis reports

Analysis Workflow

Step 1: Data Understanding

Initial Assessment:

1. Identify data format (CSV, JSON, Excel, etc.)
2. Determine data size and structure
3. Understand business context
4. Clarify analysis objectives

Use the analysis script:

python scripts/analyze.py data.csv --explore

Step 2: Data Quality Check

Validation:

• Data loads successfully
• Required columns present
• Data types appropriate
• Missing values identified
• Outliers detected
• Duplicates checked

Quality Report:

python scripts/analyze.py data.csv --quality-report

Step 3: Statistical Analysis

Perform analysis based on data type and objectives.

For Descriptive Statistics:

• Mean, median, mode
• Standard deviation, variance
• Quartiles and ranges
• Distribution shape

For Correlation Analysis:

• Pearson correlation
• Spearman rank correlation
• Covariance matrix

For Advanced Analysis: See REFERENCE.md for:

• Hypothesis testing procedures
• Regression analysis methods
• Time series analysis
• Clustering algorithms

Step 4: Visualization

Create appropriate visualizations:

Univariate Analysis:

• Histograms for distributions
• Box plots for outliers
• Bar charts for categories

Bivariate Analysis:

• Scatter plots for relationships
• Line charts for trends
• Heatmaps for correlations

Multivariate Analysis:

• Pair plots
• 3D visualizations
• Dimensionality reduction plots

Generate visualizations:

python scripts/analyze.py data.csv --visualize --output-dir ./charts

Step 5: Report Generation

Create analysis report using templates from FORMS.md:

cat FORMS.md  # View available report templates
python scripts/analyze.py data.csv --report executive-summary

Analysis Types

Pattern 1: Exploratory Data Analysis (EDA)

Objective: Understand data characteristics and relationships

Steps:

1. Load and preview data
2. Generate summary statistics
3. Check distributions
4. Identify correlations
5. Detect outliers
6. Document insights

Quick EDA:

python scripts/analyze.py data.csv --eda

Pattern 2: Comparative Analysis

Objective: Compare groups or time periods

Steps:

1. Define groups/periods
2. Calculate group statistics
3. Test for significant differences
4. Visualize comparisons
5. Interpret results

See REFERENCE.md section "Statistical Testing" for test selection.

Pattern 3: Trend Analysis

Objective: Identify patterns over time

Steps:

1. Prepare time series data
2. Check for seasonality
3. Calculate moving averages
4. Fit trend lines
5. Forecast future values

See REFERENCE.md section "Time Series Methods" for details.

Pattern 4: Predictive Modeling

Objective: Build models to predict outcomes

Steps:

1. Feature engineering
2. Train/test split
3. Model selection
4. Training and validation
5. Performance evaluation

See REFERENCE.md section "Machine Learning" for model details.

Data Type Handling

Numerical Data:

• Summary statistics
• Distribution analysis
• Correlation analysis
• Regression modeling

Categorical Data:

• Frequency tables
• Cross-tabulations
• Chi-square tests
• Category encoding

Time Series Data:

• Trend decomposition
• Seasonality detection
• Autocorrelation
• Forecasting

Text Data:

• Frequency analysis
• Sentiment analysis
• Topic modeling
• See REFERENCE.md section "Text Analytics"

Common Issues and Solutions

Issue: Missing Values

• Strategy 1: Remove rows (if <5% missing)
• Strategy 2: Impute with mean/median/mode
• Strategy 3: Use advanced imputation (KNN, MICE)
• See REFERENCE.md section "Missing Data Handling"

Issue: Outliers

• Detection: IQR method, Z-score, isolation forest
• Action: Remove, cap, or transform
• Context: Business rules may define valid outliers

Issue: Imbalanced Data

• Resampling techniques
• Class weights
• Synthetic data generation (SMOTE)

Issue: High Dimensionality

• Feature selection
• PCA or t-SNE
• Domain knowledge filtering

Output Formats

The skill can generate reports in multiple formats:

Executive Summary:

• Key findings (3-5 bullets)
• Critical metrics
• Recommendations
• See FORMS.md template "Executive Summary"

Technical Report:

• Methodology
• Detailed results
• Statistical tests
• Visualizations
• See FORMS.md template "Technical Report"

Dashboard Format:

• Interactive visualizations
• Key metrics at a glance
• Drill-down capability

Generate specific format:

python scripts/analyze.py data.csv --format executive
python scripts/analyze.py data.csv --format technical
python scripts/analyze.py data.csv --format dashboard

Validation Checklist

Before finalizing analysis:

• Data quality verified
• Appropriate methods selected
• Assumptions validated
• Results interpreted correctly
• Visualizations clear and labeled
• Report matches requested format
• Recommendations actionable

Analysis Scope

Quick Analysis (5-10 min):

• Basic statistics
• Simple visualizations
• Key findings only

Standard Analysis (20-40 min):

• Comprehensive statistics
• Multiple visualizations
• Correlation analysis
• Formatted report

Deep Analysis (1-2 hours):

• Advanced modeling
• Hypothesis testing
• Multiple methodologies
• Executive + technical reports

Ask user for preferred scope if unclear.

Example Analysis

Input: sales_data.csv with columns: date, product, region, quantity, revenue

Output:

Key Findings

1. Revenue increased 23% year-over-year
2. Product A accounts for 45% of total revenue
3. Western region shows strongest growth (31%)
4. Seasonal peak in Q4 (38% of annual sales)

Statistical Summary

• Mean daily revenue: $12,450
• Median daily revenue: $11,200
• Standard deviation: $3,890
• 95% of days: $5,000 - $20,000

Visualizations Generated

• Revenue trend line (2023-2024)
• Product revenue pie chart
• Regional comparison bar chart
• Seasonal pattern heatmap

Recommendations

1. Increase inventory for Product A in Q4
2. Investigate Western region success factors
3. Plan marketing campaigns for Q2-Q3 (slower periods)

Advanced Features

For complex scenarios, this skill integrates with:

REFERENCE.md sections:

• Statistical Methods Library
• Machine Learning Algorithms
• Time Series Techniques
• Text Analytics Methods

FORMS.md templates:

• Executive Summary Template
• Technical Report Template
• Dashboard Layout Template

Scripts:

• scripts/analyze.py - Main analysis engine
• scripts/visualize.py - Visualization generator
• scripts/report.py - Report formatter

Getting Started

Simple analysis:

python scripts/analyze.py your_data.csv

With options:

python scripts/analyze.py your_data.csv \
  --explore \
  --visualize \
  --report executive \
  --output-dir ./results

Help:

python scripts/analyze.py --help

For detailed methodology and advanced techniques, see REFERENCE.md. For report templates and output examples, see FORMS.md.