CSV Data Analysis Skill

CSV data analysis produces statistical summaries and quality insights using Python scripts with pandas and numpy. Run `python scripts/analyze_csv.py data.csv` to generate JSON output containing column metrics, validation results, and pattern detection across your tabular data.

Yes, this Skill validates CSV data to identify quality issues, missing values, and structural problems. The validation workflow checks full-file integrity and column-specific metrics, outputting results as JSON with comprehensive error handling and exit codes for automation.

Use the plotting component to visualize CSV data; run `python scripts/plot_data.py data.csv --output plot.png` to generate matplotlib-based charts. This read-only workflow converts tabular analysis into visual outputs without modifying source files.

The Skill handles full-file analyses with pandas and numpy, supporting column-specific metrics and pattern detection across tabular data. Performance depends on file size; the modular design separates analysis, validation, and plotting for flexible scaling.

The Skill uses standardized JSON output and CLI-based argparse interface, making it easy to chain with other tools. Modular components—analyze_csv.py, validate.py, and plot_data.py—allow you to embed analysis steps into automated data pipelines without heavy logic in your main context.

Basic Python and command-line familiarity help, but the Skill provides simple CLI entry points with clear argparse options and JSON output. No embedded context logic is required; the read-only workflow handles errors gracefully with informative exit codes.