First Look

How It Works

Every new dataset starts the same way: df.head(), df.describe(), df.info(),

check for nulls, check for duplicates, look at value distributions...

This skill does all of it in one pass and gives you a structured report.

What You Get

• Column-level profiling: type, null rate, unique count, min/max/mean/median
• Distribution summaries and histogram sketches for numeric columns
• Top values and frequency counts for categorical columns
• Duplicate row detection (exact and fuzzy)
• Outlier flagging (values beyond 3 standard deviations or IQR-based)
• Cross-column correlation matrix for numeric columns
• Data quality score per column and overall
• Suggested cleaning steps based on issues found

Setup Steps

1. Ask your Claw to create a "First Look" skill with the prompt below
2. Drop any data file or point it at a database table
3. Get back a comprehensive profile report in under a minute

Tips

• Run this on every new data source before doing any analysis
• The quality score helps prioritize which columns need cleaning attention
• The suggested cleaning steps feed directly into the CSV Doctor or Date Whisperer skills
• For large datasets, it profiles a sample and notes where sampling might miss issues