aws-samples/data-analysis

Data Analysis Skill

Analyze structured data files (CSV, JSON) and produce clear, actionable reports.

When to Use

• User asks to "summarize", "analyze", or "describe" a data file
• User wants statistics: row counts, column types, means, medians, distributions
• User needs to find anomalies or outliers in numeric data
• User requests a formatted report or table from raw data

When NOT to Use

• Unstructured text files (use a text-processing skill instead)
• Database queries (use a database skill)
• Image or binary file analysis
• Real-time or streaming data

Workflow

1. Identify the data file — look for CSV or JSON files in the workspace
2. Run the analysis script for deterministic statistics:

   python3 scripts/analyze_csv.py <file.csv>
   

3. Interpret the results — add context about what the numbers mean
4. Format the output — use markdown tables for readability

Output Format

Always structure your response as:

1. Overview — file name, row count, column count
2. Column Summary — for each column: type, non-null count, unique values
3. Statistics — for numeric columns: min, max, mean, median, std dev
4. Anomalies — any outliers (values beyond 2 standard deviations)
5. Recommendations — actionable insights based on the data

Important Notes

• Always report exact numbers, not approximations
• If a CSV has headers, use them as column names
• For large files (>10k rows), report a sample and note the total
• Never modify the source data file