---

name: analyze-data description: Analyze a dataset with parallel EDA agents and generate insights. Creates distribution analysis, missing data reports, correlation analysis, outlier detection, and visualizations.

Analyze Data

Perform comprehensive data analysis using parallel specialist agents. Generates insights, visualizations, and recommendations.

When This Skill Applies

• User provides a dataset path (CSV, Parquet, JSON)
• User asks to analyze or explore data
• User wants to understand data quality or distributions

Data Path

The user should provide a path to the data file. If not provided:

1. Look for data files: find . -name "*.csv" -o -name "*.parquet" -o -name "*.json" | head -10
2. Ask user: "Which dataset would you like to analyze?"

Workflow

Step 1: Initial Data Load and Profile

Load the data and generate a quick profile:

import pandas as pd
import numpy as np

# Load data (detect format)
df = pd.read_csv('[data_path]')  # or read_parquet, read_json

# Quick profile
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")
print(f"Data types:\n{df.dtypes}")
print(f"Missing values:\n{df.isnull().sum()}")
print(f"Sample:\n{df.head()}")

Step 2: Ask Analysis Questions

Use AskUserQuestion to clarify:

1. Analysis goal: "What question are you trying to answer with this data?"

   • Exploratory analysis (understand the data)
   • Predictive modeling (predict a target)
   • Statistical testing (compare groups)
   • Time series analysis (forecast trends)

2. Target variable (if applicable): "Which column is the target/outcome you want to predict or analyze?"

3. Key dimensions: "Which columns represent important groups or segments to analyze?"

Step 3: Launch Parallel Analysis Agents

CRITICAL: Launch ALL agents in a SINGLE message.

Task (model: haiku, subagent_type: general-purpose): "DISTRIBUTION ANALYSIS

Analyze distributions for this dataset:
- Load: [data_path]
- For each numeric column: compute mean, median, std, skewness, kurtosis
- Check normality (Shapiro-Wilk for n<5000, else D'Agostino)
- Identify heavily skewed columns (|skew| > 1)
- For each categorical column: value counts, cardinality

Output:
- Table of distribution stats
- List of columns needing transformation
- Anomalies found"

Task (model: haiku, subagent_type: general-purpose): "MISSING DATA ANALYSIS

Analyze missing data patterns:
- Load: [data_path]
- Missing count and percentage per column
- Missing data patterns (MCAR, MAR, MNAR indicators)
- Correlations between missingness
- Columns with >50% missing (candidates for dropping)

Output:
- Missing data summary table
- Pattern analysis
- Imputation recommendations"

Task (model: haiku, subagent_type: general-purpose): "CORRELATION ANALYSIS

Analyze relationships:
- Load: [data_path]
- Pearson correlations for numeric columns
- High correlations (|r| > 0.7) - multicollinearity risks
- Target correlations if target specified: [target]
- Cramér's V for categorical associations

Output:
- Top 10 correlations
- Multicollinearity warnings
- Feature importance ranking (if target)"

Task (model: haiku, subagent_type: general-purpose): "OUTLIER ANALYSIS

Detect outliers:
- Load: [data_path]
- IQR method for each numeric column
- Z-score method (|z| > 3)
- Isolation Forest for multivariate outliers
- Business logic outliers (negative prices, future dates, etc.)

Output:
- Outlier counts per column
- Most extreme values
- Recommended handling"

Task (model: sonnet, subagent_type: general-purpose): "VISUALIZATION GENERATION

Create key visualizations:
- Load: [data_path]
- Distribution plots for top numeric columns
- Correlation heatmap
- Target distribution (if applicable)
- Time trends (if datetime columns exist)
- Category breakdowns

Save plots to: ./analysis_output/
Use: matplotlib, seaborn
Output: List of generated plot files"

Step 4: If Predictive Modeling Requested

Launch additional modeling agents:

Task (model: sonnet, subagent_type: general-purpose): "BASELINE MODELING

Build baseline models:
- Load: [data_path]
- Target: [target]
- Train/test split (80/20, stratified if classification)
- Baseline: DummyClassifier/DummyRegressor
- Simple model: LogisticRegression or LinearRegression
- Tree model: RandomForestClassifier/Regressor

Report:
- Baseline performance
- Simple model performance
- Feature importances from tree model
- Recommended next steps"

Task (model: haiku, subagent_type: general-purpose): "FEATURE ENGINEERING SUGGESTIONS

Based on data profile, suggest features:
- Log transforms for skewed numerics
- Binning strategies
- Interaction terms
- Date feature extraction
- Encoding strategies for categoricals
- Aggregation features if hierarchical data

Output: Prioritized list of feature engineering ideas"

Step 5: Synthesize Results

Collect all agent outputs and create unified report:

# Data Analysis Report: [Dataset Name]

**Generated:** [Date] **Dataset:** [Path] **Shape:** [Rows] x [Columns]

## Executive Summary

[2-3 key findings with metrics]

## Data Quality

### Missing Data

[From missing data agent]

### Outliers

[From outlier agent]

### Data Types

[Column type summary]

## Key Distributions

[Distribution insights + plots]

## Relationships

### Correlations

[Top correlations, multicollinearity warnings]

### Target Analysis (if applicable)

[Target distribution, key predictors]

## Visualizations

[Links/embeds to generated plots]

## Recommendations

### Data Cleaning

1. [Specific action]
2. [Specific action]

### Feature Engineering

1. [Specific suggestion]
2. [Specific suggestion]

### Modeling (if applicable)

- Baseline performance: [metric]
- Recommended approach: [algorithm]
- Key features: [list]

## Next Steps

1. [Action item]
2. [Action item]

Step 6: Save Outputs

mkdir -p analysis_output

Save:

• analysis_output/report.md - Full analysis report
• analysis_output/data_profile.json - Structured data profile
• analysis_output/*.png - Visualizations
• analysis_output/notebook.ipynb - Reproducible notebook (optional)

Output

Provide the user with:

1. Executive summary (3-5 bullet points)
2. Path to full report
3. Key visualizations inline
4. Recommended next steps

Tips

• For large files (>100MB), use polars or duckdb instead of pandas
• For notebooks, use NotebookEdit to create reproducible analysis
• Reference the data-science skill for methodology details